├── .DS_Store ├── LICENSE ├── README.md ├── datasets └── pandas_01_day6.xlsx ├── notebooks ├── 01_python_basics.md └── 02_data_structures_and_functions.md └── resources ├── .DS_Store ├── Day1.png ├── Day10.png ├── Day11a.png ├── Day11b.png ├── Day12.png ├── Day13.png ├── Day15.png ├── Day16.png ├── Day17.png ├── Day18.png ├── Day19.png ├── Day2.png ├── Day20.png ├── Day21.png ├── Day22.png ├── Day24.png ├── Day26.png ├── Day27.png ├── Day28.jpeg ├── Day29.jpeg ├── Day3.png ├── Day30.jpeg ├── Day31.jpeg ├── Day32.jpeg ├── Day33.png ├── Day34.jpeg ├── Day35.jpeg ├── Day36a.jpeg ├── Day36b.jpeg ├── Day37a.jpeg ├── Day37b.jpeg ├── Day37c.jpeg ├── Day37d.jpeg ├── Day37e.jpeg ├── Day37f.jpeg ├── Day37g.jpeg ├── Day37h.jpeg ├── Day37i.jpeg ├── Day37j.jpeg ├── Day37k.jpeg ├── Day37l.jpeg ├── Day37m.jpeg ├── Day37n.jpeg ├── Day38.jpeg ├── Day38a.gif ├── Day39.jpeg ├── Day3_live.png ├── Day4.png ├── Day40.jpeg ├── Day5.png ├── Day5a.png ├── Day6.png ├── Day7.png ├── Day8.png ├── Day9.png ├── PKC2023.png ├── aammar.jpeg ├── chatGPT.png ├── cheat_sheets ├── Day10.png ├── Pandas_Cheat_Sheet_datWrangling.pdf ├── matplotlib_all_cheatsheets.pdf ├── matplotlib_cheatsheet_2.webp ├── matplotlib_handout-beginner.pdf ├── matplotlib_handout-intermediate.pdf ├── matplotlib_handout-tips.pdf └── pandas_cheatsheet.pdf ├── feedback.png ├── first_line.png ├── installation.png ├── ml_day22_23 ├── .DS_Store ├── 26.png ├── 27.png ├── 28.png ├── 29.png ├── 30.png ├── 32.png ├── 33.png ├── 34.png ├── 35.png ├── 36.png ├── 37.png ├── 38.png ├── 39.png ├── 40.png ├── 41.png ├── 42.png └── 43.png ├── poster_2.png ├── snipping1.png ├── snipping2.png └── snipping3.png /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/.DS_Store -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. Definitions. 8 | 9 | "License" shall mean the terms and conditions for use, reproduction, 10 | and distribution as defined by Sections 1 through 9 of this document. 11 | 12 | "Licensor" shall mean the copyright owner or entity authorized by 13 | the copyright owner that is granting the License. 14 | 15 | "Legal Entity" shall mean the union of the acting entity and all 16 | other entities that control, are controlled by, or are under common 17 | control with that entity. For the purposes of this definition, 18 | "control" means (i) the power, direct or indirect, to cause the 19 | direction or management of such entity, whether by contract or 20 | otherwise, or (ii) ownership of fifty percent (50%) or more of the 21 | outstanding shares, or (iii) beneficial ownership of such entity. 22 | 23 | "You" (or "Your") shall mean an individual or Legal Entity 24 | exercising permissions granted by this License. 25 | 26 | "Source" form shall mean the preferred form for making modifications, 27 | including but not limited to software source code, documentation 28 | source, and configuration files. 29 | 30 | "Object" form shall mean any form resulting from mechanical 31 | transformation or translation of a Source form, including but 32 | not limited to compiled object code, generated documentation, 33 | and conversions to other media types. 34 | 35 | "Work" shall mean the work of authorship, whether in Source or 36 | Object form, made available under the License, as indicated by a 37 | copyright notice that is included in or attached to the work 38 | (an example is provided in the Appendix below). 39 | 40 | "Derivative Works" shall mean any work, whether in Source or Object 41 | form, that is based on (or derived from) the Work and for which the 42 | editorial revisions, annotations, elaborations, or other modifications 43 | represent, as a whole, an original work of authorship. For the purposes 44 | of this License, Derivative Works shall not include works that remain 45 | separable from, or merely link (or bind by name) to the interfaces of, 46 | the Work and Derivative Works thereof. 47 | 48 | "Contribution" shall mean any work of authorship, including 49 | the original version of the Work and any modifications or additions 50 | to that Work or Derivative Works thereof, that is intentionally 51 | submitted to Licensor for inclusion in the Work by the copyright owner 52 | or by an individual or Legal Entity authorized to submit on behalf of 53 | the copyright owner. For the purposes of this definition, "submitted" 54 | means any form of electronic, verbal, or written communication sent 55 | to the Licensor or its representatives, including but not limited to 56 | communication on electronic mailing lists, source code control systems, 57 | and issue tracking systems that are managed by, or on behalf of, the 58 | Licensor for the purpose of discussing and improving the Work, but 59 | excluding communication that is conspicuously marked or otherwise 60 | designated in writing by the copyright owner as "Not a Contribution." 61 | 62 | "Contributor" shall mean Licensor and any individual or Legal Entity 63 | on behalf of whom a Contribution has been received by Licensor and 64 | subsequently incorporated within the Work. 65 | 66 | 2. Grant of Copyright License. Subject to the terms and conditions of 67 | this License, each Contributor hereby grants to You a perpetual, 68 | worldwide, non-exclusive, no-charge, royalty-free, irrevocable 69 | copyright license to reproduce, prepare Derivative Works of, 70 | publicly display, publicly perform, sublicense, and distribute the 71 | Work and such Derivative Works in Source or Object form. 72 | 73 | 3. Grant of Patent License. Subject to the terms and conditions of 74 | this License, each Contributor hereby grants to You a perpetual, 75 | worldwide, non-exclusive, no-charge, royalty-free, irrevocable 76 | (except as stated in this section) patent license to make, have made, 77 | use, offer to sell, sell, import, and otherwise transfer the Work, 78 | where such license applies only to those patent claims licensable 79 | by such Contributor that are necessarily infringed by their 80 | Contribution(s) alone or by combination of their Contribution(s) 81 | with the Work to which such Contribution(s) was submitted. If You 82 | institute patent litigation against any entity (including a 83 | cross-claim or counterclaim in a lawsuit) alleging that the Work 84 | or a Contribution incorporated within the Work constitutes direct 85 | or contributory patent infringement, then any patent licenses 86 | granted to You under this License for that Work shall terminate 87 | as of the date such litigation is filed. 88 | 89 | 4. Redistribution. You may reproduce and distribute copies of the 90 | Work or Derivative Works thereof in any medium, with or without 91 | modifications, and in Source or Object form, provided that You 92 | meet the following conditions: 93 | 94 | (a) You must give any other recipients of the Work or 95 | Derivative Works a copy of this License; and 96 | 97 | (b) You must cause any modified files to carry prominent notices 98 | stating that You changed the files; and 99 | 100 | (c) You must retain, in the Source form of any Derivative Works 101 | that You distribute, all copyright, patent, trademark, and 102 | attribution notices from the Source form of the Work, 103 | excluding those notices that do not pertain to any part of 104 | the Derivative Works; and 105 | 106 | (d) If the Work includes a "NOTICE" text file as part of its 107 | distribution, then any Derivative Works that You distribute must 108 | include a readable copy of the attribution notices contained 109 | within such NOTICE file, excluding those notices that do not 110 | pertain to any part of the Derivative Works, in at least one 111 | of the following places: within a NOTICE text file distributed 112 | as part of the Derivative Works; within the Source form or 113 | documentation, if provided along with the Derivative Works; or, 114 | within a display generated by the Derivative Works, if and 115 | wherever such third-party notices normally appear. The contents 116 | of the NOTICE file are for informational purposes only and 117 | do not modify the License. You may add Your own attribution 118 | notices within Derivative Works that You distribute, alongside 119 | or as an addendum to the NOTICE text from the Work, provided 120 | that such additional attribution notices cannot be construed 121 | as modifying the License. 122 | 123 | You may add Your own copyright statement to Your modifications and 124 | may provide additional or different license terms and conditions 125 | for use, reproduction, or distribution of Your modifications, or 126 | for any such Derivative Works as a whole, provided Your use, 127 | reproduction, and distribution of the Work otherwise complies with 128 | the conditions stated in this License. 129 | 130 | 5. Submission of Contributions. Unless You explicitly state otherwise, 131 | any Contribution intentionally submitted for inclusion in the Work 132 | by You to the Licensor shall be under the terms and conditions of 133 | this License, without any additional terms or conditions. 134 | Notwithstanding the above, nothing herein shall supersede or modify 135 | the terms of any separate license agreement you may have executed 136 | with Licensor regarding such Contributions. 137 | 138 | 6. Trademarks. This License does not grant permission to use the trade 139 | names, trademarks, service marks, or product names of the Licensor, 140 | except as required for reasonable and customary use in describing the 141 | origin of the Work and reproducing the content of the NOTICE file. 142 | 143 | 7. Disclaimer of Warranty. Unless required by applicable law or 144 | agreed to in writing, Licensor provides the Work (and each 145 | Contributor provides its Contributions) on an "AS IS" BASIS, 146 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or 147 | implied, including, without limitation, any warranties or conditions 148 | of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A 149 | PARTICULAR PURPOSE. You are solely responsible for determining the 150 | appropriateness of using or redistributing the Work and assume any 151 | risks associated with Your exercise of permissions under this License. 152 | 153 | 8. Limitation of Liability. In no event and under no legal theory, 154 | whether in tort (including negligence), contract, or otherwise, 155 | unless required by applicable law (such as deliberate and grossly 156 | negligent acts) or agreed to in writing, shall any Contributor be 157 | liable to You for damages, including any direct, indirect, special, 158 | incidental, or consequential damages of any character arising as a 159 | result of this License or out of the use or inability to use the 160 | Work (including but not limited to damages for loss of goodwill, 161 | work stoppage, computer failure or malfunction, or any and all 162 | other commercial damages or losses), even if such Contributor 163 | has been advised of the possibility of such damages. 164 | 165 | 9. Accepting Warranty or Additional Liability. While redistributing 166 | the Work or Derivative Works thereof, You may choose to offer, 167 | and charge a fee for, acceptance of support, warranty, indemnity, 168 | or other liability obligations and/or rights consistent with this 169 | License. However, in accepting such obligations, You may act only 170 | on Your own behalf and on Your sole responsibility, not on behalf 171 | of any other Contributor, and only if You agree to indemnify, 172 | defend, and hold each Contributor harmless for any liability 173 | incurred by, or claims asserted against, such Contributor by reason 174 | of your accepting any such warranty or additional liability. 175 | 176 | END OF TERMS AND CONDITIONS 177 | 178 | APPENDIX: How to apply the Apache License to your work. 179 | 180 | To apply the Apache License to your work, attach the following 181 | boilerplate notice, with the fields enclosed by brackets "[]" 182 | replaced with your own identifying information. (Don't include 183 | the brackets!) The text should be enclosed in the appropriate 184 | comment syntax for the file format. We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # **Python ka Chilla 2023** 2 | ## (Data Science with Python in 40-days) 3 | - [**Python ka Chilla 2023**](#python-ka-chilla-2023) 4 | - [(Data Science with Python in 40-days)](#data-science-with-python-in-40-days) 5 | - [**Resources, Books and Blogs Links:**](#resources-books-and-blogs-links) 6 | - [**Day-1**](#day-1) 7 | - [**Day-2**](#day-2) 8 | - [**How to use VScode (an IDE) for Python?**](#how-to-use-vscode-an-ide-for-python) 9 | - [**Day-3**](#day-3) 10 | - [**Basics of Python programming for Data Science (1/2)**](#basics-of-python-programming-for-data-science-12) 11 | - [**Day-4**](#day-4) 12 | - [**Basics of Python programming for Data Science (2/2)**](#basics-of-python-programming-for-data-science-22) 13 | - [**Day-5**](#day-5) 14 | - [**Jupyternotebook Introduction, Libraries and Data Visualization**](#jupyternotebook-introduction-libraries-and-data-visualization) 15 | - [**Markdown Language in 75 Minutes (This video is taken from Python ka chilla 2022**](#markdown-language-in-75-minutes-this-video-is-taken-from-python-ka-chilla-2022) 16 | - [**Day-6**](#day-6) 17 | - [**Pandas\_01**](#pandas_01) 18 | - [**Day-7**](#day-7) 19 | - [**Pandas library basic functions**](#pandas-library-basic-functions) 20 | - [**Day-8**](#day-8) 21 | - [**Pandas tips and tricks**](#pandas-tips-and-tricks) 22 | - [**Day-9**](#day-9) 23 | - [**Pandas practice online**](#pandas-practice-online) 24 | - [**Day-10**](#day-10) 25 | - [**Data Wrangling and Data Visualization (Basics)**](#data-wrangling-and-data-visualization-basics) 26 | - [**Day-11**](#day-11) 27 | - [**Data Visualization**](#data-visualization) 28 | - [**Day-12**](#day-12) 29 | - [**Exploratory Data Analysis (Basics)**](#exploratory-data-analysis-basics) 30 | - [**Day-13**](#day-13) 31 | - [**Exploratory Data Analysis (A-Z)**](#exploratory-data-analysis-a-z) 32 | - [**Day-14**](#day-14) 33 | - [**ABC of Statistics**](#abc-of-statistics) 34 | - [**Day-15**](#day-15) 35 | - [**Python Data Wrangling Techniques: From Beginner to Pro**](#python-data-wrangling-techniques-from-beginner-to-pro) 36 | - [**Day-16**](#day-16) 37 | - [**Machine Learning Basics**](#machine-learning-basics) 38 | - [**Day-17**](#day-17) 39 | - [**Machine Learning Basics-1**](#machine-learning-basics-1) 40 | - [**Day-18**](#day-18) 41 | - [**Regression (Machine Learning Basics-2)**](#regression-machine-learning-basics-2) 42 | - [**Day-19**](#day-19) 43 | - [**Classification (Machine Learning Basics-3)**](#classification-machine-learning-basics-3) 44 | - [**Day-20**](#day-20) 45 | - [**How to select a best Model in Machine learning with Scikit-learn?**](#how-to-select-a-best-model-in-machine-learning-with-scikit-learn) 46 | - [**Day-21**](#day-21) 47 | - [**How to select a best parameters in a Machine learning model with Scikit-learn?**](#how-to-select-a-best-parameters-in-a-machine-learning-model-with-scikit-learn) 48 | - [**Day-22**](#day-22) 49 | - [**Machine learning terminologies and theoratical concepts (Part-1)**](#machine-learning-terminologies-and-theoratical-concepts-part-1) 50 | - [**Day-23**](#day-23) 51 | - [**Machine learning terminologies and theoratical concepts (Part-2)**](#machine-learning-terminologies-and-theoratical-concepts-part-2) 52 | - [**Day-24**](#day-24) 53 | - [**How to save and re-use a model**](#how-to-save-and-re-use-a-model) 54 | - [**Day-25**](#day-25) 55 | - [**Practice session individual**](#practice-session-individual) 56 | - [**Day-26**](#day-26) 57 | - [**How to use Conda environments?**](#how-to-use-conda-environments) 58 | - [**Day-27**](#day-27) 59 | - [**Machine Learning with Tensorflow**](#machine-learning-with-tensorflow) 60 | - [**Day-28**](#day-28) 61 | - [**neural Networks introduction**](#neural-networks-introduction) 62 | - [**Day-29**](#day-29) 63 | - [**Computer Vision in PYTHON**](#computer-vision-in-python) 64 | - [**Day-30**](#day-30) 65 | - [**Image Classification in Tensorflow**](#image-classification-in-tensorflow) 66 | - [**Day-31**](#day-31) 67 | - [**Machine learning and Activation function in Tensorflow**](#machine-learning-and-activation-function-in-tensorflow) 68 | - [**Day-32**](#day-32) 69 | - [**Crash course on Activation Functions**](#crash-course-on-activation-functions) 70 | - [**Day-33**](#day-33) 71 | - [**Project based learning**](#project-based-learning) 72 | - [**What is tensorflow fasion mnist dataset?**](#what-is-tensorflow-fasion-mnist-dataset) 73 | - [**Day-34**](#day-34) 74 | - [**How many epocs should we run in a deep neural network?**](#how-many-epocs-should-we-run-in-a-deep-neural-network) 75 | - [**Day-35**](#day-35) 76 | - [**Types of Neural Networks**](#types-of-neural-networks) 77 | - [**Day-36**](#day-36) 78 | - [**Streamlit Dashboards for Data Science**](#streamlit-dashboards-for-data-science) 79 | - [**Day-37**](#day-37) 80 | - [**Time Series Analysis**](#time-series-analysis) 81 | - [**Day-38**](#day-38) 82 | - [**Introduction to NLP and text classification**](#introduction-to-nlp-and-text-classification) 83 | - [**Day-39**](#day-39) 84 | - [**github and git tools**](#github-and-git-tools) 85 | - [**Day-40**](#day-40) 86 | - [**Use of chatGPT to maintain your social media accounts**](#use-of-chatgpt-to-maintain-your-social-media-accounts) 87 | - [**Feedback**](#feedback) 88 | - [Information about the instructor:](#information-about-the-instructor) 89 | 90 | --- 91 | - [What will you learn (Course Content)](#course-content) 92 | 93 | 95 | - [Important Resources, Books and Blogs](#resources-books-and-blogs-links) 96 | - [Your Feedback matters](#feedback) 97 | 98 | > An online course via zoom and youtube in Urdu/Hindi Language. 99 | > 100 | This repository contains whole material of 40 days course on Python for Data Science in Urdu/Hindi 2023 Details are here: Registration details of our course is given [here](https://www.facebook.com/groups/codanics/permalink/1837695129921140/) 101 | 106 | 107 | 147 | 148 | --> 149 | 150 | ## **Resources, Books and Blogs Links:** 151 | 152 | - [Python for Data Science- Complete Playlist](https://www.youtube.com/playlist?list=PL9XvIvvVL50Fba7psesg6ynQXdipw-yoN) 153 | - Books: 154 | - [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/) 155 | - [Hands On Machine Learning with Scikit Learn and TensorFlow](https://github.com/yanshengjia/ml-road/blob/master/resources/Hands%20On%20Machine%20Learning%20with%20Scikit%20Learn%20and%20TensorFlow.pdf) 156 | 157 | 201 | 202 | 203 | > Everything related to course content in video lecture format will be uploaded **[here](https://youtube.com/playlist?list=PL9XvIvvVL50Fba7psesg6ynQXdipw-yoN)** on youtube. 204 | 205 | ## **Day-1** 206 | > ### **Introduction to Python ka chilla and Data Science** 207 | > 208 | > Python k chilla ki pehli class main hame ye cheezen dekhi hyn: 209 | > - What is Data Science? 210 | > - Daily Life Examples 211 | > - Installation of Python and VScode 212 | > - Writing your first line of code 213 | > - Data Types and operators 214 | > - Different operation in CMD 215 | > - What is an IDE (Integrated Development Environment)? 216 | > - The use of chatGPT was also discussed in this lecture which can be seen in the following video [here](https://youtu.be/1YYUVfNPFaw?t=7) 217 | > 218 | > The video can be seen by clicking on the picture below. 219 | > 220 | [![First Zoom Class](./resources/Day1.png)](https://youtu.be/Ux9ttEM2smk?t=106) 221 | 222 | > ### **Installation of Python and VScode** 223 | > 224 | > Python ko seekhnay se pehlay usay install karna zaroori hy or yahan ham 2 software install karen gay:\ 225 | > 1. Python form [this link](https://www.python.org/downloads/)\ 226 | > 2. VScode (Vidual Studio Code) from [this link](https://code.visualstudio.com/)\ 227 | 228 | Urdu main ham ne in software ko install krne ka tareeqa bhi bta dea hy, neechay image per click kar k video dekh saktay hyn. 229 | 230 | [![Installation of Python and other software](./resources/installation.png)](https://youtu.be/zcqW7Hp-FVk) 231 | 232 | > ### **Write your first line of code with us** 233 | > Is video main hame dekhen gay k ap ne installation k baad first line of code kaisay likhni hy jaisay ap ne [pichli Lecture](#introduction-to-python-ka-chilla-and-data-science) main dekha tha is main ham aik file k through bhi codes run karen gay. 234 | > 235 | > Lecture ko Urdu video main sunnay k liay is imagae per click karen 236 | > 237 | [![Write your first line of code](./resources/first_line.png)](https://youtu.be/4T3cIOi61cg) 238 | 239 | --- 240 | ## **Day-2** 241 | 242 | ### **How to use VScode (an IDE) for Python?** 243 | > Python ka chilla 2023 k doosray din main ham seekhen gay k IDE yani k VScode ko kaisay use karna hy to learn and use python in an efficient manner. 244 | > 245 | > Day-1 main ap ne VScode ko install kia tha ab dekhtay hyn k vscode ko use kaisay karna hy? 246 | > * Is video main ham ne vscode ki top extensions install karna dekhni hyn 247 | > * Python ko VScode main run karna 248 | > * File types or extensions kon kon si hti hyn woh dekhen gay 249 | > * Python ki extension `.py` or `.ipynb` kia hti hyn ye dekhen gay 250 | > * Questions kaisay poochnay hyn woh dekhen gay. Agar ap ka koi question hy tu ap [yahan per click kar k pooch len](https://github.com/AammarTufail/python_ka_chilla2023/discussions) 251 | > * Ap ko isi video main future strategy bhi milay ge k agay walay din kaisay practice karni hy 252 | > 253 | > Is video per click karen and watch this whole session: 254 | > 255 | 256 | 257 | [](https://www.youtube.com/watch?v=NoDwrvFogiU&list=PL9XvIvvVL50Fba7psesg6ynQXdipw-yoN&index=6 "How to use VScode (an IDE) for Python?") 258 | 259 | --- 260 | ## **Day-3** 261 | ### **Basics of Python programming for Data Science (1/2)** 262 | 263 | 264 | 265 | You can learn Basics of Python Programming from these two Videos: 266 | 1. Live Zoom Session of Day-3:\ 267 | [](https://youtu.be/7f2Ns75ibOc "Python-101 Live") 268 | 269 | 2. Python Programming (Python-101) by clicking on this:\ 270 | [](https://youtu.be/930zolu8E2g "Python-101 COmplete Python?") 271 | 272 | ## **Day-4** 273 | ### **Basics of Python programming for Data Science (2/2)** 274 | 275 | 1. Ye day-3 ka lecture hy is ko finish karen or at least 3 martaba practice karen takay ye concepts ap ko clear ho jayen, phir hi agar maza aana seekhnay ka, warna issue hi rehnay ap ko (Agar ap yahan se kuch miss karen gay then ap ko maslay hnay walay hyn agay): 276 | Python Programming (Python-101) by clicking on this:\ 277 | [](https://youtu.be/930zolu8E2g "Python-101 COmplete Python?") 278 | 279 | 1. This was the live zoom session where we discused variables, input_function and much more. watch the video by clicking following figure:\ 280 | [](https://youtu.be/6wd60_vpQ6c "Python-101 Practice?") 281 | 282 | ## **Day-5** 283 | ### **Jupyternotebook Introduction, Libraries and Data Visualization** 284 | 285 | Is lecture main ham dekhen gay k: 286 | 1. jupyter notebooks `.ipynb` extension wali kia hti hyn? [video is here](https://youtu.be/V5Pk5qvRo7A?t=273) 287 | 2. Libraries kia hti hyn, kis liay use htin, Install kaisay ki jati hyn, and import kaisay karni hyn? [video is here](https://youtu.be/V5Pk5qvRo7A?t=2176) 288 | 3. Data Visualization kaisay ki jati hy in Python? [video is here](https://youtu.be/V5Pk5qvRo7A?t=4124) 289 | 4. Complete Lecture is here:\ 290 | [](https://youtu.be/V5Pk5qvRo7A "Day-5 Lecture") 291 | 292 | ### **Markdown Language in 75 Minutes (This video is taken from Python ka chilla 2022** 293 | 294 | - Here you must learn Markdown language to make jupyter notebooks and `.md` files for github before moving ahead.\ 295 | [](https://youtu.be/qJqAXjz-Rh4?t=5 "MarkDown in 72 minutes") 296 | 297 | ## **Day-6** 298 | ### **Pandas_01** 299 | 300 | Here is the dataset to work on [download here](datasets/pandas_01_day6.xlsx) 301 | 302 | In this lecture you will see how pandas library can be used to import the dataset and run basic functions on that dataset 303 | 304 | [](https://www.youtube.com/watch?v=QllPdI6c_Ko&list=PL9XvIvvVL50Fba7psesg6ynQXdipw-yoN&index=11&t=2s "Pandas basics") 305 | 306 | ## **Day-7** 307 | ### **Pandas library basic functions** 308 | 309 | In this lecture you will see how pandas library can be used to import the dataset and run basic functions on that dataset 310 | 311 | [](https://www.youtube.com/watch?v=dnVZryp-qag&list=PL9XvIvvVL50Fba7psesg6ynQXdipw-yoN&index=12 "Pandas basics") 312 | 313 | ## **Day-8** 314 | ### **Pandas tips and tricks** 315 | 316 | Is lecture main ham seekhen gay k `pandas` library ko use karne ki tips and tricks kon konsi hyn.\ 317 | Ye lecture long hyn tu ap se request hy is ko poora dekh k sath sath pactice b karen.\ 318 | Lecture dekhnay k liay neechay is picture per click karen 319 | 320 | [](https://youtu.be/Ozil3_akpR8 "Pandas tips and tricks-Lecture") 321 | 322 | ## **Day-9** 323 | ### **Pandas practice online** 324 | 325 | Is lecture main ham pandas main or b details se practice karen gay.\ 326 | [](https://youtu.be/K_i7VGtzGNU "Pandas practice") 327 | 328 | ## **Day-10** 329 | ### **Data Wrangling and Data Visualization (Basics)** 330 | 331 | Is lecture main ham cheat sheets dekhnay walay hyn jo bht important hun ge data wrangling karne k liay. 332 | Cheat sheets yahan se download kar len [Download all Cheat Sheets here](./resources/cheat_sheets/) 333 | 334 | [](https://youtu.be/XZXmK3D_5-A "Data Wrangling and Data Visualization") 335 | 336 | 337 | ## **Day-11** 338 | ### **Data Visualization** 339 | 340 | Plots or graphs bnanay k liay ap ko python se behtar koi language nahi milni or isi baat ko btanay k liay aaj k lectures hyn. In main ap seekhen gay k python ki different libraries like `matplotlib` and `seaborn` ko use kar kaisay plot bnaye jatay hyn 341 | 342 | 1. Is video main ap seekhen gay plotting hti kia hy or q zaroori hy:\ 343 | [](https://youtu.be/2yWoQ-RICmU "Data Visualization Theory") 344 | 345 | 2. Is video main aap dekhen gay k python main coding kar k plots kaisay bna saktay hyn:\ 346 | [](https://youtu.be/IVtFDKR_KOA "Data Visualization") 347 | 348 | ## **Day-12** 349 | ### **Exploratory Data Analysis (Basics)** 350 | 351 | [](https://youtu.be/sCdu54Mq0FA "Data Wrangling and EDA with python") 352 | 353 | ## **Day-13** 354 | ### **Exploratory Data Analysis (A-Z)** 355 | 356 | [](https://youtu.be/419WLiki7jI "EDA with python") 357 | 358 | ## **Day-14** 359 | ### **ABC of Statistics** 360 | 361 | >Watch the following playlist to learn basic statistics for Data Science.\ 362 | [Click here to watch ABC of Statistics](https://youtube.com/playlist?list=PL9XvIvvVL50Hsio_tunNVlAq9XhB4cU2J) 363 | 364 | ## **Day-15** 365 | ### **Python Data Wrangling Techniques: From Beginner to Pro** 366 | 367 | >Today we will learn Data Wrangling in python: 368 | 369 | **`Data wrangling`**, also known as `data munging`, is the process of cleaning, transforming, and organizing data in a way that makes it more suitable for analysis. It is a crucial step in the data science process as real-world data is often messy and inconsistent. 370 | 371 | [](https://www.youtube.com/watch?v=jp93e9DDBlo&list=PL9XvIvvVL50Fba7psesg6ynQXdipw-yoN&index=21 "Data Wrangling") 372 | 373 | The general steps to do `Data Wrnagling` in python are as follows: 374 | 375 | >**Steps to perform data wrangling on the Titanic dataset in Python using pandas library:** 376 | >The steps of data wrangling in Python typically include: 377 | 1. Importing necessary libraries such as Pandas, NumPy, and Matplotlib 378 | 2. Loading the data into a Pandas DataFrame 379 | 3. Assessing the data for missing values, outliers, and inconsistencies 380 | 4. Cleaning the data by filling in missing values, removing outliers, and correcting errors 381 | 5. Organizing the data by creating new columns, renaming columns, sorting, and filtering the data 382 | 6. Storing the cleaned data in a format that can be used for future analysis, such as a CSV or Excel file 383 | 7. Exploring the data by creating visualizations and using descriptive statistics 384 | 8. Creating a pivot table to summarize the data 385 | 9. Checking for and handling duplicate rows 386 | 10. Encoding categorical variables 387 | 11. Removing unnecessary columns or rows 388 | 12. Merging or joining multiple datasets 389 | 13. Handling missing or null values 390 | 14. Reshaping the data 391 | 15. Formatting the data 392 | 16. Normalizing or scaling the data 393 | 17. Creating new features from existing data 394 | 18. Validating data integrity 395 | 19. Saving the final data for future use 396 | 20. Documenting the data wrangling process for reproducibility 397 | 398 | Please note that the steps may vary depending on the data, the requirements, and the goals of the analysis. 399 | It's worth noting that these are general steps and the specific steps you take will depend on the dataset you are working with and the analysis you plan to perform. 400 | 401 | ---- 402 | Here is an example of how to perform data wrangling on the `titanic` dataset in Python using the pandas library: 403 | 404 | ```python 405 | # Import the necessary libraries 406 | import pandas as pd 407 | import numpy as np 408 | import matplotlib.pyplot as plt 409 | import seaborn as sns 410 | 411 | # Load the Titanic dataset into a pandas DataFrame 412 | titanic = sns.load_dataset('titanic') 413 | 414 | # View the first few rows of the dataset 415 | titanic.head() 416 | 417 | # View the column names and data types 418 | titanic.info() 419 | 420 | # Check for missing values 421 | print(data.isnull().sum()) 422 | 423 | # Handle missing values 424 | # Option 1: Drop rows with missing values 425 | titanic.dropna(inplace=True) 426 | 427 | # Option 2: Impute missing values 428 | titanic['Age'].fillna(titanic['Age'].mean(), inplace=True) 429 | 430 | 431 | # Check for outliers and remove or transform them as necessary 432 | sns.boxplot(x=titanic['age']) 433 | 434 | # Transform outliers 435 | titanic['Age'] = np.log(titanic['Age']) 436 | 437 | # Feature engineering 438 | titanic['family_size'] = titanic['sibsp'] + titanic['parch'] + 1 439 | titanic['is_alone'] = 1 # initialize to yes/1 is alone 440 | titanic['is_alone'].loc[titanic['family_size'] > 1] = 0 # now update to no/0 if family size is greater than 1 441 | 442 | # Group and aggregate data 443 | data = titanic.groupby('Pclass').mean() 444 | 445 | # Save the cleaned dataset 446 | titanic.to_csv('titanic_cleaned.csv', index=False) 447 | ``` 448 | This is just one example of how to perform data wrangling on the Titanic dataset, but there are many other ways you can handle missing values, outliers, and feature engineering. The important thing is to understand the data, and to make decisions based on the context of the problem you're trying to solve. 449 | 450 | Another way of treating the data is as follows: 451 | 452 | ```python 453 | # Import the necessary libraries 454 | import pandas as pd 455 | import numpy as np 456 | import matplotlib.pyplot as plt 457 | import seaborn as sns 458 | 459 | 460 | # Load the Titanic dataset 461 | data = pd.read_csv('titanic.csv') 462 | 463 | # View the first few rows of the dataset 464 | print(data.head()) 465 | 466 | # Check for missing values 467 | print(data.isnull().sum()) 468 | 469 | # Handle missing values 470 | data['age'].fillna(data['age'].median(), inplace=True) 471 | data['embarked'].fillna(data['embarked'].mode()[0], inplace=True) 472 | 473 | # Check for outliers 474 | # Option 1: Remove outliers 475 | q1 = data["fare"].quantile(0.25) 476 | q3 = data["fare"].quantile(0.75) 477 | iqr = q3-q1 478 | fence_low = q1-1.5*iqr 479 | fence_high = q3+1.5*iqr 480 | data = data[(data["fare"] > fence_low) & (data["fare"] < fence_high)] 481 | 482 | # Option 2: Transform outliers 483 | data['fare'] = data['fare'].apply(lambda x: x if x < 100 else 100) 484 | 485 | # Feature engineering 486 | #if the titles are given in the dataset 487 | data['Title'] = data.Name.str.extract(' ([A-Za-z]+)\.', expand=False) 488 | data['Title'] = data['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare') 489 | data['Title'] = data['Title'].replace('Mlle', 'Miss') 490 | data['Title'] = data['Title'].replace('Ms', 'Miss') 491 | data['Title'] = data['Title'].replace('Mme', 'Mrs') 492 | data = data.drop(['Name'], axis=1) 493 | data = pd.get_dummies(data, columns = ["Title"]) 494 | 495 | # Group and aggregate data 496 | data = data.groupby(['Pclass', 'Sex']).mean() 497 | 498 | # Save the cleaned dataset 499 | data.to_csv('titanic_cleaned.csv', index=True) 500 | ``` 501 | In this example I have used IQR method to check for outliers, and I have used some feature engineering techniques, like extracting title from the name and creating dummies variables and also I have grouped the data by Pclass and Sex and taken the mean of the data. 502 | 503 | It's important to note that the steps you take during data wrangling will vary depending on the dataset and the specific analysis you plan to perform. The examples above should give you an idea of the types of tasks that are typically involved in data wrangling and how to perform them using the pandas library. 504 | 505 | --- 506 | ## **Day-16** 507 | ### **Machine Learning Basics** 508 | 509 | In this lecture we will learn what is machine learning and how we can implement that in our everyday life, projects and science themes. 510 | 511 | [](https://youtu.be/AJElOIz8ysU "Machine Learning Zero-Advanced level | in Urdu/Hindi | Day-16") 512 | 513 | ---- 514 | ## **Day-17** 515 | ### **Machine Learning Basics-1** 516 | 517 | In this lecture we will learn what is machine learning with explained and detailed example 518 | - Types of Machine learning 519 | - Algorithms in ML 520 | - Regression vs. Classification 521 | - Much more\ 522 | 523 | Here is the video: 524 | 525 | [](https://www.youtube.com/watch?v=oCguRWNFqs4&list=PL9XvIvvVL50Fba7psesg6ynQXdipw-yoN&index=23&t=1282s "What is Machine Learning") 526 | 527 | ---- 528 | ## **Day-18** 529 | ### **Regression (Machine Learning Basics-2)** 530 | 531 | In this lecture we will learn how to use linear regression model in Machine learning, what is it and how we can implement that in real life? 532 | 533 | Here is the video: 534 | 535 | [](https://www.youtube.com/watch?v=yEMS0QXflew&list=PL9XvIvvVL50Fba7psesg6ynQXdipw-yoN&index=24 "Linear Regression in Machine Learning") 536 | 537 | ---- 538 | ## **Day-19** 539 | ### **Classification (Machine Learning Basics-3)** 540 | 541 | In this lecture we will learn how to use classification model in Machine learning, what is it and how we can implement that in real life? 542 | 543 | Here is the video: 544 | 545 | [](https://youtu.be/0k7hEy_9fJg "Classification in Machine learning | in Urdu/Hindi | Day-19") 546 | 547 | ---- 548 | ## **Day-20** 549 | ### **How to select a best Model in Machine learning with Scikit-learn?** 550 | 551 | In this lecture we will learn how to select a best model in Machine learning, what is it and how we can implement that in real life? 552 | 553 | Here is the video: 554 | 555 | [](https://www.youtube.com/watch?v=XiUJwXglo5s&list=PL9XvIvvVL50Fba7psesg6ynQXdipw-yoN&index=26 "ML with Scikit-learn | in Urdu/Hindi | Day-20") 556 | 557 | Send me your assignment if you enrolled in the course: 558 | 559 | Here is the code mentioned in the video: 560 | 561 | ```python 562 | # Import the necessary libraries 563 | # import libraries 564 | import pandas as pd 565 | import numpy as np 566 | import seaborn as sns 567 | import matplotlib.pyplot as plt 568 | 569 | df = sns.load_dataset("titanic") 570 | X = df[['pclass', 'sex', 'age', 'sibsp', 'parch', 'fare']] 571 | y = df['survived'] 572 | X = pd.get_dummies(X, columns=['sex']) 573 | X.age.fillna(value = X['age'].mean(), inplace=True) 574 | 575 | 576 | from sklearn.linear_model import LogisticRegression 577 | from sklearn.svm import SVC 578 | from sklearn.tree import DecisionTreeClassifier 579 | from sklearn.ensemble import RandomForestClassifier 580 | from sklearn.neighbors import KNeighborsClassifier 581 | from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score 582 | from sklearn.model_selection import train_test_split 583 | 584 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) 585 | 586 | models = [LogisticRegression(), SVC(), DecisionTreeClassifier(), RandomForestClassifier(), KNeighborsClassifier()] 587 | model_names = ['Logistic Regression', 'SVM', 'Decision Tree', 'Random Forest', 'KNN'] 588 | 589 | models_scores = [] 590 | for model, model_name in zip(models, model_names): 591 | model.fit(X_train, y_train) 592 | y_pred = model.predict(X_test) 593 | accuracy = accuracy_score(y_test, y_pred) 594 | models_scores.append([model_name,accuracy]) 595 | 596 | sorted_models = sorted(models_scores, key=lambda x: x[1], reverse=True) 597 | for model in sorted_models: 598 | print("Accuracy Score: ",f'{model[0]} : {model[1]:.2f}') 599 | 600 | 601 | # Accuracy Score: Random Forest : 0.81 602 | # Accuracy Score: Decision Tree : 0.79 603 | # Accuracy Score: KNN : 0.76 604 | # Accuracy Score: Logistic Regression : 0.75 605 | # Accuracy Score: SVM : 0.74 606 | 607 | models = [LogisticRegression(), SVC(), DecisionTreeClassifier(), RandomForestClassifier(), KNeighborsClassifier()] 608 | model_names = ['Logistic Regression', 'SVM', 'Decision Tree', 'Random Forest', 'KNN'] 609 | models_scores = [] 610 | for model, model_name in zip(models, model_names): 611 | model.fit(X_train, y_train) 612 | y_pred = model.predict(X_test) 613 | Precision = precision_score(y_test, y_pred) 614 | models_scores.append([model_name,Precision]) 615 | 616 | sorted_models = sorted(models_scores, key=lambda x: x[1], reverse=True) 617 | for model in sorted_models: 618 | print("Precision Score: ", f'{model[0]} : {model[1]:.2f}') 619 | 620 | # Precision Score: Random Forest : 0.80 621 | # Precision Score: Decision Tree : 0.78 622 | # Precision Score: KNN : 0.75 623 | # Precision Score: Logistic Regression : 0.74 624 | # Precision Score: SVM : 0.73 625 | 626 | models = [LogisticRegression(), SVC(), DecisionTreeClassifier(), RandomForestClassifier(), KNeighborsClassifier()] 627 | model_names = ['Logistic Regression', 'SVM', 'Decision Tree', 'Random Forest', 'KNN'] 628 | models_scores = [] 629 | for model, model_name in zip(models, model_names): 630 | model.fit(X_train, y_train) 631 | y_pred = model.predict(X_test) 632 | Recall = recall_score(y_test, y_pred) 633 | models_scores.append([model_name,Recall]) 634 | 635 | sorted_models = sorted(models_scores, key=lambda x: x[1], reverse=True) 636 | for model in sorted_models: 637 | print("Recall Score: ",f'{model[0]} : {model[1]:.2f}') 638 | 639 | # Recall Score: Random Forest : 0.74 640 | # Recall Score: Decision Tree : 0.72 641 | # Recall Score: KNN : 0.68 642 | # Recall Score: Logistic Regression : 0.67 643 | # Recall Score: SVM : 0.65 644 | 645 | models = [LogisticRegression(), SVC(), DecisionTreeClassifier(), RandomForestClassifier(), KNeighborsClassifier()] 646 | model_names = ['Logistic Regression', 'SVM', 'Decision Tree', 'Random Forest', 'KNN'] 647 | models_scores = [] 648 | for model, model_name in zip(models, model_names): 649 | model.fit(X_train, y_train) 650 | y_pred = model.predict(X_test) 651 | F1 = f1_score(y_test, y_pred) 652 | models_scores.append([model_name,F1]) 653 | 654 | sorted_models = sorted(models_scores, key=lambda x: x[1], reverse=True) 655 | for model in sorted_models: 656 | print("F1 Score: ",f'{model[0]} : {model[1]:.2f}') 657 | 658 | # F1 Score: Random Forest : 0.77 659 | # F1 Score: Decision Tree : 0.75 660 | # F1 Score: KNN : 0.71 661 | # F1 Score: Logistic Regression : 0.70 662 | # F1 Score: SVM : 0.68 663 | ``` 664 | 665 | ---- 666 | ## **Day-21** 667 | ### **How to select a best parameters in a Machine learning model with Scikit-learn?** 668 | 669 | In this lecture we will learn how to select a best parameters in a model using gridsearch CV in scikit-learn 670 | 671 | Here is the video: 672 | 673 | [](https://youtu.be/-IHgSW5dB5s "Grid Seach cv | in Urdu/Hindi | Day-21") 674 | 675 | Here is the code mentioned in the video: 676 | 677 | ```python 678 | # Decision Tree Classifier and use best parameters 679 | import pandas as pd 680 | import seaborn as sns 681 | df = sns.load_dataset("titanic") 682 | X = df[['pclass', 'sex', 'age', 'sibsp', 'parch', 'fare']] 683 | y = df['survived'] 684 | X = pd.get_dummies(X, columns=['sex']) 685 | X.age.fillna(value = X['age'].mean(), inplace=True) 686 | 687 | from sklearn.tree import DecisionTreeClassifier 688 | from sklearn.model_selection import GridSearchCV 689 | import numpy as np 690 | #create a model 691 | model = DecisionTreeClassifier() 692 | # define parameter grid 693 | param_grid = {'max_depth': [3, 5, 7, None], 'min_samples_split': [2, 3, 4]} 694 | 695 | #object grid search cv (Creating the model) 696 | grid_search = GridSearchCV(model, param_grid, cv=5, scoring='precision') 697 | 698 | #traing the model 699 | grid_search.fit(X,y) 700 | 701 | # print the best parameters 702 | print("Best Parameters: ", grid_search.best_params_) 703 | print("Best Score: ", grid_search.best_score_) 704 | 705 | # Best Parameters: {'max_depth': 3, 'min_samples_split': 2} 706 | # Best Score: 0.775 707 | ``` 708 | 709 | In your assignments: 710 | 711 | Please write a code where you can select the best model based on grid search cv! 712 | 713 | 714 | ---- 715 | 716 | ## **Day-22** 717 | ### **Machine learning terminologies and theoratical concepts (Part-1)** 718 | 719 | Sab se pehlay is lecture main ap o K-means clustering kaisay python main apply ki jati hy woh sikhaya jaye ga: 720 | 721 | [](https://youtu.be/jdYTpYu4THE "Kmeans Clustering in Python") 722 | 723 | 724 | 725 | Ab se agalay 2 din ham machine learning k basic concepts dekhnay walay hyn, jo terminologies machine learning main use hti hyn woh seekhen gay, ye lectures bht important hyn is liay inhen skip na kijeay ga. 726 | 727 | Agar ap yahan tak seekh ayen hyn tu yaqeen manen ap ny in concepts ko already bht had tak dekha hy, ab clear ho jayen gay or. 728 | 729 | 730 | > Es video may hum Machine Learning ka introduction dekhyn gyn k wo hoti kya hay or is ML playlist may hum ainda kya chzyn dekhny walay hayn. 731 | > 732 | [](https://www.youtube.com/watch?v=eYXCw2FQfPA "ML") 733 | 734 | > Cross validation ka nam hum bht zeada suntay hayn ML ki dunya may or is video may hum bht h desi example k sath dekhyn gyn k Cross validation (CV) kya hota hay or us ki mukhtalif types i.e., 4 fold CV, 10 fold CV, etc. 735 | 736 | [](https://www.youtube.com/watch?v=iy3B94qUAV8 "ML") 737 | 738 | > Confusion matrix hmyn bht h zeada confuse rakhta hay is liay isko asan bnanay k liay ye video ap k liay desi examples k sath mojood hay jis may hum nay dekha hay k confusion matrix ki zrurat kiun hay or hum kis trhn bnatay hayn jis may True Positive, True Negatives, False Positives or, False Negatives ko smjha hay. 739 | 740 | [](https://www.youtube.com/watch?v=bm_pw-TxwmE "ML") 741 | 742 | > Jab hum koi b model bnatay hayn to us ko chk krty hayn k wo kitna acha kam kr raha hay. Sirf accuracy say kam nai chlta hr baar is liay ye dekhna prta hay k us k andr model kitnay positives (sensitivity) or kitnay negatives ( specificity) sai say model nay btaey hayn. 743 | 744 | [](https://www.youtube.com/watch?v=2nwjKvFsxBI "ML") 745 | 746 | > Agar ap nay 2 model bnaey hayn jis may say 1 model bht acha fit hua hay or aek model bht achi prediction kr raha hay to apko un dono ko kesay istemal krna chaheay is ko smjhnay k liay BIAS -VARIANCE tradeoff ka concept smjhna zruri hay. 747 | 748 | [](https://www.youtube.com/watch?v=12hx3VCoShY "ML") 749 | 750 | > Entropy Ka lfzi mtlb to ye hay k ap k data may Randomness/ disorder Kitna hay lekin machine learning ki dunya may is ko hum kis trhn istemal krtay hayn is video may achay say bht h sada or asan treqay say smjhaya hay ta k ap ainda jab b Entropy istemal kryn ML may to apko idea ho k ap isay kiun or kesay istemal kr skty hayn. 751 | 752 | [](https://www.youtube.com/watch?v=VUroGmmD1hc "ML") 753 | 754 | > Bht h zeada asan or aam ML Ka model hay linear regression model pr is may istemal honay walay concepts sbb KO smjh nai aatay. Is liay is video may ye btaya geya hay k linear regression hota kya hay, residual kisay kehtay hayn. 755 | 756 | [](https://www.youtube.com/watch?v=fXTMJniHdpc "ML") 757 | 758 | > Hum square kiun krty hayn difference KO or least squared residuals kya hotay hayn or kiun hum usay regression may dekhty hayn isi trhn agar hum higher dimensions may jaeyn ( aam lfzon may agar humaray paas 1 say zeada independent variables hon to kis trhn us may regression Ka model lgta hay us k liay ye video dekhna ap k liay bht zruri hay ( multiple regression) 759 | 760 | [](https://www.youtube.com/watch?v=iptI-dqLr-M "ML") 761 | 762 | ---- 763 | 764 | ## **Day-23** 765 | ### **Machine learning terminologies and theoratical concepts (Part-2)** 766 | 767 | 768 | > Aam dunya may sbb Kuch linear nai hota isi liay hum HR Baar linear regression k models istemal nai krty is liay jab b hmaray paas dependent variable may categorical/Boolean data ho to hum istemal krtay hayn Logistic regression. Is video may hum nay Dekha k logistics regression kis trhn different hay linear regression say, s curve Ka kya concept hay or kya higher dimensional logistic regression Hoti hay. 769 | 770 | [](https://www.youtube.com/watch?v=Kmk9EeFnyHM "ML") 771 | 772 | 773 | > ROC ( Receive operating characteristic) and AUC ( area under the curve) dono mil k hmyn btatay hayn k model kis trhn perform kr Raha hay. ROC aek probability curve hay or AUC us may ye btata hay k measure of separability kitni hay model ki ye dono aek sath accuracy say zeada Acha model ko evaluate krty hayn jab class imbalanced Hoti hay, is video may ye chz asan lfzon may discuss ki hui hay or ye b btaya geya hay k hum in KO kesay bna skty hayn or kesay interpretation kr skty hayn. 774 | 775 | [](https://www.youtube.com/watch?v=-f-mvDObG1U "ML") 776 | 777 | > Logistic regression may s-curve ko fit krnay k liay hum nay Jo method istemal Kia hay ( maximum likelihood) Ka wo in detail kesay lgta hay or is may hum least squared residuals wala method kiun istemal nai krty. 778 | 779 | [](https://www.youtube.com/watch?v=CQOzUDd_83U "ML") 780 | 781 | > Or agar hum model ko chk kr rhy hayn k model Kitna accurate and reliable hay to us liay hum R-squared logistic regression may kis trhn calculate krty hayn or interpret krty hayn. 782 | > 783 | [](https://www.youtube.com/watch?v=JmVvPqR44h4 "ML") 784 | 785 | 786 | > jab b hmaray model may overfitting/underfitting Ka issue ata hay to hum Realizations techniques use krty hayn or is may data or model ki noyiat Dekh k hum ye faisla krty hayn k hum kis technique pay focus kryn gyn. Jab data may bht saray usefull variables hon to hum mostly L2 use krty hayn. 787 | 788 | [](https://www.youtube.com/watch?v=nv-HHBxmfv4 "ML") 789 | 790 | > or jab useless variables zeada hon to hum L1 use krty hayn. 791 | 792 | [](https://www.youtube.com/watch?v=QDSQivx78eA "ML") 793 | 794 | > or jab hum drmean may hon to phir hybrid technique Elastic net istemal krtay hayn. 795 | 796 | [](https://www.youtube.com/watch?v=AwbS2d1xYIQ "ML") 797 | 798 | > Principal component analysis (PCA) hum zeada tr feature selection/ dimension reduction k liay istemal krtay hayn. Is video may hum nay ye Dekha hay in detail k ML may exactly kb or kesay PCA istemal KR k apna Kam asaan Kia ja skta hay or is may mojooe eigen vectors Ka concept b asan lfzon may btaya geya hay. 799 | 800 | [](https://www.youtube.com/watch?v=FufGzT9az4Y "ML") 801 | 802 | > Regression techniques k baad hum nay ye Dekha k Clustering kis trhn ki jati hay or is Ka mtlb kya hay or zrurat kiun Hoti hay. Aek bht h asan or famous techniques K-MEANS CLUSTERING ki hum is may kesay istemal krtay hayn or is may K , MEANS dono Ka mtlb kya hay or hum kesay decide krty hayn k K kya lena chaheay kis say hmara Kam asaan ho sky. 803 | 804 | [](https://www.youtube.com/watch?v=W9cl8xKQPOc "ML") 805 | 806 | ## **Day-24** 807 | ### **How to save and re-use a model** 808 | 809 | Is lecture main ham ne seekha k kis trah ham apna trained ML model save kartay hyn, click the image below to watch the video lecture: 810 | 811 | [](https://www.youtube.com/watch?v=b0NnVf31vps&list=PL9XvIvvVL50Fba7psesg6ynQXdipw-yoN&index=29 "joblib") 812 | 813 | ## **Day-25** 814 | ### **Practice session individual** 815 | 816 | Download any dataset on covid and submit the EDA on telegram if you want to have a feedback. 817 | 818 | 1. Download the data from [google datasearch](https://datasetsearch.research.google.com/) 819 | 2. Read in python 820 | 3. Run EDA analysis 821 | 4. Do data wrangling 822 | 5. Data Visualization 823 | 6. Machine learning model of your choice 824 | 7. Submit 825 | 826 | ## **Day-26** 827 | ### **How to use Conda environments?** 828 | 829 | [](https://youtu.be/nsFdTgwDrVE "conda environments") 830 | 831 | ## **Day-27** 832 | ### **Machine Learning with Tensorflow** 833 | 834 | You will learn these in this lecture: 835 | 836 | 1. What are conda environments? 837 | 2. How to install tensorflow for GPU and CPU PCs? 838 | 1. [Here's how install in Windows PC](https://www.tensorflow.org/install/pip#step-by-step_instructions) 839 | 2. [Here's how to install in Mac M1/M2](https://medium.com/mlearning-ai/install-tensorflow-on-mac-m1-m2-with-gpu-support-c404c6cfb580) 840 | 3. What is git and github? 841 | 842 | 843 | [](https://youtu.be/iNENqZOrgiY "tensorflow") 844 | 845 | After this lecture you have send the video presentation as mentioned in the lecture. 846 | 847 | ## **Day-28** 848 | ### **neural Networks introduction** 849 | 850 | Is video main ap neural network or is ki types dekhnay walay hyn. 851 | 852 | [](https://www.youtube.com/watch?v=6AlfcbhgBvo&list=PL9XvIvvVL50Fba7psesg6ynQXdipw-yoN&index=32&t=970s "neural networks") 853 | 854 | 855 | ## **Day-29** 856 | ### **Computer Vision in PYTHON** 857 | 858 | This video will give you the basic concepts of computer vision in python. 859 | 860 | [](https://youtube.com/live/5VZX-ovRgAgDay29 "computer vision") 861 | 862 | ## **Day-30** 863 | ### **Image Classification in Tensorflow** 864 | 865 | This video will give you the basic concepts of activation function in tensorflow. 866 | 867 | [](https://youtu.be/ch2Yl40D9zk "Image Classification in Tensorflow") 868 | 869 | ## **Day-31** 870 | ### **Machine learning and Activation function in Tensorflow** 871 | 872 | This video will give you the basic concepts of activation function in tensorflow. 873 | 874 | [](https://youtube.com/live/UEPkIu8mHL4 "ML with TF") 875 | 876 | ## **Day-32** 877 | ### **Crash course on Activation Functions** 878 | 879 | This video will give you the basic concepts of activation function in tensorflow. 880 | 881 | [](https://youtu.be/tW6v915O4g4 "Activation Functions") 882 | 883 | ## **Day-33** 884 | ### **Project based learning** 885 | 886 | In thie lecture we used fashion mnist dataset to do some machine learning and deep learning tasks in Python with tensorflow. 887 | 888 | 889 | #### **What is tensorflow fasion mnist dataset?** 890 | 60,000 28x28 grayscale images of 10 fashion categories, along with a test set of 10,000 images. 891 | This dataset can be used as a drop-in replacement for MNIST. The class labels are: 892 | Label Description 893 | * 0: T-shirt/top 894 | * 1: Trouser 895 | * 2: Pullover 896 | * 3: Dress 897 | * 4: Coat 898 | * 5: Sandal 899 | * 6: Shirt 900 | * 7: Sneaker 901 | * 8: Bag 902 | * 9: Ankle boot 903 | 904 | HERE IS THE LECTURE:\ 905 | [](https://www.youtube.com/watch?v=Is0EB1jkop8&list=PL9XvIvvVL50Fba7psesg6ynQXdipw-yoN&index=37 "Project based learning") 906 | 907 | ## **Day-34** 908 | ### **How many epocs should we run in a deep neural network?** 909 | 910 | Call back function ko use kar k ham andaza laga saktay hyn k kitnay epochs use karnay chaheay based on the accuracy or loss, is lecture main ap ko sab clear hnay wala hy. 911 | 912 | HERE IS THE LECTURE:\ 913 | [](https://youtu.be/o_41Gxk_JQA "Project based learning") 914 | 915 | ## **Day-35** 916 | ### **Types of Neural Networks** 917 | 918 | In this lecture we will learn about the types of neural networks. 919 | 920 | HERE IS THE LECTURE:\ 921 | [](https://youtu.be/GGOjRuiU3fE "Types of Neural Networks") 922 | 923 | 924 | ## **Day-36** 925 | ### **Streamlit Dashboards for Data Science** 926 | 927 | Is lecture main ap streamlit ko dekhen gay. jo aik library hy jis se ap behtareen qisam ki webapps bna saktay hyn asani se. 928 | 929 | Ye din several parts main hy is liay bear with me and learn alot today. 930 | 931 | 1. Intro to Streamlit 932 | 933 | [](https://youtu.be/5bY-g9p_mxs "Streamlit-1") 934 | 935 | 2. Streamlit with titanic dataset 936 | 937 | [](https://youtu.be/hy0A6wxlBuM "Streamlit-2") 938 | 939 | 3. Streamlit with Plotly 940 | 941 | [](https://youtu.be/FIkHuQ5-94w "Streamlit-3") 942 | 943 | 4. Animated plots with Streamlit & Plotly 944 | 945 | [](https://youtu.be/gIKgqBH-dTM "Streamlit-4") 946 | 947 | 5. Streamlit webb app for EDA analysis 948 | 949 | [](https://youtu.be/crZcSoBLYaM "Streamlit-5") 950 | 951 | 6. Streamlit k Jugaar 952 | 953 | [](https://youtu.be/ye7uSnLGkXY "Streamlit-6") 954 | 955 | 7. Machine Leaning Web-application in python with streamlit 956 | 957 | [](https://youtu.be/goR8rWxJ-j0 "Streamlit-7") 958 | 959 | 8. Deploy a streamlit data science app online 960 | 961 | [](https://youtu.be/_Dc42sybVoQ "Streamlit-8") 962 | 963 | 9. Add video & audio to your streamlit data science webapp in python 964 | 965 | [](https://youtu.be/m9KhCRwKOgI "Streamlit-9") 966 | 10. Add code to streamlit webapp 967 | 968 | [](https://youtu.be/wnRncTcyuFM "Streamlit-10") 969 | 970 | 11. Make Interactive Dashboard with Explainer Dashboard 971 | 972 | [](https://youtu.be/WlKenV8m3xU "Streamlit-11") 973 | 974 | 12. Embedding code snippets in streamlit webapp 975 | 976 | [](https://youtu.be/LwAAxmS2X0Y "Streamlit-12") 977 | 978 | 13. Streamlit App development with Python `project based` 979 | 980 | [](https://youtube.com/live/a6uAZ_bv6Yw "Streamlit-13") 981 | 982 | 14. Data Science Web app development via Streamlit in Python (Project based) 983 | 984 | [](https://youtube.com/live/cLlFT3M-NPQ "Streamlit-14") 985 | 986 | 987 | ## **Day-37** 988 | ### **Time Series Analysis** 989 | 990 | Is lecture main time series analysis ko dekhen gay. 991 | Ye lecture two parts main hy. 992 | 993 | 1. Intro to Time Series Analysis 994 | 995 | [](https://youtube.com/live/VgYCEhui6FE "Time series") 996 | 997 | 2. Advance Time Series Analysis 998 | 999 | [](https://youtube.com/live/k4zP_EVJSP0 "Times series") 1000 | 1001 | 1002 | ## **Day-38** 1003 | ### **Introduction to NLP and text classification** 1004 | 1005 | 1006 | In this lecture we will learn about the basics of NLP and text classification. 1007 | 1008 | HERE IS THE LECTURE:\ 1009 | [](https://youtube.com/live/ytXo6nnPsSQ "NLP") 1010 | 1011 | - Use of Tensorboad in Machine Learning 1012 | 1013 | HERE IS THE LECTURE:\ 1014 | [](https://youtube.com/live/EMPS8s8s4Hk "tensorboard") 1015 | 1016 | ## **Day-39** 1017 | ### **github and git tools** 1018 | 1019 | In this lecture we will learn about the github and git tools. Ap is main seekhen gay k kaisay github ko sue kar k ap apnay documentation save kar saktya hyn. 1020 | 1021 | HERE IS THE LECTURE:\ 1022 | [](https://youtube.com/live/twuCzvsjno0 "github") 1023 | 1024 | ## **Day-40** 1025 | ### **Use of chatGPT to maintain your social media accounts** 1026 | 1027 | 1028 | In this lecture we will learn about the use of chatGPT to maintain your social media accounts. 1029 | 1030 | HERE IS THE LECTURE:\ 1031 | [](https://youtube.com/live/1WUM42VXQDo "chatGPT for SOcial Media") 1032 | 1033 | --- 1034 | ## **Feedback** 1035 | 1036 | Your feedback matters alot, may you please comment on the following [post on facebook](https://www.facebook.com/groups/codanics/permalink/1872283496462303/) to give use your feed back? 1037 | 1038 | [](https://www.facebook.com/groups/codanics/permalink/1872283496462303/ "Feedback") 1039 | 1040 | --- 1041 | 1042 | # Information about the instructor: 1043 | 1044 | [](https://www.facebook.com/groups/codanics/permalink/1872283496462303/ "Image") 1045 | 1046 | **Dr. Muhammad Aammar Tufail** 1047 | 1048 | PhD Data Science in Agriculture\ 1049 | [Youtube channel](https://www.youtube.com/channel/UCmNXJXWONLNF6bdftGY0Otw/)\ 1050 | [Twitter](https://twitter.com/aammar_tufail)\ 1051 | [Linkedin](https://www.linkedin.com/in/muhammad-aammar-tufail-02471213b/) 1052 | [github](https://github.com/AammarTufail) 1053 | 1054 | contact: aammar@codanics.com 1055 | 1056 | -------------------------------------------------------------------------------- /datasets/pandas_01_day6.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/datasets/pandas_01_day6.xlsx -------------------------------------------------------------------------------- /notebooks/01_python_basics.md: -------------------------------------------------------------------------------- 1 | # **Basics of Python** 2 | (Python ka chilla 2023 by [Dr. Muhammad Aammar Tufail](https://github.com/AammarTufail))\ 3 | This course is in Urdu/Hindi Language designed for the students and professionals to become experts in Data Science/Machine Learnin/Artificial Intelligence 4 | 5 | ## 1. Python hy ya calculator? 6 | Python ki basic command line aisay hi kaam karti hy jaisay calculator ho. Ap sirf likhtay jayen or woh perform karta jaye ga. Expression straight forward hy `+, -, *, and /` same usi tareeqay se kaam karta hy jaisay doosray software k function htay like MS Excel and other spreadsheet software. Is main paranthesis `(())` ko use kar k ham cheezon ko group b kar saktay hyn. 7 | 8 | For Example: 9 | ```python 10 | >>> 2 + 3 #used for Addition 11 | 5 12 | >>> 2 * 3 # * used for multiplication 13 | 6 14 | >>> 2 / 3 # / used for division 15 | 0.6666666666666666 16 | >>> 2 - 3 # - used for subtracttion 17 | -1 18 | >>> 17 / 3 # classic division returns a float 19 | 5.666666666666667 20 | >>> 17 // 3 # floor division discards the fractional part 21 | 5 22 | >>> 17 % 3 # the % operator returns the remainder of the division 23 | 2 24 | >>> 5 * 3 + 2 # floored quotient * divisor + remainder 25 | 17 26 | >>> (2 + 3) * 4 # () used for grouping 27 | 20 28 | >>> 2 + 3 * 4 # multiple signs follow rules DMAS, BODMAS or PEMDAS (please google these) 29 | 14 30 | ``` 31 | 32 | -------------------------------------------------------------------------------- /notebooks/02_data_structures_and_functions.md: -------------------------------------------------------------------------------- 1 | 2 | Is lecture main ham dekhen gay k data structures and sequences kia hti hyn. 3 | >1. **Tuple** 4 | >- Sequence of python objects 5 | >- Fixed length 6 | >- Immutable (once assigned, can not be changed) 7 | >- The easiest way to create them is with comman-separated seuquence of objects/python values having paranthese around them: 8 | 9 | ```python 10 | tup = (1,2,3,4,5) 11 | ``` 12 | >in many context of tuples, we can remove paranthesis 13 | ```python 14 | tup = 1,2,3,4,5 15 | ``` 16 | You can also convert any sequence or iterator to a tuple by invoking tuple? 17 | ```python 18 | tup = tuple([1,2,3,4,5]) 19 | ``` 20 | `Tuple` of string is as follows 21 | ```python 22 | tup = ('string') 23 | tup 24 | ``` 25 | > Elements can be accessed with square brackets [] as with most other sequence types. 26 | ```python 27 | tup[0] #output: s 28 | ``` 29 | Nested Tuples: Assignment? 30 | ```python 31 | nested_tuple = tuple(['foo', [1, 2], True]) 32 | ``` 33 | >2. **List** 34 | >- Sequence of python objects 35 | >- Mutable 36 | >- Can be defined as a comma-separated sequence of objects between square brackets 37 | ```python 38 | a_list = [1,2,3,4,5] 39 | ``` 40 | >3. **Dictionary** 41 | >- A mapping of unique keys to values 42 | >- Can be defined by using curly braces {} and colons to separate keys and values 43 | ```python 44 | a_dict = {'a':1, 'b':2, 'c':3} 45 | ``` 46 | >4. **Set** 47 | >- An unordered collection of unique elements 48 | >- Can be defined by placing a comma-separated sequence of elements between curly braces 49 | ```python 50 | a_set = {1,2,3,4,5} 51 | ``` 52 | Sets support mathematical set operations like union, intersection, difference, and symmetric difference. 53 | ```python 54 | a_set = {1,2,3,4,5} 55 | b_set = {3,4,5,6,7} 56 | a_set.union(b_set) #output: {1,2,3,4,5,6,7} 57 | a_set.intersection(b_set) #output: {3,4,5} 58 | ``` 59 | >5. **Built-In Sequence Functions**\ 60 | Python has a handful of useful sequence functions that you should familiarize yourself with and use at any opportunity. 61 | >- `enumerate`: Returns an enumerate object. It contains the index and value of all the items in the sequence as pairs. 62 | ```python 63 | seq = [1,2,3,4,5] 64 | for i, value in enumerate(seq): 65 | print(i, value) 66 | ``` 67 | >- `sorted`: Returns a new sorted list from the elements of any sequence. 68 | ```python 69 | seq = [1,2,3,4,5] 70 | sorted(seq) #output: [1,2,3,4,5] 71 | ``` 72 | >- `zip`: “Pairs” up the elements of a number of lists, tuples, or other sequences to create a list of tuples. 73 | ```python 74 | seq1 = [1,2,3,4,5] 75 | seq2 = [6,7,8,9,10] 76 | zipped = zip(seq1, seq2) 77 | list(zipped) #output: [(1,6), (2,7), (3,8), (4,9), (5,10)] 78 | ``` 79 | >- `reversed`: Returns a sequence of the elements in the order of reverse of the argument sequence. 80 | ```python 81 | seq = [1,2,3,4,5] 82 | list(reversed(seq)) #output: [5,4,3,2,1] 83 | ``` 84 | >- `dict`: Converts a sequence of (key, value) pairs into a dictionary. 85 | ```python 86 | mapping = dict(zip(range(5), reversed(range(5)))) 87 | mapping #output: {0:4, 1:3, 2:2, 3:1, 4:0} 88 | ``` 89 | >- `list`: Converts a sequence or iterator into a list. 90 | ```python 91 | list(range(5)) #output: [0,1,2,3,4] 92 | ``` 93 | >- `all`: Returns True if all elements of the sequence are true (or if the sequence is empty). 94 | ```python 95 | all([True, 1, {3}]) #output: True 96 | all([True, 1, {}]) #output: False 97 | ``` 98 | >- `any`: Returns True if any element of the sequence is true. If the sequence is empty, returns False. 99 | ```python 100 | any([True, 1, {}]) #output: True 101 | any([False, 0, {}]) #output: False 102 | ``` 103 | >- `sum`: Sums the elements of an arbitrary sequence. 104 | ```python 105 | sum(range(5)) #output: 10 106 | ``` 107 | 6. **List, Set, and Dictionary Comprehensions** 108 | >- List comprehensions: A list comprehension is a way to quickly construct a list whose contents obey a simple rule. For example, suppose we want to create a list of squares: 109 | ```python 110 | squares = [] 111 | for x in range(10): 112 | squares.append(x**2) 113 | ``` 114 | >- A more concise way of doing this is: 115 | ```python 116 | squares = [x**2 for x in range(10)] 117 | ``` 118 | >- Set comprehensions: A set comprehension looks just like a list comprehension except you use curly braces instead of square brackets. 119 | ```python 120 | unique_lengths = {len(x) for x in strings} 121 | ``` 122 | >- Dictionary comprehensions: A dictionary comprehension looks like a set comprehension except that it uses curly braces containing a key: value pair instead of a single value. 123 | ```python 124 | loc_mapping = {val : index for index, val in enumerate(strings)} 125 | ``` 126 | >- Nested list comprehensions: You can nest list comprehensions, but the input and output sequence lengths will be the same. 127 | ```python 128 | all_data = [['John', 'Emily', 'Michael', 'Mary', 'Steven'], 129 | ['Maria', 'Juan', 'Javier', 'Natalia', 'Pilar']] 130 | names_of_interest = [] 131 | for names in all_data: 132 | enough_es = [name for name in names if name.count('e') >= 2] 133 | names_of_interest.extend(enough_es) 134 | ``` 135 | >- A more concise way of doing this is: 136 | ```python 137 | result = [name for names in all_data for name in names 138 | if name.count('e') >= 2] 139 | ``` 140 | 141 | ### **Functions** 142 | >1. **Defining and Calling Functions** 143 | >- Functions are defined using the `def` keyword. For example: 144 | ```python 145 | def my_function(x, y, z=1.5): 146 | if z > 1: 147 | return z * (x + y) 148 | else: 149 | return z / (x + y) 150 | ``` 151 | >- Functions can return multiple values as a tuple. For example: 152 | ```python 153 | def f(): 154 | a = 5 155 | b = 6 156 | c = 7 157 | return a, b, c 158 | ``` 159 | >- Functions can also be called using keyword arguments of the form `kwarg=value`. 160 | ```python 161 | def func(a, b=5, c=10): 162 | print('a is', a, 'and b is', b, 'and c is', c) 163 | func(3, 7) #output: a is 3 and b is 7 and c is 10 164 | func(25, c=24) #output: a is 25 and b is 5 and c is 24 165 | func(c=50, a=100) #output: a is 100 and b is 5 and c is 50 166 | ``` 167 | >- Functions can be passed to other functions. For example: 168 | ```python 169 | def add_numbers(x, y): 170 | return x + y 171 | def apply_to_one(f): 172 | """Calls the function f with 1 as its argument""" 173 | return f(1) 174 | my_addition = apply_to_one(add_numbers) 175 | ``` 176 | >- Functions can be defined inside other functions. For example: 177 | ```python 178 | def my_print(message="my default message"): 179 | print(message) 180 | def my_print(message="my default message"): 181 | def print_message(): 182 | print(message) 183 | print_message() 184 | ``` 185 | >- Functions can capture local state in the form of *closure*. For example: 186 | ```python 187 | def make_adder(n): 188 | """Returns a function that adds n to its argument""" 189 | def adder(k): 190 | return k + n 191 | return adder 192 | plus_3 = make_adder(3) 193 | plus_5 = make_adder(5) 194 | ``` 195 | #### ***Anonymous (Lambda) Functions*** 196 | >- Python has support for so-called anonymous or lambda functions, which are a way of writing functions consisting of a single statement, the result of which is the return value. They are defined using the `lambda` keyword, which has no meaning other than “we are declaring an anonymous function”. For example: 197 | ```python 198 | def short_function(x): 199 | return x * 2 200 | equiv_anon = lambda x: x * 2 201 | ``` 202 | >- Lambda functions can be used wherever function objects are required. For example: 203 | ```python 204 | def apply_to_one(f): 205 | """Calls the function f with 1 as its argument""" 206 | return f(1) 207 | my_double = lambda x: x * 2 208 | x = apply_to_one(my_double) 209 | ``` 210 | >- Lambda functions are particularly convenient in data analysis because there are many cases where the data transformation functions will be simple, one-line functions. For example: 211 | ```python 212 | strings = ['foo', 'card', 'bar', 'aaaa', 'abab'] 213 | strings.sort(key=lambda x: len(set(list(x)))) 214 | ``` 215 | >- The `key` argument to `sort` specifies a function that transforms each element before comparison. In this case, we are sorting strings by the number of distinct letters in each string. The `lambda` function passed to `sort` transforms each string into the number of distinct letters. The `set` function builds a collection of distinct letters, and `list` makes it into a list, so that `len` can be applied. 216 | 217 | #### ***Errors and Exception Handling*** 218 | >- Python has a number of built-in exceptions that are raised when your code encounters an error (something in the program goes wrong). For example, if you try to access a list element with an index that is out of bounds, you’ll get an `IndexError`: 219 | ```python 220 | x = [1, 2, 3] 221 | x[10] 222 | ``` 223 | >- If you try to open a file that doesn’t exist, you’ll get an `IOError`: 224 | ```python 225 | open('myfile.txt') 226 | ``` 227 | >- When these exceptions occur, they usually cause your program to crash. If you don’t handle the exception, your program will crash and you will see a message like: 228 | ```python 229 | Traceback (most recent call last): 230 | File "", line 1, in 231 | IndexError: list index out of range 232 | ``` 233 | >- Handling exceptions is one of the most important things that you can do in writing robust programs. If you don’t handle exceptions, your users will see ugly error messages and your program will crash whenever it encounters input that it doesn’t expect. Handling exceptions gracefully allows your program to continue running even if things go wrong. 234 | >- The `try` and `except` statements in Python are used to catch and handle exceptions. For example: 235 | ```python 236 | try: 237 | # use `pass` if you don't want any code to execute 238 | pass 239 | except Zero 240 | DivisionError: 241 | print("cannot divide by zero") 242 | except: 243 | print("something else went wrong") 244 | ``` 245 | >- The `try` statement works as follows. First, the try clause (the statement(s) between the try and except keywords) is executed. If no exception occurs, the except clause is skipped and execution of the try statement is finished. If an exception occurs during execution of the try clause, the rest of the clause is skipped. Then if its type matches the exception named after the except keyword, the except clause is executed, and then execution continues after the try statement. If an exception occurs which does not match the exception named in the except clause, it is passed on to outer try statements; if no handler is found, it is an unhandled exception and execution stops with a message as shown above. 246 | 247 | #### ***Files and the Operating System*** 248 | >- Python has a built-in `open` function for reading and writing files. The first argument is a string containing the filename. The second argument is another string containing a few characters describing the way in which the file will be used. mode can be 'r' when the file will only be read, 'w' for only writing (an existing file with the same name will be erased), and 'a' opens the file for appending; any data written to the file is automatically added to the end. 'r+' opens the file for both reading and writing. The mode argument is optional; 'r' will be assumed if it’s omitted. In addition, files can be opened in binary mode by appending 'b' to the mode argument. For example: 249 | ```python 250 | f = open('workfile', 'w') 251 | ``` 252 | >- When you’re done with a file, call `f.close()` to close it and free up any system resources taken up by the open file. It is good practice to use the `with` keyword when dealing with file objects. The advantage is that the file is properly closed after its suite finishes, even if an exception is raised at some point. Using `with` is also much shorter than writing equivalent `try-finally` blocks: 253 | ```python 254 | with open('workfile') as f: 255 | read_data = f.read() 256 | f.closed 257 | ``` 258 | >- If you’re not using the `with` keyword, then you should call `f.close()` to close the file and immediately free up any system resources used by it. If you don’t explicitly close a file, Python’s garbage collector will eventually destroy the object and close the open file for you, but the file may stay open for a while. Another risk is that different Python implementations will do this clean-up at different times. If you want to be sure the file is closed, then call `f.close()`. 259 | >- `f.read(size)` reads some quantity of data and returns it as a string (in text mode) or bytes object (in binary mode). size is an optional numeric argument. When size is omitted or negative, the entire contents of the file will be read and returned; it’s your problem if the file is twice as large as your machine’s memory. Otherwise, at most size bytes are read and returned. If the end of the file has been reached, `f.read()` will return an empty string (''). 260 | >- `f.readline()` reads a single line from the file; a newline character (`\n`) is left at the end of the string, and is only omitted on the last line of the file if the file doesn’t end in a newline. This makes the return value unambiguous; if `f.readline()` returns an empty string, the end of the file has been reached, while a blank line is represented by `'\n'`, a string containing only a single newline. `f.readlines()` reads the remaining lines from the file object and returns them as a list. The returned list is empty if the end of the file has already been reached. A common pattern when reading a file is to loop over the lines of the file, as in this example: 261 | ```python 262 | for line in f: 263 | print(line, end='') 264 | ``` 265 | >- `f.write(string)` writes the contents of string to the file, returning the number of characters written. 266 | >- `f.tell()` returns an integer giving the file object’s current position in the file represented as number of bytes from the beginning of the file when in binary mode and an opaque number when in text mode. (Use `f.seek(offset, from_what)` to change the file object’s position.) 267 | >- `f.seek(offset, from_what)` changes the file object’s position. The position is computed from adding offset to a reference point; the reference point is selected by the from_what argument. A from\_what value of 0 measures from the beginning of the file, 1 uses the current file position, and 2 uses the end of the file as the reference point. from\_what can be omitted and defaults to 0, using the beginning of the file as the reference point. 268 | >- `f.close()` closes the file. Like `file objects`, `open()` returns a file object, and is most commonly used with two arguments: `open(filename, mode)`. 269 | >- The `mode` argument is optional; `'r'` will be assumed if it’s omitted. It may be any of the following: 270 | `'r'` : open for reading (default)\ 271 | `'w'` : open for writing, truncating the file first\ 272 | `'x'` : create a new file and open it for writing\ 273 | `'a'` : open for writing, appending to the end of the file if it exists\ 274 | `'b'` : binary mode\ 275 | `'t'` : text mode (default)\ 276 | `'+'` : open a disk file for updating (reading and writing)\ 277 | `'U'` : universal newline mode (deprecated)\ 278 | >- The `open()` function returns a file object, and is most commonly used with two arguments: `open(filename, mode)`. 279 | 280 | #### ***Reading and Writing Files*** 281 | >- There is a method for reading or writing one line at a time. Each of these methods returns a string that corresponds to the line of text in the file, minus the newline character. If a file is opened in text mode, `'\n'` is automatically appended to the string when reading from the file. When writing to the file, `'\n'` characters are added to the string. This makes the file easier to read by humans, but if you want to manipulate the data in the file, you’ll have to remove the extra characters. 282 | ```python 283 | f = open('workfile', 'r') 284 | f.readline() 285 | f.readline() 286 | f.readline() 287 | f.close() 288 | ``` 289 | >- The `for` statement is more elegant and concise. It is also much faster than calling `f.readline()` repeatedly, because there is no function call overhead. 290 | ```python 291 | for line in f: 292 | print(line, end='') 293 | ``` 294 | >- If you want to read all the lines of a file in a list you can also use `list(f)` or `f.readlines()`. 295 | ```python 296 | list(f) 297 | f.readlines() 298 | ``` 299 | >- The `write()` method of file objects does not add line separators to the strings it writes. So if you want to write multiple lines you have to add the new line characters yourself. The easiest way to do this is by using the string `'\n'` as the line separator. 300 | ```python 301 | f = open('workfile', 'w') 302 | f.write('This is a test\n') 303 | f.write('This is a test\n') 304 | f.write('This is a test\n') 305 | f.write('This is a test\n') 306 | f.write('This is a test\n') 307 | f.write('This is a test\n') 308 | f.write('This is a test\n') 309 | f.write('This is a test\n') 310 | f.write('This is a test\n') 311 | f.write('This is a test\n') 312 | f.close() 313 | ``` 314 | >- If you don’t want to have to deal with the file’s `close()` method, you can use the `with` statement. This creates a temporary variable (often called `f`), which is only accessible in the indented block of the `with` statement. 315 | ```python 316 | with open('workfile') as f: 317 | read_data = f.read() 318 | f.closed 319 | ``` 320 | -------------------------------------------------------------------------------- /resources/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/.DS_Store -------------------------------------------------------------------------------- /resources/Day1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day1.png -------------------------------------------------------------------------------- /resources/Day10.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day10.png -------------------------------------------------------------------------------- /resources/Day11a.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day11a.png -------------------------------------------------------------------------------- /resources/Day11b.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day11b.png -------------------------------------------------------------------------------- /resources/Day12.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day12.png -------------------------------------------------------------------------------- /resources/Day13.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day13.png -------------------------------------------------------------------------------- /resources/Day15.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day15.png -------------------------------------------------------------------------------- /resources/Day16.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day16.png -------------------------------------------------------------------------------- /resources/Day17.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day17.png -------------------------------------------------------------------------------- /resources/Day18.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day18.png -------------------------------------------------------------------------------- /resources/Day19.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day19.png -------------------------------------------------------------------------------- /resources/Day2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day2.png -------------------------------------------------------------------------------- /resources/Day20.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day20.png -------------------------------------------------------------------------------- /resources/Day21.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day21.png -------------------------------------------------------------------------------- /resources/Day22.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day22.png -------------------------------------------------------------------------------- /resources/Day24.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day24.png -------------------------------------------------------------------------------- /resources/Day26.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day26.png -------------------------------------------------------------------------------- /resources/Day27.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day27.png -------------------------------------------------------------------------------- /resources/Day28.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day28.jpeg -------------------------------------------------------------------------------- /resources/Day29.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day29.jpeg -------------------------------------------------------------------------------- /resources/Day3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day3.png -------------------------------------------------------------------------------- /resources/Day30.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day30.jpeg -------------------------------------------------------------------------------- /resources/Day31.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day31.jpeg -------------------------------------------------------------------------------- /resources/Day32.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day32.jpeg -------------------------------------------------------------------------------- /resources/Day33.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day33.png -------------------------------------------------------------------------------- /resources/Day34.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day34.jpeg -------------------------------------------------------------------------------- /resources/Day35.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day35.jpeg -------------------------------------------------------------------------------- /resources/Day36a.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day36a.jpeg -------------------------------------------------------------------------------- /resources/Day36b.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day36b.jpeg -------------------------------------------------------------------------------- /resources/Day37a.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day37a.jpeg -------------------------------------------------------------------------------- /resources/Day37b.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day37b.jpeg -------------------------------------------------------------------------------- /resources/Day37c.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day37c.jpeg -------------------------------------------------------------------------------- /resources/Day37d.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day37d.jpeg -------------------------------------------------------------------------------- /resources/Day37e.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day37e.jpeg -------------------------------------------------------------------------------- /resources/Day37f.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day37f.jpeg -------------------------------------------------------------------------------- /resources/Day37g.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day37g.jpeg -------------------------------------------------------------------------------- /resources/Day37h.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day37h.jpeg -------------------------------------------------------------------------------- /resources/Day37i.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day37i.jpeg -------------------------------------------------------------------------------- /resources/Day37j.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day37j.jpeg -------------------------------------------------------------------------------- /resources/Day37k.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day37k.jpeg -------------------------------------------------------------------------------- /resources/Day37l.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day37l.jpeg -------------------------------------------------------------------------------- /resources/Day37m.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day37m.jpeg -------------------------------------------------------------------------------- /resources/Day37n.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day37n.jpeg -------------------------------------------------------------------------------- /resources/Day38.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day38.jpeg -------------------------------------------------------------------------------- /resources/Day38a.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day38a.gif -------------------------------------------------------------------------------- /resources/Day39.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day39.jpeg -------------------------------------------------------------------------------- /resources/Day3_live.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day3_live.png -------------------------------------------------------------------------------- /resources/Day4.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day4.png -------------------------------------------------------------------------------- /resources/Day40.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day40.jpeg -------------------------------------------------------------------------------- /resources/Day5.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day5.png -------------------------------------------------------------------------------- /resources/Day5a.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day5a.png -------------------------------------------------------------------------------- /resources/Day6.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day6.png -------------------------------------------------------------------------------- /resources/Day7.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day7.png -------------------------------------------------------------------------------- /resources/Day8.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day8.png -------------------------------------------------------------------------------- /resources/Day9.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/Day9.png -------------------------------------------------------------------------------- /resources/PKC2023.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/PKC2023.png -------------------------------------------------------------------------------- /resources/aammar.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/aammar.jpeg -------------------------------------------------------------------------------- /resources/chatGPT.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/chatGPT.png -------------------------------------------------------------------------------- /resources/cheat_sheets/Day10.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/cheat_sheets/Day10.png -------------------------------------------------------------------------------- /resources/cheat_sheets/Pandas_Cheat_Sheet_datWrangling.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/cheat_sheets/Pandas_Cheat_Sheet_datWrangling.pdf -------------------------------------------------------------------------------- /resources/cheat_sheets/matplotlib_all_cheatsheets.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/cheat_sheets/matplotlib_all_cheatsheets.pdf -------------------------------------------------------------------------------- /resources/cheat_sheets/matplotlib_cheatsheet_2.webp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/cheat_sheets/matplotlib_cheatsheet_2.webp -------------------------------------------------------------------------------- /resources/cheat_sheets/matplotlib_handout-beginner.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/cheat_sheets/matplotlib_handout-beginner.pdf -------------------------------------------------------------------------------- /resources/cheat_sheets/matplotlib_handout-intermediate.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/cheat_sheets/matplotlib_handout-intermediate.pdf -------------------------------------------------------------------------------- /resources/cheat_sheets/matplotlib_handout-tips.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/cheat_sheets/matplotlib_handout-tips.pdf -------------------------------------------------------------------------------- /resources/cheat_sheets/pandas_cheatsheet.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/cheat_sheets/pandas_cheatsheet.pdf -------------------------------------------------------------------------------- /resources/feedback.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/feedback.png -------------------------------------------------------------------------------- /resources/first_line.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/first_line.png -------------------------------------------------------------------------------- /resources/installation.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/installation.png -------------------------------------------------------------------------------- /resources/ml_day22_23/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/ml_day22_23/.DS_Store -------------------------------------------------------------------------------- /resources/ml_day22_23/26.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/ml_day22_23/26.png -------------------------------------------------------------------------------- /resources/ml_day22_23/27.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/ml_day22_23/27.png -------------------------------------------------------------------------------- /resources/ml_day22_23/28.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/ml_day22_23/28.png -------------------------------------------------------------------------------- /resources/ml_day22_23/29.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/ml_day22_23/29.png -------------------------------------------------------------------------------- /resources/ml_day22_23/30.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/ml_day22_23/30.png -------------------------------------------------------------------------------- /resources/ml_day22_23/32.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/ml_day22_23/32.png -------------------------------------------------------------------------------- /resources/ml_day22_23/33.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/ml_day22_23/33.png -------------------------------------------------------------------------------- /resources/ml_day22_23/34.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/ml_day22_23/34.png -------------------------------------------------------------------------------- /resources/ml_day22_23/35.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/ml_day22_23/35.png -------------------------------------------------------------------------------- /resources/ml_day22_23/36.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/ml_day22_23/36.png -------------------------------------------------------------------------------- /resources/ml_day22_23/37.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/ml_day22_23/37.png -------------------------------------------------------------------------------- /resources/ml_day22_23/38.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/ml_day22_23/38.png -------------------------------------------------------------------------------- /resources/ml_day22_23/39.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/ml_day22_23/39.png -------------------------------------------------------------------------------- /resources/ml_day22_23/40.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/ml_day22_23/40.png -------------------------------------------------------------------------------- /resources/ml_day22_23/41.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/ml_day22_23/41.png -------------------------------------------------------------------------------- /resources/ml_day22_23/42.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/ml_day22_23/42.png -------------------------------------------------------------------------------- /resources/ml_day22_23/43.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/ml_day22_23/43.png -------------------------------------------------------------------------------- /resources/poster_2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/poster_2.png -------------------------------------------------------------------------------- /resources/snipping1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/snipping1.png -------------------------------------------------------------------------------- /resources/snipping2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/snipping2.png -------------------------------------------------------------------------------- /resources/snipping3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kousarraza/python_ka_chilla2023/a7de0a8df34328634dcdfa02e7b6f2c2d46c9445/resources/snipping3.png --------------------------------------------------------------------------------