├── images ├── dl.jpg ├── dl.png ├── la.png ├── ml.jpg ├── py.png ├── algo.png ├── data.jpg ├── funml.jpg └── mlcourseai.jpg ├── LICENSE └── README.md /images/dl.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/datasciencescoop/Data-Science-Free/HEAD/images/dl.jpg -------------------------------------------------------------------------------- /images/dl.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/datasciencescoop/Data-Science-Free/HEAD/images/dl.png -------------------------------------------------------------------------------- /images/la.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/datasciencescoop/Data-Science-Free/HEAD/images/la.png -------------------------------------------------------------------------------- /images/ml.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/datasciencescoop/Data-Science-Free/HEAD/images/ml.jpg -------------------------------------------------------------------------------- /images/py.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/datasciencescoop/Data-Science-Free/HEAD/images/py.png -------------------------------------------------------------------------------- /images/algo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/datasciencescoop/Data-Science-Free/HEAD/images/algo.png -------------------------------------------------------------------------------- /images/data.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/datasciencescoop/Data-Science-Free/HEAD/images/data.jpg -------------------------------------------------------------------------------- /images/funml.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/datasciencescoop/Data-Science-Free/HEAD/images/funml.jpg -------------------------------------------------------------------------------- /images/mlcourseai.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/datasciencescoop/Data-Science-Free/HEAD/images/mlcourseai.jpg -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 |

2 | Master 3 |

4 | 5 |

A collection of awesome manuals, blogs, cheats, resources and more.

6 | 7 |
8 | 9 |

10 | 11 | Branch 12 | 13 | 14 | 15 | License 16 | 17 | 18 | 19 | awesome 20 | 21 | 22 | 23 | Stargazers 24 | 25 |

26 | 27 |

28 | Created by Shubham Kumar 29 |

30 |
31 | 32 | # Show your support by giving :octocat: a :star: 33 | **DISCLAIMER:** ***This is for absolute beginners, no experience needed.*** 34 | 35 | ## Table of Contents 36 | 37 | * [Python](#python) 38 | * [Pandas](#pandas) 39 | * [Algorithms in Python](#algorithms-in-python) 40 | * [Practice Python](#practice-python) 41 | * [Calculus](#calculus) 42 | * [Linear Algebra](#linear-algebra) 43 | * [Algorithms in Python](#algorithms-in-python) 44 | * [Probability & Stats](#probability-and-statistics) 45 | * [Statistical Learning](#statistical-learning) 46 | * [Machine Learning](#machine-learning) 47 | * [Practice ML](#practice-ml) 48 | * [Deep Learning](#deep-learning) 49 | * [Deep Learning Projects](#deep-learning-projects) 50 | * [Medium Blog Posts](#AWESOME-BLOG-POSTS-ON-MEDIUM) 51 | * [Playgrounds](#playgrounds) 52 | * [Research Papers](#research-papers) 53 | * [Interview Questions](#interview-questions) 54 | * [Top Rated Courses](#top-rated-courses) 55 | * [Blogs to Follow](#blogs-to-follow) 56 | 57 | 58 | ## PYTHON 59 |

60 | py 61 |

62 | 63 | * **Introduction to Python 3** [Sentdex](https://www.youtube.com/watch?v=eXBD2bB9-RA&list=PLQVvvaa0QuDeAams7fkdcwOGBpGdHpXln) 64 | 65 | * **Quantative Economics with Python** [Here](https://lectures.quantecon.org/py/) 66 | 67 | * **Python Data science handbook** [Chapter 1-4](https://github.com/jakevdp/PythonDataScienceHandbook/blob/8a34a4f653bdbdc01415a94dc20d4e9b97438965/notebooks/Index.ipynb) 68 | 69 | * **Python for Data Analysis** [2nd Edition](https://github.com/wesm/pydata-book) 70 | 71 | * **Learning Python 5th edition, oreilly publication by** [Mark Lutz](https://drive.google.com/file/d/14ayuNFEo9VZ-xstgXlyMgDFHoAgrGqJU/view?usp=sharing) **(LONG VERSION)** 72 | 73 | * **Python by** [Scipy](https://scipython.com/book/) 74 | 75 | * **Udemy Complete Python Bootcamp by Jose Portilla** [Google Drive](https://drive.google.com/drive/folders/0ByWO0aO1eI_MaExzRWZ2S0dndjQ?usp=sharing) 76 | 77 | * **Udacity - Developing scalable app in Python** [Google Drive](https://drive.google.com/open?id=0ByWO0aO1eI_MT1E1NW91VlJ2TVk) 78 | 79 | * **Python Ebooks on** [Google Drive](https://drive.google.com/open?id=0ByWO0aO1eI_MZ19fbVV3YS1hckk) 80 | 81 | ## PANDAS 82 | 83 | * **Pandas & Data Analysis by [mlcourse.ai](https://www.youtube.com/watch?v=fwWCw_cE5aI&feature=youtu.be)** (Video) 84 | 85 | * **Pandas by** [Pydata](https://pandas.pydata.org/pandas-docs/stable/getting_started/10min.html) 86 | 87 | * **Effective Pandas** [GitHub](https://github.com/TomAugspurger/effective-pandas) 88 | 89 | * **Pandas** [Cheatsheets](https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf) 90 | 91 | * **Pandas Data School** [Videos](https://www.youtube.com/watch?v=yzIMircGU5I&list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y) 92 | 93 | ## ALGORITHMS IN PYTHON 94 | 95 | ![algo](images/algo.png) 96 | 97 | * **Python & Algorithms 2.0 by Marina Wahl** [Pdf](https://drive.google.com/file/d/1rR0GSEAqVtFDWRDdK1NLEJvc8oKC94PT/view?usp=sharing) 98 | 99 | * **Udacity** [Course](https://eu.udacity.com/course/data-structures-and-algorithms-in-python--ud513) 100 | 101 | * **Another good resource** [Here](http://interactivepython.org/runestone/static/pythonds/index.html) 102 | 103 | * **Visualising algorithms through animation** [Visit](https://visualgo.net/en) 104 | 105 | ## PRACTICE PYTHON 106 | 107 | * [Project based](https://github.com/tuvtran/project-based-learning#python) 108 | 109 | * [Project Euler](https://projecteuler.net/) 110 | 111 | ## CALCULUS 112 | 113 | * **Essence of Calculus** [3Blue1Brown PlayList](https://www.youtube.com/watch?v=WUvTyaaNkzM&list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr) 114 | 115 | * **Khan Academy calculus-1** [Here](https://www.khanacademy.org/math/calculus-1) 116 | 117 | * **Khan Academy calculus-2** [Here](https://www.khanacademy.org/math/calculus-2) 118 | 119 | * **Khan Academy multivariable calculus** [Here](https://www.khanacademy.org/math/multivariable-calculus) 120 | 121 | ## LINEAR ALGEBRA 122 |

123 | la 124 |

125 | 126 | * **Manga Guide to Linear Algebre** [Google Drive](https://drive.google.com/file/d/1sdnIBqPjSgPzitrInV0roHTEJ856ntYe/view?usp=sharing) 127 | 128 | * **3Blue1Brown** [PlayList](https://www.youtube.com/watch?v=fNk_zzaMoSs&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) 129 | 130 | * **Khan Academy** [Linear algebra](https://www.khanacademy.org/math/linear-algebra) 131 | 132 | * **UBC Maths by James B. Carrell** [Here](https://www.math.ubc.ca/~carrell/NB.pdf) 133 | 134 | ## PROBABILITY AND STATISTICS 135 | 136 | * **Khan Academy** [Course](https://www.khanacademy.org/math/statistics-probability) 137 | 138 | * **Think Stats** [Pdf](http://greenteapress.com/thinkstats/thinkstats.pdf) 139 | 140 | * **Probability** [Cheat sheet](http://www.wzchen.com/probability-cheatsheet/) 141 | 142 | * **Bayesian-Methods-for-Hackers** [Here](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/) 143 | 144 | ## STATISTICAL LEARNING 145 | 146 | * **An Introduction to Statistical Learning** [Essential](https://www-bcf.usc.edu/~gareth/ISL/index.html) 147 | 148 | * **Elements of Statistical Learning Stanford** [Extremely useful](https://web.stanford.edu/~hastie/ElemStatLearn/) 149 | 150 | ## MACHINE LEARNING 151 |

152 | ml 153 |

154 | 155 | * **Hands on Machine Learning with Tensorflow & Scikit-learn** [Google Drive](https://drive.google.com/file/d/1CHv8CTQRRaoSDeBGN0_tkvd0D2E9mYxo/view?usp=sharing) 156 | 157 | * **Practical Machine Learning Tutorial with Python** [Sentdex](https://www.youtube.com/watch?v=OGxgnH8y2NM&list=PLQVvvaa0QuDfKTOs3Keq_kaG2P55YRn5v) 158 | 159 | * **Stanford - AndrewNg Course** [YouTube](https://www.youtube.com/watch?v=PPLop4L2eGk&list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN) 160 | 161 | * **Udemy - Machine Learning A-Z using Python & R by SuperDataScience Team** [Here](https://drive.google.com/open?id=1mTUyna5oynW4RVItOldP2f2yhe_3xL4s) 162 | 163 | * **Jason Mayes (Google Engineer ML Class 101)** [Slides](https://docs.google.com/presentation/d/1kSuQyW5DTnkVaZEjGYCkfOxvzCqGEFzWBy4e9Uedd9k/mobilepresent?slide=id.g168a3288f7_0_58) 164 | 165 | * **Ebooks for ML on** [Google Drive](https://drive.google.com/drive/folders/0ByWO0aO1eI_Md1JGZW9NSDFpQ1U?usp=sharing) 166 | 167 | * **More Ebooks on** [Google Drive](https://drive.google.com/open?id=1gmW2K_VAQrXPWAfgQcg-3umt5ipV7-G9) 168 | 169 | * **Manning publication books on** [Google Drive](https://drive.google.com/open?id=1yXZ1HgyQ7yPUJI8cC7ZnTWi0BAbxq_Kg) 170 | 171 | * **Cheat Sheets for ML, DeepL, AI** [Google Drive](https://drive.google.com/open?id=1qYgzm4oTVYIR_iEsT4ZW9l9o3GUMSzYG) 172 | 173 | * **Google Machine Learning crash course using Tensorflow (Not for Beginners)** [Here](https://developers.google.com/machine-learning/crash-course/) 174 | 175 | * **Reinforcement Learning Book by Andrew Barto and Richard S. Sutton** [Google Drive](https://drive.google.com/file/d/1OFquNBwdPxFFFbxCixBDHYUEmnbimegH/view?usp=sharing) 176 | 177 | * **Stanford's CS 229 Machine Learning** [VIP Cheatsheet](https://github.com/afshinea/stanford-cs-229-machine-learning) 178 | 179 | ## PRACTICE ML 180 | 181 | * **Home for Data Science -** [Kaggle](https://www.kaggle.com/) 182 | 183 | ## DEEP LEARNING 184 |

185 | dl 186 |

187 | 188 | * **Grokking Deep Learning by Andrew Trask** [Pdf](https://drive.google.com/file/d/1gLbOH2AbiaStYGmLV6oivTgU6eraqlW2/view?usp=sharing) 189 | 190 | * **Practical Deep Learning for Coders** [Fast-ai](https://course.fast.ai/) 191 | 192 | * **MIT Deep Learning** [Lex-Fridman](https://www.youtube.com/watch?v=O5xeyoRL95U&list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf) 193 | 194 | * **MIT 6.S191: Introduction to Deep Learning** [Alexander-Amini](https://www.youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI) 195 | 196 | * **Demystifying RL** [Intel AI](https://www.intel.ai/demystifying-deep-reinforcement-learning/#gs.0tu98m) 197 | 198 | * **Deep RL Bootcamp** [Berkeley CA](https://sites.google.com/view/deep-rl-bootcamp/lectures) 199 | 200 | ## DEEP LEARNING PROJECTS 201 | 202 | * **Python Deep Learning Projects Packt** [Packt Pdf](https://drive.google.com/file/d/1f7wsE-Aui2nYhG12Ti6-L8UMtSUsNvFu/view?usp=sharing) 203 | 204 | * **Implement a Pong-playing agent** [Pong from Pixels](http://karpathy.github.io/2016/05/31/rl/) 205 | 206 | ## AWESOME BLOG POSTS ON MEDIUM 207 | 208 | * **Simple Reinforcement Learning with Tensorflow series by** [Arthur Juliani](https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0) 209 | 210 | * **Machine Learning for Humans by** [Vishal Maini](https://medium.com/machine-learning-for-humans/why-machine-learning-matters-6164faf1df12) 211 | 212 | ## PLAYGROUNDS 213 | 214 | * **Tensorflow [Playground](https://playground.tensorflow.org/)** 215 | 216 | * **Machine Learning [Playground](http://ml-playground.com/)** 217 | 218 |
219 | 220 | ## RESEARCH PAPERS 221 | 222 | * **[PAPERS WITH CODE](https://paperswithcode.com/)** 223 | 224 | ## INTERVIEW QUESTIONS 225 | 226 | * **100 Data Science Interview Questions and** [Answers](https://www.dezyre.com/article/100-data-science-interview-questions-and-answers-general-for-2018/184) 227 | 228 | * **40 Interview Questions asked at startups in Machine Learning** [Here](https://www.analyticsvidhya.com/blog/2016/09/40-interview-questions-asked-at-startups-in-machine-learning-data-science/) 229 | 230 | * **Top 100 Data science interview** [Questions](http://nitin-panwar.github.io/Top-100-Data-science-interview-questions/?utm_campaign=News&utm_medium=Community&utm_source=DataCamp.com) 231 | 232 | * **109 Data Science Interview Questions and Answers for 2019 on** [Springboard](https://www.springboard.com/blog/data-science-interview-questions/) 233 | 234 | * **111 Data Science Interview Questions with Detailed Answers** [Here](https://rpubs.com/JDAHAN/172473) 235 | 236 | ## TOP RATED COURSES 237 | 238 | ![mlcourse.ai](images/mlcourseai.jpg) 239 | 240 | * **Open Machine Learning Course [mlcourse.ai](https://mlcourse.ai/)** 241 | 242 | * **UC Berkeley CS294-112 [Deep Reinforcement Learning](http://rail.eecs.berkeley.edu/deeprlcourse/)** 243 | 244 | * **UCL Course on [RL](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html)** 245 | 246 | ## Blogs to Follow 247 | 248 | ![funml](images/funml.jpg) 249 | 250 | * **Machine Learning for Everyone** [Blog](https://vas3k.com/blog/machine_learning/) 251 | 252 | * **[KDNuggets](https://www.kdnuggets.com/)** 253 | 254 | * **[Data Science Plus](https://datascienceplus.com/)** 255 | 256 | * **[Analytics Vidhya](https://www.analyticsvidhya.com/)** 257 | 258 | * **[Towards Data Science](https://towardsdatascience.com/)** 259 | 260 |
261 |
262 | 263 | >You take the blue pill, the story ends. You wake up in your 264 | >bed and believe whatever you want to believe. You take the 265 | >red pill, you stay in Wonderland, and I show you how deep 266 | >the rabbit hole goes.” — Morpheus 267 | 268 |
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