└── ReadMe.md /ReadMe.md: -------------------------------------------------------------------------------- 1 | ## **A-Z Guide to Multivariate Calculus for Machine Learning πŸš€** 2 | 3 |

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6 | Welcome to the Multivariate Calculus for Machine Learning repository! This A-Z guide explores the w β€” specifically designed to empoweorld of multivariate calculus through interactive tutorials, hands-on code, and easy-to-follow videosr machine learning enthusiasts. 7 | 8 | πŸ”— Visit us at: coursesteach.com 9 | 10 | ## **OverviewπŸ‘‹πŸ›’** 11 | 12 | The A-Z Guide to Machine Learning is a comprehensive resource designed to cater to both beginners and experienced practitioners in the field of Machine Learning. Whether you're just starting your journey into ML or seeking to deepen your understanding and refine your skills, this repository has something for everyone. 13 | 14 |

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17 | 18 | ## **FeaturesπŸ‘‹πŸ›’** 19 | 20 | Extensive Algorithm Coverage: Explore a wide range of ML algorithms, including but not limited to linear regression, decision trees, support vector machines, neural networks, clustering techniques, and more. 21 | 22 | **1- Hands-On Implementations:** Dive into practical implementations of these algorithms in Python, alongside explanations and insights into their workings. 23 | 24 | **2- Code Examples and Jupyter Notebooks**: Access code examples and Jupyter notebooks that provide step-by-step guidance, making it easier to grasp complex concepts and experiment with different techniques. 25 | 26 | **3- Supplementary Resources**: Discover additional resources, such as articles, tutorials, and datasets, to supplement your learning and enhance your understanding of Machine Learning principles and applications. 27 | 28 | **4- Contents** 29 | Algorithms: Implementation examples of various ML algorithms, organized for easy navigation and reference. 30 | 31 | **5- Techniques:** Practical demonstrations of ML techniques, such as feature engineering, model evaluation, hyperparameter tuning, and more. 32 | 33 | ## **ContributingπŸ™Œ** 34 | We believe that the most effective learning and growth happen when people come together to exchange knowledge and ideas. Whether you're an experienced professional or just beginning your machine learning journey, your input can be valuable to the community. 35 | We welcome contributions from the community! Whether it's fixing a bug, adding a new algorithm implementation, or improving documentation, your contributions are valuable. Please contact on my **skype ID: themushtaq48** for guidelines on how to contribute. 36 | 37 | ## πŸ“‹ Prerequisites 38 | 39 | - Introduction of Python (Variable, Loop etc) 40 | - Basic Probability Theory (Expectations and Distributions) 41 | - Multivariate Calculus 42 | 43 | ## Why Contribute? 44 | 45 | 1- **Share Your Expertise**: If you have knowledge or insights in machine learning or TinyML, your contributions can assist others in learning and applying these concepts. 46 | 47 | 2-**Enhance Your Skills**: Contributing to this project offers a great opportunity to deepen your understanding of machine learning systems. Writing, coding, or reviewing content will reinforce your knowledge while uncovering new areas of the field. 48 | 49 | 3- **Collaborate and Connect**: Join a community of like-minded individuals committed to advancing AI education. Work with peers, receive feedback, and build connections that may open up new opportunities. 50 | 51 | 4- **Make a Difference**: Your contributions can shape how others learn and engage with machine learning. By refining and expanding content, you help shape the education of future engineers and AI experts. 52 | 53 | ## **πŸ’‘ How to Participate?** 54 | 55 | πŸš€ Fork & Star this repository 56 | 57 | πŸ‘©β€πŸ’» Explore and Learn from structured lessons 58 | 59 | πŸ”§ Enhance the current blog or code, or write a blog on a new topic 60 | 61 | πŸ”§ Implement & Experiment with provided code 62 | 63 | πŸ”§Convert lessons into interactive Colab notebooks 64 | 65 | 🀝 Collaborate with fellow ML enthusiasts 66 | 67 | πŸ”§ Add new tutorials 68 | 69 | πŸ”§ Add quizzes or solutions 70 | 71 | πŸ”§ Create blog from next topic in our jounrney 72 | 73 | πŸ”§ suggestion other important website ,repistory,youtube Channel etc 74 | 75 | πŸ“Œ Contribute your own implementations & projects 76 | 77 | πŸ“Œ Share valuable blogs, videos, courses, GitHub repositories, and research websites 78 | 79 | 80 | ## **🌍 Join Our Community** 81 | 82 | πŸ”— [**YouTube Channe**l](https://www.youtube.com/@coursesteach-mv5si/videos) 83 | 84 | πŸ”— [**SubStack Blogs**](https://substack.com/@coursesteach) 85 | 86 | πŸ”— [**Facebook**](https://www.facebook.com/CourseTeach) 87 | 88 | πŸ”— [**LinkedIn**](https://www.linkedin.com/company/90909828/admin/page-posts/published/) 89 | 90 | πŸ”— [**Gumroad**](https://gumroad.com/products/antows/edit) 91 | 92 | 93 | πŸ“¬ Need Help? Connect with us on [**WhatsApp**](https://chat.whatsapp.com/L9URPRThBEa7GFl0mlwggg) 94 | 95 | πŸ™ Special thanks πŸ™ to our Virtual University of Pakistan students, reviewers, and content contributors, notably Dr Said Nabi 96 | 97 | Star this repo if you find it useful ⭐ 98 | 99 | Also please subscribe to my [youtube channel!](https://www.youtube.com/@coursesteach-mv5si) 100 | 101 | [Machine Learning-gumroad](https://mushtaqmind.gumroad.com/) 102 | 103 | ## πŸ“¬ Stay Updated with Weekly Machine Learning Lessons! 104 | 105 | Never miss a tutorial! Get weekly insights, updates, and bonus content straight to your inbox. 106 | **Join hundreds of Machine Learning learners on Substack.** 107 | 108 | πŸ‘‰ [**Subscribe to Our Machine Learning Newsletter**](https://substack.com/@coursesteach) ✨ 109 | 110 | πŸ’‘ Optional Badge (to make it pop) 111 | 112 | [![Subscribe on Substack](https://img.shields.io/badge/Subscribe-Substack-orange?style=for-the-badge&logo=substack)](https://substack.com/@coursesteach) 113 | 114 | 115 | 116 |
117 |

Course 01 - βš™οΈMultivariate Calculus for Machine Learning

118 | 119 | ## πŸ“šChapter: 1 - **Basics Function, Gradients and Derivatives,Time saving rule** 120 | 121 | | Topic Name/Tutorial | Video | Code| 122 | |---|---|---| 123 | |[**🌐1-Calculus for Machine Learning: Building Blocks for Data Science**](https://medium.com/@Coursesteach/multivariate-calculus-for-machine-learning-part-1-d35586a6eee8) | Content 2 | Content 3 | 124 | |[**🌐2- Introduction to Functions**](https://medium.com/@Coursesteach/multivariate-calculus-for-machine-learning-part-2-e3945f87c43) |[**Video**](https://drive.google.com/file/d/1ofuknBJQb26qeDE4UQn85aZ8182-SZaj/view)[**-Video2**](https://drive.google.com/file/d/1ZCkgqeaOcUbz1r4N_WuWvUX6DzEyNgIX/view) | Content 6 | 125 | |[**🌐3-How Calculus is useful**](https://medium.com/@Coursesteach/multivariate-calculus-for-machine-learning-part-3-b53a8f9f4833)|[**Video1**](https://drive.google.com/file/d/11xev97TRlFAll5kdgBHRs8OvNEsjUIYa/view)|---| 126 | |[**🌐4-Understanding Derivative in Machine Learning: A Key Concept for Algorithm Optimization**](https://medium.com/@Coursesteach/multivariate-calculus-for-machine-learning-part-5-84d9a9cad9ad)|[**Video1**](https://drive.google.com/file/d/1Pw5sGObxavlRimObBrwhk2NrYJcOAtAt/view)[**-Video2**](https://drive.google.com/file/d/1agLgVt0VuFA_knJVgM9bJu6JzvS2qIyL/view)|---| 127 | |[**🌐5-Differentiation examples & special cases**](https://medium.com/@Coursesteach/multivariate-calculus-for-machine-learning-part-5-differentiation-examples-special-cases-62d1ebb003ed)|[**Video1**](https://drive.google.com/file/d/19b9Ou-E_8Fa8YvYlrG913Vu7B-ZGXTnV/view)| 128 | |[**🌐6-Product rule**](https://medium.com/@Coursesteach/multivariate-calculus-for-machine-learning-part-6-product-rule-b4d45a239dd2)|[**Video**](https://drive.google.com/file/d/1GvOQRHuP9zqi5zC0xqx_Rqao5C8QjWxt/view)|---| 129 | |[**🌐7-Chain rule**](https://medium.com/@Coursesteach/multivariate-calculus-for-machine-learning-part-7-chain-rule-0720e0e8ca11)|[**Video**](https://drive.google.com/file/d/1SjRlBKMftjgeksaYRpb6r-F4ItFHLdsC/view)[**-Video2**](https://drive.google.com/file/d/1prhlkKE3D5U3nkTWHb5ROMDQsQWWR0db/view)[**-Video3**](https://drive.google.com/file/d/1ShgeXZxCbJHdkzNAfHSToBnD-FSe-aLH/view)|---| 130 | |[**🌐8-Taming a beast**](https://medium.com/@Coursesteach/multivariate-calculus-for-machine-learning-part-8-taming-a-beast-e8f673b254b1)|[**1**](https://drive.google.com/file/d/1hi-GcsaQumLmwemxrJZoNZMf5VYFADnC/view)|---| 131 |
132 | 133 | 134 | 135 |
136 |

πŸ“• Multivariate Calculus Resources

137 | 138 | ## πŸ‘οΈ Chapter 1: - **Free Courses** 139 | | No. | Title/Link | Description | Reading Status | University / Platform | Feedback | 140 | |-----|------------|-------------|----------------|------------------------|----------| 141 | | 1 | [**Learn Calculus by Coding in Python**](https://www.freecodecamp.org/news/learn-college-calculus-and-implement-with-python/) | By Beau Carnes, Coursera | In Progress | freeCodeCamp | ⭐️⭐️⭐️⭐️ | 142 | | 2 | [**Machine Learning**](https://techdevguide.withgoogle.com/paths/machine-learning/) | A free course from Google | Pending | Google | | 143 | | 3 | [**Machine Learning from Scratch - Python**](https://www.youtube.com/playlist?list=PLqnslRFeH2Upcrywf-u2etjdxxkL8nl7E) | By Patrick Loeber (YouTube) | Pending | YouTube | | 144 | | 4 | [**Machine Learning Zoomcamp**](https://github.com/DataTalksClub/machine-learning-zoomcamp) | A free 4-month course on ML engineering | Pending | DataTalks.Club | | 145 | | 5 | [**Stanford CS229: Machine Learning**](https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU) | Full course taught by Andrew Ng | Pending | Stanford | | 146 | | 6 | [**Google Machine Learning Education**](https://developers.google.com/machine-learning) | Google's dedicated ML learning hub | Pending | Google | | 147 | | 7 | [**StatQuest: Machine Learning**](https://www.youtube.com/watch?v=Gv9_4yMHFhI&list=PLblh5JKOoLUICTaGLRoHQDuF_7q2GfuJF&index=2) | Easy-to-understand ML explained with stats | Pending | StatQuest (YouTube) | | 148 | | 8 | [**PreCalculus - Math for ML**](https://www.youtube.com/playlist?list=PLHXZ9OQGMqxcFN7BoQsgCyS9Wh0JPwttc) | By Dr. Trefor Bazett (Great math fundamentals) | Pending | YouTube | | 149 | | 9 | [**Machine Learning with Graphs**](https://www.youtube.com/playlist?list=PLoROMvodv4rPLKxIpqhjhPgdQy7imNkDn) | Covers GNNs and graph-based ML | Pending | Stanford | | 150 | | 10 | [**MIT RES.LL-005 Mathematics of Big Data and ML**](https://www.youtube.com/playlist?list=PLUl4u3cNGP62uI_DWNdWoIMsgPcLGOx-V) | In-depth mathematical foundations | Pending | MIT | | 151 | | 11 | [**CS294-158 Deep Unsupervised Learning SP19**](https://www.mrdbourke.com/2020-machine-learning-roadmap/) | Covers deep learning and generative models | Pending | UC Berkeley | | 152 | | 12 | [**Introduction to Machine Learning**](https://www.youtube.com/playlist?list=PL05umP7R6ij35ShKLDqccJSDntugY4FQT) | By Dmitry (University of TΓΌbingen) | Pending | University of TΓΌbingen | | 153 | | 13 | [**Statistical Machine Learning - 2020**](https://www.youtube.com/playlist?list=PL05umP7R6ij2XCvrRzLokX6EoHWaGA2cC) | By Ulrike von Luxburg | Pending | University of TΓΌbingen | | 154 | | 14 | [**Probabilistic Machine Learning - 2020**](https://www.youtube.com/playlist?list=PL05umP7R6ij1tHaOFY96m5uX3J21a6yNd) | By Philipp Hennig | Pending | University of TΓΌbingen | | 155 | | 15 | [**Machine Learning Concepts**](https://inria.github.io/scikit-learn-mooc/toc.html) |github websit it implement all concept in sklearn| Pending | Github |⭐️⭐️⭐️ | 156 | | 16 | [**Singular Value Decomposition**](https://www.youtube.com/playlist?list=PLMrJAkhIeNNSVjnsviglFoY2nXildDCcv) |Steve Brunton| Pending | Youtub | | 157 | | 17 | [**Linear Algebra for Machine Learning**](https://www.youtube.com/watch?v=Qc19jQWHdL0&list=PLRDl2inPrWQW1QSWhBU0ki-jq_uElkh2a) |Jon Krohn| Pending | Youtub |⭐️⭐️⭐️ | 158 | | 18 | [**Learning from Data**](https://work.caltech.edu/lectures.html) |Taught by Feynman Prize winner Professor Yaser Abu-Mostafa. | Youtub |⭐️⭐️⭐️ | 159 | | 19| [**UC Berkeley CS188 Intro to AI**](https://ai.berkeley.edu/lecture_videos.html) |Complete sets of Lecture Slides and Videos | Youtub |⭐️⭐️⭐️ | 160 | 161 | 162 | 163 | ## πŸ‘οΈ Chapter 2: Important Websites 164 | 165 | | **Title** | **Description** | **Status** | 166 | |-----------------------------------|-----------------------------------------------------|------------| 167 | | [**βœ… 1-Roadmap.sh**](https://roadmap.sh/r/llm-engineer-ay1q6) | Comprehensive roadmap for AI courses | Completed | 168 | | [**βœ… 2-Bolt**](https://bolt.new/) | Write software code and deploy | Completed | 169 | | [**βœ… 3-AI Personal Assistant**](https://www.uphop.ai/app?code=cHVMT) | Write software code and deploy | Completed | 170 | | [**βœ… 4-Deep-ML**](https://www.deep-ml.com/) | Interactive learning of ML, solve ML problems | Completed | 171 | | [**βœ… 5-LeetGPU**](https://leetgpu.com/challenges) | It offers real-time execution and GPU simulation for learning and performance analysis.| InProgress | 172 | 173 | 174 | 175 | ## βž• Additional Social Media Groups 176 | 177 | | **Title/Link** | **Description** | **Status** | Platform| 178 | |---------------------------------------------------------------------------------|----------------------------------------------------|------------|---| 179 | | [**βœ… 1- HELP ME CROWD-SOURCE A MACHINE LEARNING ROADMAP - 2025**](https://www.reddit.com/r/learnmachinelearning/comments/1ixx095/help_me_crowdsource_a_machine_learning_roadmap/?share_id=Vhs4ll7wCPbA32yvSAOnj&utm_content=2&utm_medium=ios_app&utm_name=ioscss&utm_source=share&utm_term=1) | Reddit thread focused on crowd-sourcing a 2025 ML learning roadmap | Pending |Reddit 180 | | [**βœ… 2- Introductory Books to Learn the Math Behind Machine Learning (ML)**](https://www.reddit.com/r/learnmachinelearning/comments/1jtw8p7/introductory_books_to_learn_the_math_behind/) | Community recommendations for foundational ML math books | Pending |Reddit| 181 | | [**βœ… 3- Industry ML Skill**](https://substack.com/home/post/p-156032645) | Substack publication sharing ML skills in industry settings | Pending |Substack| 182 | | [**βœ… 4- Data School**](https://www.youtube.com/@dataschool) | YouTube channel focused on teaching Scikit-learn and data science | Pending |Youtube| 183 | 184 | 185 | ## πŸ‘οΈ Chapter 4: Free Books 186 | 187 | | **Title/Link** | **Description** | **Code** | 188 | |---------------------------------------------------------------------------------|---------------------------------------------------|----------| 189 | | [**βœ… 1- Linear Algebra and Optimization for Machine Learning**](https://macro.com/app/pdf/d885fcad-84b5-4ba4-a03e-6f931d746dc5) | Videos and GitHub resources for learning | Not provided | 190 | | [**βœ… 2- The-Art-of-Linear-Algebra**](https://github.com/dr-mushtaq/Machine-Learning/blob/master/The-Art-of-Linear-Algebra.pdf) | Videos and GitHub resources for learning | Not provided | 191 | 192 | 193 | ## πŸ‘οΈ Chapter 5: Github Repositories 194 | 195 | | **Title/Link** | **Description** | **Status** | 196 | |---------------------------------------------------------------------------------|--------------------------------------------------|------------| 197 | | [**βœ… 1- Computer Science Courses with Video Lectures**](https://github.com/Developer-Y/cs-video-courses?fbclid=IwZXh0bgNhZW0CMTAAAR2J9tEPD3kPegVzCWQ0WkBYSS6go_0G0PjRSaNojiOjDG85ccS45lZGyBE_aem_Ack4D65TusReJ6ybfh6ZIy9MXZ6ezPKugIzvqWZO2HtMW1C4Y38SpzlpjSzB4pr4-X4tFDusPKaI4SeieXZKMIcn) | GitHub repository with video lectures for computer science courses | Pending | 198 | | [**βœ… 2- ML YouTube Courses**](https://github.com/dair-ai/ML-YouTube-Courses?fbclid=IwAR26ZRVJyPC6_fFmcOy5IA-u4relyRSAxM5N-pleAD59VwrsSvOX8MsEpaQ) | GitHub repository containing YouTube courses on machine learning | Pending | 199 | | [**βœ… 3- ML Roadmap**](https://github.com/loganthorneloe/ml-roadmap?tab=readme-ov-file#mathematics) | GitHub repository for machine learning roadmap | Pending | 200 | | [**βœ… 4- Courses & Resources**](https://github.com/SkalskiP/courses#cr%C3%A8me-de-la-cr%C3%A8me-of-ai-courses) | GitHub repository with AI courses and resources | Pending | 201 | | [**βœ… 5- Awesome Machine Learning and AI Courses**](https://github.com/luspr/awesome-ml-courses#awesome-machine-learning-and-ai-courses) | GitHub repository featuring a curated list of machine learning and AI courses | Pending | 202 | | [**βœ… 6- Feature Engineering and Feature Selection**](https://github.com/Yimeng-Zhang/feature-engineering-and-feature-selection) | GitHub repository focused on feature engineering and selection in Python by Yimeng Zhang | Pending | 203 | | [**βœ… 7- machine-learning**](https://github.com/ethen8181/machine-learning/tree/master) | This is a continuously updated repository that documents personal journey on learning data science, machine learning related topics basci to advance level implementationm and topic | Pending | 204 | 205 | ## πŸ‘οΈ Chapter1: - **Important Library and Packages** 206 | | Title| Description | Code | 207 | |---|---|---| 208 | |[**🌐1- Prompt Library**](https://www.promptly.fyi/library)|Find Prompt|---| 209 | |[**🌐2- Computer Science courses w**]()|It is Videos and github|---| 210 |
211 | 212 | ## πŸ’» Workflow: 213 | 214 | - Fork the repository 215 | 216 | - Clone your forked repository using terminal or gitbash. 217 | 218 | - Make changes to the cloned repository 219 | 220 | - Add, Commit and Push 221 | 222 | - Then in Github, in your cloned repository find the option to make a pull request 223 | 224 | > print("Start contributing for Machine Learning") 225 | > 226 | ## βš™οΈ Things to Note 227 | 228 | * Make sure you do not copy codes from external sources because that work will not be considered. Plagiarism is strictly not allowed. 229 | * You can only work on issues that have been assigned to you. 230 | * If you want to contribute the algorithm, it's preferrable that you create a new issue before making a PR and link your PR to that issue. 231 | * If you have modified/added code work, make sure the code compiles before submitting. 232 | * Strictly use snake_case (underscore_separated) in your file_name and push it in correct folder. 233 | * Do not update the **[README.md](https://github.com/prathimacode-hub/ML-ProjectKart/blob/main/README.md).** 234 | 235 | πŸ” **Explore moreπŸ‘‹πŸ›’** 236 | 237 | Explore cutting-edge tools and Python libraries, access insightful slides and source code, and tap into a wealth of free online courses from top universities and organizations. Connect with like-minded individuals on Reddit, Facebook, and beyond, and stay updated with our YouTube channel and GitHub repository. Don’t wait β€” enroll now and unleash your Machine Learning potential!” 238 | 239 | * [**Supervised learning with scikit-learn**](https://coursesteach.com/enrol/index.php?id=21) 240 | * [**Fundamental of Machine Learning**](https://coursesteach.com/enrol/index.php?id=6) 241 | 242 | 243 | ## **✨Top Contributors** 244 | We would love your help in making this repository even better! If you know of an amazing AI course that isn't listed here, or if you have any suggestions for improvement in any course content, feel free to open an issue or submit a course contribution request. 245 | 246 | Together, let's make this the best AI learning hub website! πŸš€ 247 | 248 | Thanks goes to these Wonderful People. Contributions of any kind are welcome!πŸš€ 249 | 250 | 251 | 252 | 253 | 254 | 255 | 256 | 257 | 258 | 259 | 260 | 261 | 262 | --------------------------------------------------------------------------------