├── CONTRIBUTING.md ├── LICENSE ├── Packages.md ├── README.md └── resources ├── Generative-AI-and-LLMs-for-Dummies.pdf └── README.md /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | # Contributing to AI-ML-Roadmap-from-scratch 🤝🏽 2 | 3 | I'm thrilled you're interested in contributing to this repository and helping others embark on their exciting journeys into AI and Machine Learning! This guide outlines how you can effectively contribute valuable resources and enhance the overall learning experience. 4 | 5 | ## How to Contribute 6 | 7 | There are several ways you can contribute to this project: 8 | 9 | **1. Adding New Resources:** 10 | 11 | * Identify high-quality resources (courses, tutorials, books, etc.) that align with the roadmap's structure and progression levels. 12 | * Ensure the resources are credible and well-regarded in the AI/ML learning community. Prioritize free or affordable options whenever possible. 13 | * Identify which module it belongs to. 14 | * Follow the existing format in the README.md file for consistency. Include details like: 15 | * Resource title. 16 | * Type of resource (e.g., YouTube, Website, Blog, Course, etc.). 17 | * Link to the resource (URL). 18 | 19 | 20 | **2. Improving Existing Resources:** 21 | 22 | * Review the current resources and suggest corrections or updates if necessary. 23 | * Provide feedback on the resource's effectiveness, clarity, or value for specific learning levels. 24 | * Create a pull request (PR) or open an issue outlining your proposed changes or additions. 25 | 26 | 27 | ## Before Submitting a Pull Request 28 | 29 | * **Thoroughly review** the README.md file to ensure your contribution aligns with the existing structure and content. 30 | * **Search for existing issues** that might address your proposed addition or update. 31 | * **Test any new links** you add to resources to verify they function correctly. 32 | * **Write clear and concise commit messages** that describe your changes. 33 | 34 | ## Creating a Pull Request 35 | 36 | Here's a step-by-step guide on creating a pull request: 37 | 38 | 1. **Fork this repository:** 39 | 40 | - Visit this repository's URL: https://github.com/aadi1011/AI-ML-Roadmap-from-scratch 41 | - Click the "Fork" button in the top right corner. This creates a copy of the repository in your own GitHub account. 42 | - You can directly edit on your GitHub Web and submit a pull request **(skip to step 6)** or edit on your local machine (continue with step 2) 43 | 44 | 2. **Clone your forked repository to your local machine:** 45 | 46 | - You'll need Git installed on your system. If you don't have it, follow the installation instructions from https://git-scm.com/downloads. 47 | - Open a terminal and use the `git clone` command to clone your forked repository. Replace `` with your GitHub username: 48 | 49 | ```bash 50 | git clone https://github.com//AI-ML-Roadmap-from-scratch.git 51 | ``` 52 | 53 | - This creates a local copy of the repository on your machine. 54 | 55 | 3. **Make your changes:** 56 | 57 | - Open the cloned repository in your preferred code editor. 58 | - Make your edits to the `README.md` file, adding your new resources or suggesting changes to existing ones. 59 | 60 | 4. **Commit your changes:** 61 | 62 | - In your terminal, navigate to the cloned repository directory using `cd`: 63 | 64 | ```bash 65 | cd AI-ML-Roadmap-from-scratch 66 | ``` 67 | 68 | - Use Git to add your modified files to the staging area: 69 | 70 | ```bash 71 | git add README.md 72 | ``` 73 | 74 | - Commit your changes with a descriptive message using `git commit`: 75 | 76 | ```bash 77 | git commit -m "Added new resource on Deep Learning" 78 | ``` 79 | 80 | Replace "Added new resource on Deep Learning" with a clear message that reflects your contribution. 81 | 82 | 5. **Push your changes to your forked repository:** 83 | 84 | - Push your committed changes to your forked repository on GitHub: 85 | 86 | ```bash 87 | git push origin main 88 | ``` 89 | 90 | 6. **Create a pull request:** 91 | 92 | - Go to your forked repository on GitHub `https://github.com//AI-ML-Roadmap-from-scratch`. 93 | - Click on the "Pull requests" tab. 94 | - Click the green "New pull request" button. 95 | - You'll see a comparison between your branch and the main branch of the original repository. 96 | - Add a clear and concise title and description for your pull request, explaining the changes you made. 97 | - Click the "Create pull request" button to submit your request. 98 | 99 | ## Code Style and Formatting 100 | 101 | * Maintain consistent formatting and indentation as in the existing code. 102 | 103 | **We appreciate your contributions!** 104 | 105 | If you have any questions or suggestions, feel free to create an issue or reach out to the repository maintainer. 106 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 Aadith Sukumar 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 | -------------------------------------------------------------------------------- /Packages.md: -------------------------------------------------------------------------------- 1 | # Commonly Used AI/ML Python Libraries 2 | 3 | This README provides an overview of the most common libraries used in data science, machine learning, computer vision, and deep learning, along with instructions on how to install them using `pip`. 4 | 5 | ## Table of Contents 6 | 1. [Data Science Libraries](#data-science-libraries) 7 | 2. [Machine Learning Libraries](#machine-learning-libraries) 8 | 3. [Computer Vision Libraries](#computer-vision-libraries) 9 | 4. [Deep Learning Libraries](#deep-learning-libraries) 10 | 5. [Installation Instructions](#installation-instructions) 11 | 12 | 💡Incase you have installed pip and want to download all packages in one go without reading much about it, run this command directly onto your terminal: 13 | ```sh 14 | pip install numpy pandas matplotlib seaborn scipy scikit-learn xgboost lightgbm catboost opencv-python scikit-image pillow tensorflow keras torch torchvision mxnet 15 | ``` 16 | 17 | ## Data Science Libraries 18 | 19 | 1. **NumPy** 20 | - **Description:** Fundamental package for scientific computing with Python. 21 | - **Installation:** `pip install numpy` 22 | 23 | 2. **Pandas** 24 | - **Description:** Data manipulation and analysis library. 25 | - **Installation:** `pip install pandas` 26 | 27 | 3. **Matplotlib** 28 | - **Description:** Comprehensive library for creating static, animated, and interactive visualizations. 29 | - **Installation:** `pip install matplotlib` 30 | 31 | 4. **Seaborn** 32 | - **Description:** Statistical data visualization based on Matplotlib. 33 | - **Installation:** `pip install seaborn` 34 | 35 | 5. **SciPy** 36 | - **Description:** Library for mathematics, science, and engineering. 37 | - **Installation:** `pip install scipy` 38 | 39 | ## Machine Learning Libraries 40 | 41 | 1. **Scikit-learn** 42 | - **Description:** Simple and efficient tools for predictive data analysis. 43 | - **Installation:** `pip install scikit-learn` 44 | 45 | 2. **XGBoost** 46 | - **Description:** Scalable and flexible gradient boosting library. 47 | - **Installation:** `pip install xgboost` 48 | 49 | 3. **LightGBM** 50 | - **Description:** High-performance gradient boosting framework. 51 | - **Installation:** `pip install lightgbm` 52 | 53 | 4. **CatBoost** 54 | - **Description:** Gradient boosting on decision trees library. 55 | - **Installation:** `pip install catboost` 56 | 57 | ## Computer Vision Libraries 58 | 59 | 1. **OpenCV** 60 | - **Description:** Library of programming functions mainly aimed at real-time computer vision. 61 | - **Installation:** `pip install opencv-python` 62 | 63 | 2. **scikit-image** 64 | - **Description:** Collection of algorithms for image processing. 65 | - **Installation:** `pip install scikit-image` 66 | 67 | 3. **Pillow** 68 | - **Description:** Python Imaging Library (PIL) adds image processing capabilities. 69 | - **Installation:** `pip install pillow` 70 | 71 | ## Deep Learning Libraries 72 | 73 | 1. **TensorFlow** 74 | - **Description:** End-to-end open-source platform for machine learning. 75 | - **Installation:** `pip install tensorflow` 76 | 77 | 2. **Keras** 78 | - **Description:** Deep learning API written in Python, running on top of TensorFlow. 79 | - **Installation:** `pip install keras` 80 | 81 | 3. **PyTorch** 82 | - **Description:** Deep learning platform that provides maximum flexibility and speed. 83 | - **Installation:** `pip install torch torchvision` 84 | 85 | 4. **MXNet** 86 | - **Description:** Deep learning framework designed for both efficiency and flexibility. 87 | - **Installation:** `pip install mxnet` 88 | 89 | --- 90 | 91 | # Installation Instructions 92 | 93 | To install any of the above libraries, you can use the `pip` command followed by the library name. Here are general steps to install these libraries: 94 | 95 | 1. **Open your command line or terminal.** 96 | 2. **Type the following command to install the desired library:** 97 | 98 | ```sh 99 | pip install 100 | ``` 101 | 102 | Replace `` with the name of the library you wish to install. For example, to install NumPy, you would use: 103 | 104 | ```sh 105 | pip install numpy 106 | ``` 107 | 108 | 3. **Wait for the installation to complete.** 109 | 110 | ### Example Installation 111 | 112 | To install multiple libraries at once, you can list them all in a single `pip install` command: 113 | 114 | ```sh 115 | pip install numpy pandas matplotlib seaborn scipy scikit-learn xgboost lightgbm catboost opencv-python scikit-image pillow tensorflow keras torch torchvision mxnet 116 | ``` 117 | 118 | This command will install all the listed libraries in one go. 119 | 120 | ## Additional Resources 121 | 122 | For more detailed documentation and tutorials on each library, refer to their official websites: 123 | 124 | - [NumPy Documentation](https://numpy.org/doc/) 125 | - [Pandas Documentation](https://pandas.pydata.org/pandas-docs/stable/) 126 | - [Matplotlib Documentation](https://matplotlib.org/stable/contents.html) 127 | - [Seaborn Documentation](https://seaborn.pydata.org/) 128 | - [SciPy Documentation](https://docs.scipy.org/doc/scipy/) 129 | - [Scikit-learn Documentation](https://scikit-learn.org/stable/documentation.html) 130 | - [XGBoost Documentation](https://xgboost.readthedocs.io/) 131 | - [LightGBM Documentation](https://lightgbm.readthedocs.io/) 132 | - [CatBoost Documentation](https://catboost.ai/docs/) 133 | - [OpenCV Documentation](https://docs.opencv.org/) 134 | - [scikit-image Documentation](https://scikit-image.org/docs/stable/) 135 | - [Pillow Documentation](https://pillow.readthedocs.io/) 136 | - [TensorFlow Documentation](https://www.tensorflow.org/learn) 137 | - [Keras Documentation](https://keras.io/) 138 | - [PyTorch Documentation](https://pytorch.org/docs/) 139 | - [MXNet Documentation](https://mxnet.apache.org/versions/1.8.0/api/python/docs/tutorials/index.html) 140 | 141 | This README should help you get started with installing and using some of the most popular libraries in data science, machine learning, computer vision, and deep learning. Happy coding! 142 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 |

Free AI and Machine Learning Roadmap with Resources

2 | 3 | 🧠 Become skilled in Artificial Intelligence, Machine Learning, Generative AI, Deep Learning, Data Science, Natural Language Processing, Reinforcement Learning and more with this complete 0 to 100 repository. 4 | 5 | 💡 You can follow these modules simultaneously as well as in order given below. The modules are ranked in increasing order of difficulty. Content with a `⭐` are highly recommended. 6 | 7 | 📚 These are a collection of the best free resources from YouTube and online courses, as well as other popular blogs and websites. 8 | 9 | ## Contents 10 | 11 | **Learning Pathway Modules** 12 | - [Module 0](#module-0---before-you-start) - Before You Start 13 | - [Module 1](#module-1---the-math-behind-it-all) - The Math Behind It All 14 | - [Module 2](#module-2---building-your-foundation) - Building Your Foundation 15 | - [Module 3](#module-3---data-science) - Data Science 16 | - [Module 4](#module-4---machine-learning) - Machine Learning 17 | - [Module 5](#module-5---computer-vision) - Computer Vision 18 | - [Module 6](#module-6---deep-learning-neural-network) - Deep Learning Neural Network 19 | - [Module 7](#module-7---generative-ai) - Generative AI 20 | - [Sub-Module 7A](#sub-module-7a---retrieval-augmented-generation-rag) - Retrieval Augmented Generation (RAG) 21 | - [Module 8](#module-8---natural-language-processing) - Natural Language Processing 22 | - [Module 9](#module-9---reinforcement-learning) - Reinforcement Learning 23 | - [Module 10](#module-10---agentic-ai) - Agentic AI 24 | - [Bonus Module](#bonus-module---advanced-learning-pathway-courses) - Advanced Learning Pathway Courses 25 | 26 |
**Additional Cool Stuff** 27 | - [PROJECTS!](#projects) 28 | - [Interesting Websites to Visit](#interesting-websites-to-visit) 29 | - [AI Newsletters](#ai-newsletters) 30 | - [AI Blogs](#ai-blogs) 31 | - [Contribute](#contribute) 32 |
33 | 34 | ## Module 0 - Before You Start 35 | 36 | Before you begin, it is best to build your foundations and have the set-up ready. This would help you get your system working for Python on a compiler software. 37 | Mathematics is a foundation for everything in the world for Artificial Intelligence. Have a core in applied mathematical concepts like linear algebra, matrics and more can help you theoretically understand how machines work. 38 | 39 | | S.No | Type | Course Name | 40 | | ------------- | ------------- | ------------- | 41 | | 1 |`Software` | [Python 3.13 Download](https://www.python.org/downloads/) | 42 | | 2 |`Software` | [Visual Studio Code Download](https://code.visualstudio.com/download) | 43 | | 3 |`Py Package` | [Install Pip Package Installer on Python](https://www.geeksforgeeks.org/how-to-install-pip-on-windows/) | 44 | | 4 |`Py Package` | [Common Python Libraries used for AI/ML](https://github.com/aadi1011/AI-ML-Roadmap-from-scratch/blob/main/Packages.md) | 45 | 46 | 47 | ## Module 1 - The Math Behind It All 48 | 49 | The domain of AI/ML is a vast deep ocean and it's time for you to build a boat and rafters for a smooth sail. These foundational courses in Computer Science and Python Programming will get you going strong! 50 | 51 | | S.No | Type | Course Name | 52 | | ------------- | ------------- | ------------- | 53 | | 1 |`Playlist` | [Math for Machine Learning Playlist](https://youtube.com/playlist?list=PLD80i8An1OEGZ2tYimemzwC3xqkU0jKUg&si=6sZ51wadUZnscjRG) | 54 | | 2 |`⭐Course` | [NPTEL Swayam Discrete Mathematics Course](https://onlinecourses.nptel.ac.in/noc22_cs33/preview) | 55 | | 3 |`Course` | [Discrete Structures via Saylor Academy](https://www.classcentral.com/course/saylor-academy-67-cs202-discrete-structures-99529) | 56 | | 4 |`Lectures` | [Linear Algebra Lecture Series from MIT](https://ocw.mit.edu/courses/18-06-linear-algebra-spring-2010/download/) | 57 | | 5 |`Course` | [Fundamental Math for Data Science](https://www.codecademy.com/learn/paths/fundamental-math-for-data-science)| 58 | 59 | 60 | 61 | ## Module 2 - Building Your Foundation 62 | 63 | The domain of AI/ML is a vast deep ocean and it's time for you to build a boat and rafters for a smooth sail. These foundational courses in Computer Science and Python Programming will get you going strong! 64 | 65 | | S.No | Type | Course Name | 66 | | ------------- | ------------- | ------------- | 67 | | 1 |`Course` | [MITx: Introduction to Computer Science and Programming Using Python](https://www.edx.org/learn/computer-science/massachusetts-institute-of-technology-introduction-to-computer-science-and-programming-using-python) | 68 | | 2 |`Course` | [HarvardX: CS50's Introduction to Programming with Python](https://www.edx.org/learn/python/harvard-university-cs50-s-introduction-to-programming-with-python) | 69 | | 3 |`Website` | [Introduction to Python - W3 Schools](https://www.w3schools.com/python/python_intro.asp) | 70 | | 4 | `YouTube` | [Learn Python in 4 Hours](https://www.youtube.com/watch?v=rfscVS0vtbw) | 71 | | 5 | `⭐Practice!` | [Practice Python on HackerRank](https://www.hackerrank.com/domains/python) | 72 | | 6 | `Certificate` | [Python Basic Certification](https://www.hackerrank.com/skills-verification/python_basic) | 73 | 74 | 75 | 76 | 77 | ## Module 3 - Data Science 78 | 79 | Data is the new oil! Before jumping into making advanced AI, let's learn about the data that drives it. We'll cover basics of statistics and Data Science using Python in this module. 80 | 81 | | S.No | Type | Course Name | 82 | | ------------- | ------------- | ------------- | 83 | | _Bonus_ | `YouTube` | [Quick 5 Minute Intro to Data Science](https://www.youtube.com/watch?v=X3paOmcrTjQ) | 84 | | 1 | `YouTube` | [Data Science Overview](https://www.youtube.com/watch?v=ua-CiDNNj30) | 85 | | 2 | `Website` | [Data Science Introduction](https://www.w3schools.com/datascience/ds_introduction.asp) | 86 | | 3 | `YouTube` | [Python for Data Science](https://www.youtube.com/watch?v=LHBE6Q9XlzI) | 87 | | 4 | `Course` | [Google Data Analytics Professional Certificate](https://www.coursera.org/professional-certificates/google-data-analytics) | 88 | | 5 | `⭐Course` | [IBM Data Science Professional Certificate](https://www.coursera.org/professional-certificates/ibm-data-science) | 89 | 90 | 91 | 92 | 93 | 94 | ## Module 4 - Machine Learning 95 | 96 | Time to use that data to train a machine on how to learn them. Machine learning is the science of computer algorithms that help machines learn and improve from data analysis without explicit programming. _THAT'S SO COOL!_ 97 | 98 | | S.No | Type | Course Name | 99 | | ------------- | ------------- | ------------- | 100 | | 1 | `Website` | [Introductory Article on Machine Learning - Spiceworks](https://www.spiceworks.com/tech/artificial-intelligence/articles/what-is-ml/) | 101 | | 2 | `⭐Course` | [HarvardX: Data Science: Machine Learning](https://www.edx.org/learn/machine-learning/harvard-university-data-science-machine-learning) | 102 | | 3 | `Website` | [Machine Learning Tutorial - GFG](https://www.geeksforgeeks.org/machine-learning/) | 103 | | 4 | `Course` | [Explore Azure with OpenAI](https://learn.microsoft.com/en-us/training/modules/explore-azure-openai/)| 104 | | _5*_ | `Course` | [Machine Learning Specialization by Andrew Ng](https://www.coursera.org/specializations/machine-learning-introduction) | 105 | | 6 | `Course` | [Machine Learning Engineer Learning Path from Google Cloud Skills Boost](https://www.cloudskillsboost.google/paths/17) 106 | 107 | _*❗ The ML Specialization by Andrew NG is a highly specialized and industry level course by one of the most promient AI scientist - Andrew NG. It is an expert level course and is highly recommened to do one you get a good grasp of the foundational knowledge._ 108 | 109 | 110 | 111 | 112 | ## Module 5 - Computer Vision 113 | 114 | Giving the power of vision to our intelligent computers! Computer Vision trains computers to interpret and understand the visual world, just the way we see it (_or in an more advanced way ;)_) 115 | 116 | | S.No | Type | Course Name | 117 | | ------------- | ------------- | ------------- | 118 | | 1 | `YouTube` | [Computer Vision Crash Course Overview](https://www.youtube.com/watch?v=-4E2-0sxVUM&pp=ygUPY29tcHV0ZXIgdmlzaW9u) | 119 | | 2 | `YouTube` | [OpenCV Course - Full Tutorial with Python](https://www.youtube.com/watch?v=oXlwWbU8l2o) | 120 | | 3 | `Course` | [OpenCV Bootcamp](https://opencv.org/university/free-opencv-course/) | 121 | | 4 | `⭐Course` | [Computer Vision Essentials](https://www.mygreatlearning.com/academy/learn-for-free/courses/computer-vision-essentials) | 122 | | 5 | `Playlist` | (VERY ADVANCED) [Stanford Computer Vision Lectures](https://youtube.com/playlist?list=PLf7L7Kg8_FNxHATtLwDceyh72QQL9pvpQ&si=51MnhQ_APncU-RVk) | 123 | 124 | 125 | 126 | ## Module 6 - Deep Learning Neural Network 127 | 128 | Time to harness the power of our human brain to develop something that resembles the powers of a human brain. Neural Networks help you understand how information is processed from raw data like the human brain to mimic desired outputs. 129 | 130 | | S.No | Type | Course Name | 131 | | ------------- | ------------- | ------------- | 132 | | 1 | `Course` | [DeepLearning.AI Neural Networks and Deep Learning](https://www.coursera.org/learn/neural-networks-deep-learning) | 133 | | 2 | `Course` | [Neural Networks and Deep Learning](https://www.classcentral.com/course/neural-networks-deep-learning-9058) | 134 | | 3 | `Course` | [Convolutional Neural Networks](https://www.coursera.org/learn/convolutional-neural-networks) | 135 | | 4 | `⭐YouTube` | [Deep Learning Crash Course for Beginners](https://www.youtube.com/watch?v=VyWAvY2CF9c) | 136 | | 5 | `Playlist` | [Neural Networks: Zero to Hero](https://youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ&si=xfuffzwv3I9MT0Lc) | 137 | 138 | 139 | 140 | 141 | ## Module 7 - Generative AI 142 | 143 | The big buzz word everywhere! Create text, images, audios, videos, and more all thanks to Generative Adversarial Networks! 144 | 145 | | S.No | Type | Course Name | 146 | | ------------- | ------------- | ------------- | 147 | | 1 | `Course` | [Microsoft Fundamentals of Generative AI](https://learn.microsoft.com/en-us/training/modules/fundamentals-generative-ai/) | 148 | | 2 | `Course` | [Microsoft Responsible Generative AI](https://learn.microsoft.com/en-us/training/modules/responsible-generative-ai/) | 149 | | 3 | `⭐YouTube` | [Generative AI in a Nutshell](https://youtu.be/2IK3DFHRFfw?si=V9I81wsPVAhkuinS) | 150 | | 4 | `Course` | [Generative Adversarial Networks (GANs) Specialization](https://www.coursera.org/specializations/generative-adversarial-networks-gans) | 151 | | 5 | `E-Book` | [Generative AI and LLMs for Dummies](./resources/Generative-AI-and-LLMs-for-Dummies.pdf) | 152 | | 6 | `Course` | [Generative AI Learning Path by Google Cloud Skills Boost](https://www.cloudskillsboost.google/paths/118) | 153 | | 7 | `YouTube` | [Generative AI for Developers](https://www.youtube.com/watch?v=F0GQ0l2NfHA) | 154 | 155 | ### Sub-Module 7A - Retrieval Augmented Generation (RAG) 156 | Retrieval-augmented generation (RAG) is a natural language processing (NLP) technique that combines the capabilities of traditional information retrieval systems with the strengths of generative large language models (LLMs) 157 | 158 | | S.No | Type | Course Name | 159 | | ------------- | ------------- | ------------- | 160 | | 1 | `Course` | [Retrieval Augmented Generation Introduction (RXM403)](https://training.linuxfoundation.org/training/retrieval-augmented-generation-rag-intro-rxm403/) | 161 | | 2 | `Project ` | [Guided Project on RAG](https://www.coursera.org/projects/introduction-to-rag) | 162 | | 3 | `YouTube` | [Learn RAG From Scratch](https://www.youtube.com/watch?v=sVcwVQRHIc8&pp=ygUeUmV0cmlldmFsIGFndW1lbnRlZCBnZW5lcmF0aW9u) | 163 | 164 | 165 | ## Module 8 - Natural Language Processing 166 | 167 | English, Spanish, French, Hindi, Tamil, Russian, Python, Java, C++ and wait what? Let's learn how can we help computers understand our human language better (the natural language) 168 | 169 | | S.No | Type | Course Name | 170 | | ------------- | ------------- | ------------- | 171 | | 1 | `Website` | [How To Get Started with NLP](https://towardsdatascience.com/how-to-get-started-in-nlp-6a62aa4eaeff) | 172 | | 2 | `⭐Playlist` | [Tensorflow's NLP Zero to Hero](https://youtube.com/playlist?list=PLQY2H8rRoyvzDbLUZkbudP-MFQZwNmU4S&si=CTpntcabz40_MDLR) | 173 | | 3 | `YouTube` | [Natural Language Processing Pipeline](https://www.youtube.com/watch?v=6I-Alfkr5K4) | 174 | 175 | 176 | 177 | ## Module 9 - Reinforcement Learning 178 | 179 | Walk, fall, get up, learn, repeat. Just like how humans learn through experiences on what to do and what not to do, AI is no different! 180 | 181 | | S.No | Type | Course Name | 182 | | ------------- | ------------- | ------------- | 183 | | 1 | `Playlist` | [Reinforcement Learning By The Book](https://www.youtube.com/playlist?list=PLzvYlJMoZ02Dxtwe-MmH4nOB5jYlMGBjr) | 184 | | 2 | `YouTube` | [RL Basics from Scratch](https://youtu.be/vXtfdGphr3c?si=fnC5onHgc2Kmaeww) | 185 | | 3 | `Website` | [Reinforcement Learning Tutorial - JavaTPoint](https://www.javatpoint.com/reinforcement-learning) | 186 | | 4 | `⭐Website` | [Deep Reinforcement Learning Course - HuggingFace](https://huggingface.co/learn/deep-rl-course/en/unit0/introduction) | 187 | 188 | 189 | 190 | ## Module 10 - Agentic AI 191 | 192 | Don't just provide the solutions, start acting on it. Agentic AI workflows integrate AI and operations to fuel the next wave automation like never before. 193 | 194 | | S.No | Type | Course Name | 195 | | ------------- | ------------- | ------------- | 196 | | 1 | `⭐YouTube` | [AI Agents Fundamentals in 7 Minutes](https://www.youtube.com/watch?v=dJrgZrPKJfQ) | 197 | | 2 | `YouTube` | [Getting Started with LangFlow](https://www.youtube.com/watch?v=knPg4KdKU6w&pp=ygUOTGVhcm4gbGFuZ2Zsb3c%3D) | 198 | | 3 | `YouTube` | [Building RAG Based LLM App using LangFlow ](https://www.youtube.com/watch?v=rz40ukZ3krQ&pp=ygUOTGVhcm4gbGFuZ2Zsb3c%3D) | 199 | | 4 | `YouTube` | [Building a Team of AI Agents in n8n with No Code](https://www.youtube.com/watch?v=9FuNtfsnRNo) | 200 | | 5 | `Website` | [n8n Documentation](https://docs.n8n.io/) | 201 | | 6 | `Website` | [Generative AI vs Agentic AI - Forbes](https://www.forbes.com/sites/bernardmarr/2025/02/03/generative-ai-vs-agentic-ai-the-key-differences-everyone-needs-to-know/) | 202 | 203 | 204 | 205 | ## `Bonus` Module - Advanced Learning Pathway Courses 206 | 207 | Additional bonus courses and problem solving exercises. 208 | 209 | | S.No | Course Name | 210 | | ------------- | ------------- | 211 | | 1 | [Stanford Machine Learning Specialization](https://www.coursera.org/specializations/machine-learning-introduction) | 212 | | 2 | [Google: Google AI for Anyone](https://www.edx.org/learn/artificial-intelligence/google-google-ai-for-anyone) | 213 | | 3 | [IBM AI Foundations for Business Specialization](https://www.coursera.org/specializations/ibm-ai-foundations-for-business) | 214 | | 4 | [Solve Artificial Intelligence Problems on HackerRank](https://www.hackerrank.com/domains/ai) | 215 | | 5 | [Solve Functional Programming on HackerRank](https://www.hackerrank.com/domains/algorithms) | 216 | 217 | 218 | ## PROJECTS! 219 | * 20 Popular Deep Learning Projects - [TheCleverProgrammer Blog](https://thecleverprogrammer.com/2020/11/22/deep-learning-projects-with-python/) 220 | * 500 AI, Machine learning, Deep learning, Computer vision, NLP Projects with code - [GitHub Repo](https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code) 221 | * Machine Learning Projects - [GeeksForGeeks](https://www.geeksforgeeks.org/machine-learning-projects/) 222 | * 15 Python Reinforcement Learning Project Ideas for Beginners - [Project Pro](https://www.projectpro.io/article/reinforcement-learning-projects-ideas-for-beginners-with-code/521) 223 | 224 | ## Interesting Websites to Visit: 225 | * [AI Club - SIT Pune](https://www.instagram.com/aiclub.sit/) 226 | * [AI WareHouse](https://www.youtube.com/@aiwarehouse) 227 | * [Google Talk to Books](https://books.google.com/talktobooks/) 228 | * [Google Semantris Machine Learning Word Game](https://research.google.com/semantris/) 229 | * [Replika AI Avatars](https://replika.com/) 230 | * [AI Music, Text to Speech, and Voice to Voice](https://fakeyou.com/) 231 | 232 | ### AI Newsletters 233 | * [The Rundown AI](https://www.therundown.ai/) 234 | * [Mindstream](https://www.mindstream.news/) 235 | * [AI Breakfast](https://aibreakfast.beehiiv.com/) 236 | * [TLDR AI](https://tldr.tech/ai) 237 | * [The Neuron](https://www.theneurondaily.com/) 238 | 239 | ### AI Blogs 240 | * [Google AI Blogs](https://ai.google/discover/blogs/) 241 | * [Distill Publications](https://distill.pub/) 242 | * [Machine Learning Mastery](https://machinelearningmastery.com/blog/) 243 | 244 | 245 | 246 | ## Contribute 247 | 248 | Many hands make light work! I would be more than happy if you are willing to contribute to this repository and help others learn better. 249 | 250 | Make sure to read the [`CONTRIBUTING`](https://github.com/aadi1011/AI-ML-Roadmap-from-scratch/blob/main/CONTRIBUTING.md) file to understand more on how you can help! 251 | -------------------------------------------------------------------------------- /resources/Generative-AI-and-LLMs-for-Dummies.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aadi1011/AI-ML-Roadmap-from-scratch/238ad92f075e399e3dadfa621f002281b69db29b/resources/Generative-AI-and-LLMs-for-Dummies.pdf -------------------------------------------------------------------------------- /resources/README.md: -------------------------------------------------------------------------------- 1 | Additional resources to learn. 2 | --------------------------------------------------------------------------------