└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # The Ultimate FREE Machine Learning Study Plan 2 | 3 | A complete study plan to become a Machine Learning Engineer with links to all FREE resources. If you finish the list you will be equipped with enough theoretical and practical experience to get started in the industry! I tried to limit the resources to a minimum, but some courses are extensive. 4 | 5 |
6 | 7 | Watch the video on YouTube for instructions: 8 | [![Alt text](https://img.youtube.com/vi/dYvt3vSJaQA/hqdefault.jpg)](https://www.youtube.com/watch?v=dYvt3vSJaQA) 9 | [https://www.youtube.com/watch?v=dYvt3vSJaQA](https://www.youtube.com/watch?v=dYvt3vSJaQA) 10 |
11 | #### IMPORTANT: 12 | - This list is not sponsored by any of the mentioned links! I did a lot of the courses myself and can highly recommend them! 13 | - This list takes a lot of time and effort to finish if you want to do it properly! The list does not look that long, but don't underestimate it. 14 | 15 | #### How to use the Plan: 16 | - For theory lectures: Follow along, take notes, and repeat the notes afterwards. 17 | - For practical lectures/courses: Follow along, take notes. If they provide exercises, do them!!! Do not just google the answer, but try to solve it yourself first! 18 | - For coding tutorials: Code along, and after the video try to code it on your own again. 19 | - Step 3 is critical! Your theoretical knowledge is worthless if you don't know how to apply it to real world problems! Do as many personal projects and competitions as you can! You don't have to wait with step 3 until you finished the other parts, I recommend starting with a side project or kaggle competition after you finished part 1.1 (Andrew Ng's course). 20 | 21 | ## The Plan 22 | 23 | ### 0. Prerequisites 24 | - [ ] Linear Algebra and Multivariate Calculus 25 | - [ ] [Khan Academy - Multivariable Calculus](https://www.khanacademy.org/math/multivariable-calculus) 26 | - [ ] [Khan Academy - Differential Equations](https://www.khanacademy.org/math/differential-equations) 27 | - [ ] [Khan Academy - Linear Algebra](https://www.khanacademy.org/math/linear-algebra) 28 | - [ ] [3Blue1Brown - Essence of Linear Algebra](https://www.3blue1brown.com/essence-of-linear-algebra-page/) 29 | - [ ] Statistics 30 | - [ ] [Khan Academy - Statistics Probability](https://www.khanacademy.org/math/statistics-probability) 31 | 32 | - [ ] Python 33 | - [ ] [Python Full Course 4 Hours - FreeCodeCamp on YouTube](https://www.youtube.com/watch?v=rfscVS0vtbw) 34 | - [ ] [Advanced Python - Playlist on YouTube (Python Engineer)](https://www.youtube.com/watch?v=QLTdOEn79Rc&list=PLqnslRFeH2UqLwzS0AwKDKLrpYBKzLBy2) 35 | - [ ] [Numpy - Free Udemy Course](https://www.udemy.com/course/deep-learning-prerequisites-the-numpy-stack-in-python/) 36 | - [ ] Matplotlib 37 | - [ ] [sentdex - Playlist on YouTube](https://www.youtube.com/watch?v=q7Bo_J8x_dw&list=PLQVvvaa0QuDfefDfXb9Yf0la1fPDKluPF) or 38 | - [ ] [Corey Schafer - Playlist on Youtube](https://www.youtube.com/watch?v=UO98lJQ3QGI&list=PL-osiE80TeTvipOqomVEeZ1HRrcEvtZB_) 39 | - [ ] [Pandas Tutorial - Playlist on Youtube (Corey Schafer)](https://www.youtube.com/watch?v=ZyhVh-qRZPA&list=PL-osiE80TeTsWmV9i9c58mdDCSskIFdDS) 40 | 41 | ### 1. Basics Machine Learning 42 | - [ ] [Coursera Free Course by Andrew Ng](https://www.coursera.org/learn/machine-learning) 43 | - [ ] [Machine Learning Stanford Full Course on YouTube](https://www.youtube.com/watch?v=PPLop4L2eGk&list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN) 44 | - [ ] [Udacity - Introduction to Machine Learning](https://www.udacity.com/course/intro-to-machine-learning--ud120) 45 | - [ ] [Machine Learning From Scratch - Playlist on YouTube (Python Engineer)](https://www.youtube.com/watch?v=ngLyX54e1LU&list=PLqnslRFeH2Upcrywf-u2etjdxxkL8nl7E) 46 | - [ ] Books (Optional and not free, but I recommend at least the first one): 47 | - [ ] [Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow - Aurélien Géron](https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/ref=sr_1_1?crid=1J69S9GKU93E4&keywords=hands+on+machine+learning+with+scikit-learn+and+tensorflow+2&qid=1584648367&sprefix=hands+o%2Caps%2C256&sr=8-1) 48 | - [ ] [Python Machine Learning - Sebastian Raschka](https://www.amazon.com/Python-Machine-Learning-scikit-learn-TensorFlow/dp/1789955750/ref=sr_1_1?crid=L7PEHL95RXH4&keywords=python+machine+learning&qid=1584648438&sprefix=python+ma%2Caps%2C230&sr=8-1) 49 | - [ ] [Introduction to Machine Learning with Python - Andreas Müller](https://www.amazon.com/Introduction-Machine-Learning-Python-Scientists/dp/1449369413/ref=sr_1_1?crid=WAQPG9CEM3W&keywords=introduction+to+machine+learning+with+python&qid=1584648523&sprefix=introduc%2Caps%2C238&sr=8-1) 50 | 51 | ### 2. Deep Learning 52 | - [ ] [Stanford Lecture - Convolutional Neural Networks for Visual Recognition](https://www.youtube.com/watch?v=vT1JzLTH4G4&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv) 53 | - [ ] Learn PyTorch (or Tensorflow) 54 | - [ ] [pytorch.org official Tutorials](https://pytorch.org/tutorials/) 55 | - [ ] [PyTorch Free Course on YouTube (Python Engineer)](https://www.youtube.com/watch?v=EMXfZB8FVUA&list=PLqnslRFeH2UrcDBWF5mfPGpqQDSta6VK4) 56 | - [ ] fast.ai - Free Courses 57 | - [ ] [Practical Deep Learning for Coders Part 1](https://www.fast.ai/) 58 | - [ ] [Part 2](https://course.fast.ai/part2) 59 | 60 | Optional: 61 | - [ ] [Stanford Lecture - Natural Language Processing with Deep Learning](https://www.youtube.com/watch?v=8rXD5-xhemo&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z) 62 | - [ ] [Stanford Lecture- Reinforcement Learning](https://www.youtube.com/watch?v=FgzM3zpZ55o&list=PLoROMvodv4rOSOPzutgyCTapiGlY2Nd8u) 63 | 64 | ### 3. Competitions and Own Projects 65 | - [ ] [Kaggle](https://www.kaggle.com/) 66 | - [ ] Datasets (develop own projects) 67 | - [ ] Competitions (start with Getting started section) 68 | - [ ] [8 Fun Machine Learning Projects For Beginners](https://elitedatascience.com/machine-learning-projects-for-beginners) 69 | 70 | ### 4. Prep for Interviews 71 | - [ ] https://github.com/alexeygrigorev/data-science-interviews 72 | 73 | ## Next Level 74 | - Make your own projects to show what you have learned. 75 | - Reproduce paper and implement the algorithms. 76 | - Write a blog to explain what you have learned. 77 | - Contribute to ML/DL related open source projects (sklearn, pytorch, fastai, ...). 78 | - Get into Kaggle competitions. 79 | 80 | ## Further readings 81 | - [The cold start problem: how to break into machine learning](https://towardsdatascience.com/the-cold-start-problem-how-to-break-into-machine-learning-732ee9fedf1d) (Towardsdatascience) 82 | - [How to Start Learning Machine Learning?](https://www.geeksforgeeks.org/how-to-start-learning-machine-learning/) (GeekforGeeks) 83 | - [How to get started in machine learning - best books and sites for machine learning](https://www.youtube.com/watch?v=itzmu0l93wM) (YouTube) 84 | - [How you can get a world-class machine learning education for free](https://elitedatascience.com/learn-machine-learning#step-0) (Elite Data Science) 85 | - [Get started with AI and machine learning in 3 months](https://medium.com/@gordicaleksa/get-started-with-ai-and-machine-learning-in-3-months-5236d5e0f230) (Aleksa Gordić) 86 | - https://towardsdatascience.com/beginners-guide-to-machine-learning-with-python-b9ff35bc9c51 87 | - [One year of deep learning](https://www.fast.ai/2019/01/02/one-year-of-deep-learning/) (Fast.ai) 88 | - [Getting Started with Applied Machine Learning](https://machinelearningmastery.com/start-here/#getstarted) (Machine Learning Mastery) 89 | 90 | 91 | GitHub: 92 | - https://github.com/ZuzooVn/machine-learning-for-software-engineers 93 | - https://github.com/Avik-Jain/100-Days-Of-ML-Code 94 | - https://github.com/yanshengjia/ml-road 95 | 96 | ## Further resources added by the community 97 | Contributions are welcome! If you can recommend any other resources, feel free to open a pull request :) 98 | - [ ] [Book: Automate The Boring Stuff with Python](https://automatetheboringstuff.com/) (Till Chapter 6 for Python Basics, the remaining chapters include the applications of Python) 99 | - [ ] [Book: Python Crash Course by Erric Matthes](https://ehmatthes.github.io/pcc_2e/regular_index/) 100 | - [ ] [Book: Learning Python by Mark Lutz](https://www.oreilly.com/library/view/learning-python-5th/9781449355722/) 101 | - [ ] [Basics of Neural Networks, how they learn and some of the involved Mathematics(3Blue1Brown series)](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi) 102 | - [ ] [Article on Beginner Level Datasets](https://medium.com/machine-learning-india/getting-started-in-data-science-beginner-level-datasets-376ffe60c6fe) 103 | - [ ] [Article on Life Cycle of a Data Science Project](https://medium.com/machine-learning-india/the-life-cycle-of-a-data-science-project-d614d8d233b7) 104 | - [ ] [Book: Grokking Algorithms: An Illustrated Guide for Programmers and Other Curious People](https://www.manning.com/books/grokking-algorithms) 105 | - [ ] [Book: Mathematics for Machine Learning](https://mml-book.github.io/) (with tutorials - FREE) 106 | - [ ] [Book: An Introduction to Statistical Learning](https://www.statlearning.com/) (- FREE) 107 | - [ ] [Essentials of Statistics by Monica Wahi](https://www.youtube.com/watch?v=8mxrwJcB2eI&list=PL64SCLAD3d1FlVowhKnYrY7JGuVd24HWm&ab_channel=MonikaWahi) (YouTube) 108 | --------------------------------------------------------------------------------