From 899e49c2c6bb6bdc3f0c6bb0bc8de131e3ec99e6 Mon Sep 17 00:00:00 2001 From: Python Engineer Date: Thu, 19 Mar 2020 22:14:36 +0100 Subject: [PATCH] Initial commit --- README.md | 69 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 69 insertions(+) create mode 100644 README.md diff --git a/README.md b/README.md new file mode 100644 index 0000000..ff45c45 --- /dev/null +++ b/README.md @@ -0,0 +1,69 @@ +# The Ultimate FREE Machine Learning Study Plan + +### 0. Prerequisites +- Linear Algebra and Multivariate Calculus + - [ ] [Khan Academy - Multivariable Calculus](https://www.khanacademy.org/math/multivariable-calculus) + - [ ] [Khan Academy - Differential Equations](https://www.khanacademy.org/math/differential-equations) + - [ ] [Khan Academy - Linear Algebra](https://www.khanacademy.org/math/linear-algebra) + - [ ] [3Blue1Brown - Essence of Linear Algebra](https://www.3blue1brown.com/essence-of-linear-algebra-page/) +- Statistics + - [ ] [Khan Academy - Statistics Probability](https://www.khanacademy.org/math/statistics-probability) +- Python + - [ ] [Python Full Course 4 Hours - FreeCodeCamp on YouTube](https://www.youtube.com/watch?v=rfscVS0vtbw) + - [ ] [Advanced Python - Playlist on YouTube (Python Engineer)](https://www.youtube.com/watch?v=QLTdOEn79Rc&list=PLqnslRFeH2UqLwzS0AwKDKLrpYBKzLBy2) + - [ ] [Numpy - Free Udemy Course](https://www.udemy.com/course/deep-learning-prerequisites-the-numpy-stack-in-python/) + - Matplotlib + - [ ] [sentdex - Playlist on YouTube](https://www.youtube.com/watch?v=q7Bo_J8x_dw&list=PLQVvvaa0QuDfefDfXb9Yf0la1fPDKluPF) or + - [ ] [Corey Schafer - Playlist on Youtube](https://www.youtube.com/watch?v=UO98lJQ3QGI&list=PL-osiE80TeTvipOqomVEeZ1HRrcEvtZB_) + - [ ] [Pandas Tutorial - Playlist on Youtube (Corey Schafer)](https://www.youtube.com/watch?v=ZyhVh-qRZPA&list=PL-osiE80TeTsWmV9i9c58mdDCSskIFdDS) + +### 1. Basics Machine Learning +- [ ] [Coursera Free Course by Andrew Ng](https://www.coursera.org/learn/machine-learning) +- [ ] [Machine Learning Stanford Full Course on YouTube](https://www.youtube.com/watch?v=PPLop4L2eGk&list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN) +- [ ] [Machine Learning From Scratch - Playlist on YouTube (Python Engineer)](https://www.youtube.com/watch?v=ngLyX54e1LU&list=PLqnslRFeH2Upcrywf-u2etjdxxkL8nl7E) +- Books (Optional): + - [ ] [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) + - [ ] [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) + - [ ] [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) + +### 2. Deep Learning +- [ ] [Stanford Lecture - Convolutional Neural Networks for Visual Recognition](https://www.youtube.com/watch?v=vT1JzLTH4G4&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv) +- Learn PyTorch (or Tensorflow) + - [ ] [pytorch.org official Tutorials](https://pytorch.org/tutorials/) + - [ ] [PyTorch Free Course on YouTube (Python Engineer)](https://www.youtube.com/watch?v=EMXfZB8FVUA&list=PLqnslRFeH2UrcDBWF5mfPGpqQDSta6VK4) +- fast.ai - Free Courses + - [ ] [Practical Deep Learning for Coders Part 1](https://www.fast.ai/) + - [ ] [Part 2](https://course.fast.ai/part2) + +Optional: +- [ ] [Stanford Lecture - Natural Language Processing with Deep Learning](https://www.youtube.com/watch?v=8rXD5-xhemo&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z) +- [ ] [Stanford Lecture- Reinforcement Learning](https://www.youtube.com/watch?v=FgzM3zpZ55o&list=PLoROMvodv4rOSOPzutgyCTapiGlY2Nd8u) + +### 3. Competitions and Own Projects +- [Kaggle](https://www.kaggle.com/) + - [ ] Datasets (develop own projects) + - [ ] Competitions (start with Getting started section) + +### 4. Prep for Interviews +- [ ] https://github.com/alexeygrigorev/data-science-interviews + +## Next Level +- Make your own projects to show what you have learned. +- Reproduce paper and implement the algorithms. +- Write a blog to explain what you have learned. +- Contribute to ML/DL related open source projects (sklearn, pytorch, fastai, ...). +- Get into Kaggle competitions. + +## Further readings +- https://towardsdatascience.com/the-cold-start-problem-how-to-break-into-machine-learning-732ee9fedf1d +- https://www.geeksforgeeks.org/how-to-start-learning-machine-learning/ +- https://www.youtube.com/watch?v=itzmu0l93wM +- https://elitedatascience.com/learn-machine-learning#step-0 +- https://medium.com/@gordicaleksa/get-started-with-ai-and-machine-learning-in-3-months-5236d5e0f230 +- https://towardsdatascience.com/beginners-guide-to-machine-learning-with-python-b9ff35bc9c51 +- https://www.fast.ai/2019/01/02/one-year-of-deep-learning/ + +GitHub: +- https://github.com/ZuzooVn/machine-learning-for-software-engineers +- https://github.com/Avik-Jain/100-Days-Of-ML-Code +- https://github.com/yanshengjia/ml-road