https://github.com/assemblyai-community/ml-study-guide

Minimal Machine Learning Study Plan
https://github.com/assemblyai-community/ml-study-guide

Last synced: 29 days ago
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Minimal Machine Learning Study Plan

• Host: GitHub
• URL: https://github.com/assemblyai-community/ml-study-guide
• Owner: AssemblyAI-Community
• Created: 2022-06-21T13:46:10.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-06-13T05:49:54.000Z (5 months ago)
• Last Synced: 2024-10-01T20:40:38.474Z (about 1 month ago)
• Size: 13.7 KB
• Stars: 1,846
• Watchers: 49
• Forks: 385
• Open Issues: 5
• Metadata Files:
  • Readme: README.md

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README

        
# How to get started with ML

This my recommended study guide to get started with Machine Learning.

Watch the video on [YouTube](https://youtu.be/wtolixa9XTg).

## 1. Math

Learn some math basics! Focus only on these topics, then come back later in case you need to learn more.

 * [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)

 * [Khan Academy - Statistics Probability](https://www.khanacademy.org/math/statistics-probability)

 * [Optional: 3Blue1Brown - Essence of Linear Algebra](https://www.3blue1brown.com/essence-of-linear-algebra-page/)

 

## 2. Learn Python

* [4h Beginner Course](https://youtu.be/rfscVS0vtbw)

* [6h Intermediate Python Programming Course](https://youtu.be/HGOBQPFzWKo)

## 3. Learn The ML Tech Stack:

* NumPy:  [1h NumPy Crash Course](https://youtu.be/9JUAPgtkKpI)

* Pandas: [1h Pandas Crash Course](https://youtu.be/vmEHCJofslg)

* Matplotlib: [1h Matplotlib Crash Course Course](https://youtu.be/3Xc3CA655Y4)

(Scikit-Learn and TensorFlow are taught in step 4. PyTorch is optional, maybe in step 7)

## 4. Machine Learning Courses

* [Machine Learning Specialization Andrew Ng | Coursera](https://www.coursera.org/specializations/machine-learning-introduction) (3 Courses)

* Optional: [Machine Learning From Scratch](https://youtube.com/playlist?list=PLqnslRFeH2Upcrywf-u2etjdxxkL8nl7E)

## 5. Hands-on Data Preparation

* [Kaggle Intro to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning)

* [Kaggle Intermediate Machine Learning](https://www.kaggle.com/learn/intermediate-machine-learning)

## 6. Practise!

Solve Challenges and build your own projects with datasets from [Kaggle.com](Kaggle.com).

## 7. Specialize & Create Blog

* Specialize in one field (e.g. Computer Vision, NLP, etc.) 

* Look at requirements in corresponding job descriptions and learn those skills

* Tip: Create a blog and share tutorials and what you have learned!

## Books

If you prefer learning with books, these are great recommendations:

* [Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/)

* [Machine Learning with PyTorch and Scikit-Learn](https://www.packtpub.com/product/machine-learning-with-pytorch-and-scikit-learn/9781801819312)