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From Reading to Coding - Self-study

(Nguyen Truong Thinh)

The Brief Introduction

As a mobile app developer with more than 3 years of experience but with no
background in machine learning. I found these books to be pure gold. I want to
understand Machine Learning algorithms and will find tutorials to teach the concepts
required, with a final hands-on application at the end that is useful.

I have read many artificial intelligence books. But most of the books are heavily
focused on the mathematics of artificial intelligence, which makes them difficult to
understand for people without mathematics or computer science backgrounds.
I have always wanted to find out a book that could make it easier to get into
the artificial intelligence field for beginners—people from all different disciplines.

Thanks to the countless researchers and developers around the world and
their open source code, particularly Python-based open source code, it is
much easier to use artificial intelligence now than 10 years ago.
Through these books, I will find that I can do amazing things with just
a few lines of code, and in some cases, I don’t need to code at all.

Used Software Info

  • PyCharm 2022.2 (Professional Edition)
  • Python 3.7 or above

Datasets Resource:

  1. Kaggle
  2. UCI Machine Learning Repository

Illustrated results

Custom environment with Gym

  • Coin Catcher

Gym's Built-in & third-party environments

  • Lunar Lander

  • Mountain Car

  • Phoenix

How reinforcement learning helps a robot balance a pole

  • Random movements cannot keep the balance for a long time

  • The better than random actions

References

  1. Machine Learning with Python for Everyone
  2. Programming Machine Learning: From Coding to Deep Learning
  3. Artificial Intelligence Programming with Python: From Zero to Hero
  4. Machine Learning Engineering in Action
  5. A Concise Introduction to Machine Learning
  6. Tensors For Data Processing
  7. Machine Learning with TensorFlow
  8. Build Your Own A.I. Investor
  9. Dive into Deep Learning
  10. Practical Deep Reinforcement Learning with Python
  11. Mathematical Thinking in Computer Science by University of California San Diego
  12. The Hundred-Page Machine Learning Book
  13. Clean Machine Learning Code
  14. Machine Learning in Production
  15. Hands-On Application Development with PyCharm