Recommended learning resources for Python, A.I. and machine learning

Artificial intelligence / machine learning has been all over the news. This is a field that excites me and I’m aiming to transition into data engineering in the near future.

In the last 2 or 3 years before 2022, I had been reading several books and an endless stream of online articles about AI/ML. I tried to take a couple of free courses here and there – including Andrew Ng’s Machine Learning course on Coursera, Khan Academy’s video course on linear algebra, and Kaggle’s 30 Days of ML – but they were mostly false starts which didn’t get me very far.

It wasn’t until 2022 that I started to take my learning more seriously, mostly by doing the courses offered by AI Singapore and Datacamp. Along the way, I noted which books and courses worked best for me, which I share below.

Before that, here is the best tip I can offer to anyone who is serious about learning this stuff:

Don’t do one or two hours today, pause for a few days, get busy with other things, and then come back next week or next month to pick up where you left off. You are going to forget what you learned, and will have to backtrack just to refresh your memory. It’s better to keep doing it every single day, even if for only an hour or even half an hour per day. That way, it stays fresh in your mind.

Below is a list of books and courses which I personally find the most useful.

Books

Python books

  • Think Python, 2nd Edition (2015) by Allen B. Downey [Free]
    This is by far the most beginner-friendly and comprehensive Python textbook I have come across. I only discovered this book after I had already read other Python books, and I wish I had started with this one. As it’s released under Creative Commons, you can download it for free as a PDF or read it online. You can download this and many other free ebooks by Allen Downey from this site: greenteapress.com

  • Python Crash Course: A Hands-On, Project-Based Introduction to Programming, 2nd Edition (2019), by Eric Matthes.
    What I like about this book is that after covering the theory in the first half of the book, the second half walks you through 3 hands-on projects, which is indispensable if you want to internalize what you learned:
    Project 1: Alien invasion arcade game.
    Project 2: Data Visualization – data analysis using the Matplotlib package. Just nice for those learning data!
    Project 3: Learning Log web app – an online journal system that lets you keep track of information you’ve learned about particular topics, developed on the Django framework.

Math books

  • Introductory Statistics [Free]
    This is a high quality, beginner-friendly textbook, developed collaboratively by college teachers on the Openstax platform. I discovered it on Google when I wanted to understand why the exponential distribution‘s probability distribution curve is shaped that way, and the chapter explained it perfectly.

Courses

Kaggle courses [free]

Kaggle is a platform famous for hosting competitions sponsored by companies which put up prize money to find the best solutions for their data science problems. Kaggle has a series of free mini courses for beginners:

  • Python
  • Intro to Machine Learning
  • Pandas
  • Data Visualization
  • Intro to SQL
  • … and many more

These introductory courses are easy to follow and you get to practise coding via the Jupyter notebook interface. When you finish each of these courses, you can get a digital certificate to post on your social media.

AI Singapore’s courses for Everyone [free]

There are 2 main relevant sections under here:

  1. AI for Everyone (AI4E)
  2. Data Analytics for Everyone (DA4E)

These are good for beginners and consist of videos and hands-on practice. Upon completion, you can get digital certificates to post on your social media.

AI Singapore’s courses for Professionals [free and paid]

There are some free courses here, including a Literacy in AI course and Becoming an AI Apprentice. There are also two other courses by Steven Skiena (Data Science Design Manual, which I describe below) and Intel (Intel AI Academy). Upon completion, you can get digital certificates to post on your social media.

There are also premium courses which are available only to paying subscribers, which gives you access to Datacamp online courses. I recommend the premium subscription – the Datacamp access gives you a structured hands-on learning experience, which for me personally is invaluable, because this is the first course which I’m doing consistently.

Steven Skiena’s The Data Science Design Manual online course

Steven Skiena is a co-inventor of the Apple iPad. Based on his famous book of the same title, this online course contains lecture videos and slides. I have not done the course yet, but I have read about half of the book, and I love his witty humour, insights and war stories. I found myself laughing out loud when reading many of the chapters, but admittedly, I think the learning curve of some chapters might be a little too steep for beginners coming in cold. I’ll come back to this after I’ve covered my foundations.


Hope these are useful for those who are on the same learning journey as me. I will come back and add to this list from time to time.

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