Below is a non-exhaustive list of resources including blogs, courses, books, podcasts, and video lectures which I have found extremely useful in learning python, statstics, and machine-learning concepts.

For practical purposes, I’ve noticed, it is not always necessary dive super deep in a concept, rather its helpful to get a concise version of the concept, understand the core assumptions, and start applying the concept right away figuring out your knowledge gaps along the way. I strongly believe in the 80-20 rule (80% output from 20% input). In that spirit, following are the top five sources to get upto speed on learning the basics of ML.

I started to read on ML and data analysis using this wonderful book by Aurélien Géron. This is one of the best, if not the best, introductory books for machine learning. It is concise and simple to read and has jupyter notebooks to apply the concepts taught in it. Initial chapters (Part 1) of the book offer a strong foundation for traditional ML algorithms.

Besides just focusing on ML, having experience with data wrangling using PyData stack (NumPy, Pandas, and friends) is always a plus. In fact, most of the time the limitation in setting up any ML model is massaging data into machine readable format.

Deep Learning is the most popular sub-branch of ML and something you should have a general understanding of. Jeremy Howard and team have setup this wonderful didactic coursework using PyTorch (personal preference) comprising of useful collection of walkthroughs and practical examples.

Fantastic high-level math focussed introduction to algorithms.

Approachable compendium of key ML concepts boiled down to key insights, offers a nice way to articulate concepts in a concise way.

Nice (free) online courses:

Machine Learning

Data Science and Computation

Miscellaneous

Python in general

Learning Python

Tutorials / Projects

Writing better code

Datasets

Books

Statstics & Exploratory Analysis

Data Science

Data Visualization

Machine-Learning

Machine-learning focused key commentaries, perspectives, and reviews

Area reviews

General tips

Commentaries

In Chemical Sciences:

Molecular science:

Graph networks

Cheat Sheets

I’ve compiled some nice cheat-sheets discussing basics of ML, Data Science, Statistics concepts alongside some tips on NumPy, Pandas, and Scikit-learn. These compilations are particularly useful when brushing up details before a potential job interview. Link to dataset repository

Video series

Explanations

PyCon talks

AI talks / commentaries

Blogs

Data Science focused

Statistics Blogs

ML inclined

ML code examples and tutorials

General compilations

Data-inspired Podcasts

Fanstastic resource, you can be a fly on the wall and listen to experts talk about a topic that interests you

  • AI in Business
  • McKinsey AI
  • AZ16 podcast
  • Data Skeptic
  • Lex Friedman / AI podcast
  • Microsoft Research Podcast

YouTubers

List of YouTuber channels that never fail to inspire me

1. Science and Technology

Statistics

2. Food

3.Videography and Design

4. Journalism

Diversity & Inclusion

Misc

Comics