Resources Master Thread

I thought Instead of posting relevant resources, courses etc. in different threads. Why not create a master wiki thread with all the resources people find are good for the beginners .

I don’t know how to create a wiki thread which all can edit. someone create one if my idea is good.


I’ve turned your post into a wiki (only I can do that).

The challenge with posts like this is it’s very hard for people to know which resources are really good, and which are just random links that are the first thing someone came across. One possibility is maybe this should be a wiki, but instead each link should be a single reply in the thread, saying what the link is, and why it’s being recommended. Then people who use that resource and find it really helpful can click ‘like’. The replies that have the most likes, therefore, would be the most recommended resources.

Just another thought - I haven’t tried it so can’t say if it would work.


@Jeremy Yes I think your Idea about the reply and likes is a good one. I think We should try that.

Couse on First Python Notebook:
An amazing sample project to start on using Python (pandas) for diving into data science.
A step-by-step guide to analyzing data with Python and the Jupyter Notebook by working on a case project, using pandas library, a popular open-source tool for analyzing data.
The course will teach how to use pandas to read, filter, join, group, aggregate and rank structured data. One will also learn how to record, remix and republish your analysis using the Jupyter Notebook.



Source - my github repository…

Machine Learning & Deep Learning

Hope it helps…Feel free to add more …

Loose path:

  1. Complete them before anything Though knowledge of the commonly used frameworks (Pytorch, Keras, Tensorflow etc…) would be a huge advantage…)
  2. Math
  3. Programming
  4. Machine Learning concepts
  5. Specializations zero)

  1. Deep Learning Part 1: Practical Deep Learning for Coders

  2. What we cover in the course

  3. Deep Learning Part 2: Cutting Edge Deep Learning for Coders

  4. Computational Linear Algebra: Online textbook and Videos

  5. A Unique Path to Deep Learning Expertise—our teaching approach

  6. Pytorch Tutorial

  7. github link

The best point is the 5th one…


Understanding Math is pivotal. You can never be a good Machine Learning Scientist
by skipping the Math.

  1. Probability & Statistics
    Basic Probability and Stats will be helpful in understanding ML algorithms like Naive Bayes.

  2. Statistics 101 - Udacity
    Taught by the founder of GoogleX it’s full of exercises in Python so you won’t get bored.

  3. MIT 18.06 Linear Algebra
    Prof. Strang is terrific! Not only he’ll make you fall in love in Linear Algebra but you’ll learn
    important concepts like SVD and matrix algebra. You might wanna grab this PDF
    as well. Be sure to also solve the exam question papers from here: link

  4. MIT Single Variable Calculus
    This is my personal favorite book, use it for SVC + MVC link
    Amazing course but it gets quite tedious in the middle, you might wanna skim some geometry, but the key is
    to understand how optimization works. Be sure to solve questions from here: link

  5. MIT Multi Variable Calculus
    Understanding vector calculus is necessary for algorithms like SVM, you might wanna skim some parts
    which are purely theoretical. Be sure to solve questions from here: link

  6. (Optional) Stanford Convex Optimization
    WARNING: Do this course only if you’re very good at math. Convex Optimization will teach you numerous
    functions used in Machine Learning. But this course is extremely heavy on Math!

Introduction to Programming & Algorithms

Introduction to Machine Learning

  1. Machine Learning by Andrew Ng
    A must do course, best course of Introduction to Machine Learning so far, light on Math and focuses more on concepts.

Complete one out of two:

  1. Machine Learning A-Z
    Introductory course on ML focusing on not only Python but also R, one of the best sellers on Udemy.

  2. Introduction to Machine Learning - Udacity
    Sebastian Thrun does an awesome job explaining various approaches in ML. It gets a little boring in the middle
    but overall it’s very good.

Applied Machine Learning

Two quick courses on applying the theory you learnt. They’re short so I recommend doing both of them.

  1. Python for Data Science and Machine Learning Bootcamp

  2. Machine Learning with Python - Hands On!


Bonus Material

General Neural Network References:

Books/Guides on Deep/Machine Learning: (all excellent)




Hacker’s Guide to Neural Nets by karpathy(My Favourite)


  1. Youtube Playlist on “Deep Learning”, t from Oxford U. by Nando de Freitas

  2. Andrew Ng’s online course on ML at Stanford comes highly recommended

  3. Stanford Tutorial:

Concepts in NN/Deep Learning:

  1. [Backpropagation (i.e. the chain rule)]( book), chapter 2

  2. Chris Olah on backprop

  3. Karpathy on backprop

Recurrent Neural Networks (RNN) (which mostly feature LSTM nowadays):

  1. Karpathy post

  2. Karpathy talk

  3. Excellent annotated Char-NN in Keras tutorial

  4. Andrew Trask post/tutorial

  5. Denny Britz post

  6. Class notes/tutorial (long!)

  7. CS class notes (short)

  8. Excellent post by Ross Goodwin RNNs

  9. Great List of references; TensorFlow

  1. Theano tutorial

  2. Batch Normalization to the hidden-to-hidden transitions of our RNNs

Traditional RNNs suffer from vanishing/exploding gradient. Hence LSTM & others…

Long Short-Term Memory (LSTM):

  1. Tutorial

  2. Chris Olah post

  3. Zach Lipton post, “Demystifying LSTM” (with Tutorial theano code)

  4. Demo: Lightweight Theano-LSTM

  5. Massive 33-page review article by Lipton et al

  6. LSTM tutorial in Tensorflow

  7. Stateful LSTM in Keras for time-series prediction

  8. Much-need Docs on stateful LSTM in Keras

  9. Tensorflow sequence prediction


Nice compilation


@ecdrid has posted a very extensive list consisting of the best material.
I find that Ng’s courses (ml, best complement the course. I still find myself going back to them. And use the deeplearning book as a reference.

Please Checkout this one too…