Over 200 of the Best Machine Learning, NLP, and Python Tutorials — 2018 Edition

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Last year, that was pretty popular (161K reads in Medium), listing the best tutorials I found while digging into a number of machine learning topics. Thirteen months later and now there are dozens of new tutorials on both traditional machine learning concepts as well as the cutting-edge techniques that have emerged over the past year. The sheer volume of content that continues to be created around machine learning is staggering.


The article contains the best tutorial content that I’ve found so far. It’s by no means an exhaustive list of every ML-related tutorial on the web — that would be overwhelming and duplicative. Plus, there is a bunch of mediocre content out there. My goal was to link to the best tutorials I found on the important subtopics within machine learning and NLP.

By tutorial, I’m referring to introductory content that is intending to teach a concept succinctly. I’ve avoided including chapters of books, which have a greater breadth of coverage, and research papers, which generally don’t do a good job in teaching concepts. Why not just buy a book? Tutorials are helpful when you’re trying to learn a specific niche topic or want to get different perspectives.

I’ve split this post into four sections: Machine Learning, NLP, Python, and Math. I’ve included a sampling of topics within each section, but given the vastness of the material, I can’t possibly include every possible topic.

If there are good tutorials you are aware of that I’m missing, please let me know! I’m trying to limit each topic to five or six tutorials since much beyond that would be repetitive. Each link should have different material from the other links or present information in a different way (e.g. code versus slides versus long-form) or from a different perspective.

Machine Learning

(machinelearningmastery.com)

(medium.com/@ageitgey)

(martin.zinkevich.org)

Machine Learning Crash Course: , , (Machine Learning at Berkeley)

(toptal.com)

(monkeylearn.com)

(sas.com)

(sas.com)

(kaggle.com/kanncaa1)

Activation and Loss Functions

(neuralnetworksanddeeplearning.com)

(quora.com)

(stats.stackexchange.com)

(medium.com)

(exegetic.biz)

(Stanford CS231n)

(rishy.github.io)

(neuralnetworksanddeeplearning.com)

Bias

(stackoverflow.com)

(makeyourownneuralnetwork.blogspot.com)

(quora.com)

Perceptron

(neuralnetworksanddeeplearning.com)

(natureofcode.com)

(dcu.ie)

(toptal.com)

Regression

(duke.edu)

(ufldl.stanford.edu)

(readthedocs.io)

(readthedocs.io)

(machinelearningmastery.com)

(machinelearningmastery.com)

(ufldl.stanford.edu)

Gradient Descent

(neuralnetworksanddeeplearning.com)

(iamtrask.github.io)

(kdnuggets.com)

(sebastianruder.com)

(Stanford CS231n)

Generative Learning

(Stanford CS229)

(monkeylearn.com)

Support Vector Machines

(monkeylearn.com)

(Stanford CS229)

(Stanford 231n)

Backpropagation

(medium.com/@karpathy)

(github.com/rasbt)

(neuralnetworksanddeeplearning.com)

(wildml.com)

(machinelearningmastery.com)

(Stanford CS231n)

Deep Learning

(yerevann.com)

(github.com/floodsung)

(nikhilbuduma.com)

(Quoc V. Le)

(machinelearningmastery.com)

(nvidia.com)

(gluon.mxnet.io)

Optimization and Dimensionality Reduction

(knime.org)

(Stanford CS229)

(Hinton @ NIPS 2012)

(rishy.github.io)

Long Short Term Memory (LSTM)

(machinelearningmastery.com)

(colah.github.io)

(echen.me)

(iamtrask.github.io)

Convolutional Neural Networks (CNNs)

(neuralnetworksanddeeplearning.com)

(medium.com/@ageitgey)

(colah.github.io)

(colah.github.io)

Recurrent Neural Nets (RNNs)

(wildml.com)

(distill.pub)

(karpathy.github.io)

(nikhilbuduma.com)

Reinforcement Learning

(analyticsvidhya.com)

(mst.edu)

(wildml.com)

(karpathy.github.io)

Generative Adversarial Networks (GANs)

(aaai18adversarial.github.io)

(nvidia.com)

(medium.com/@ageitgey)

(aylien.com)

(oreilly.com)

Multi-task Learning

(sebastianruder.com)

NLP

(medium.com/@ageitgey)

(Yoav Goldberg)

(monkeylearn.com)

(algorithmia.com)

(vikparuchuri.com)

(arxiv.org)

Deep Learning and NLP

(arxiv.org)

(Richard Socher)

(wildml.com)

(colah.github.io)

(explosion.ai)

(nvidia.com)

(pytorich.org)

Word Vectors

(kaggle.com)

On word embeddings , , (sebastianruder.com)

(acolyer.org)

(arxiv.org)

Word2Vec Tutorial — , (mccormickml.com)

Encoder-Decoder

(wildml.com)

(tensorflow.org)

(NIPS 2014)

(medium.com/@ageitgey)

(machinelearningmastery.com)

(google.github.io)

Python

(google.com)

(github.com/josephmisiti)

(kdnuggets.com)

(nbviewer.jupyter.org)

(tutorialspoint.com)

Examples

(machinelearningmastery.com)

(wildml.com)

(iamtrask.github.io)

(kdnuggets.com)

(github.com/eriklindernoren)

(github.com/rasbt)

Scipy and numpy

(scipy-lectures.org)

(Stanford CS231n)

(UCSB CHE210D)

(nbviewer.jupyter.org)

scikit-learn

(nbviewer.jupyter.org)

(github.com/mmmayo13)

(scikit-learn.org)

(github.com/mmmayo13)

Tensorflow

(tensorflow.org)

(medium.com/@erikhallstrm)

(metaflow.fr)

(wildml.com)

(wildml.com)

(surmenok.com)

PyTorch

(pytorch.org)

(gaurav.im)

(iamtrask.github.io)

(github.com/jcjohnson)

(github.com/MorvanZhou)

(github.com/yunjey)

Math

(ucsc.edu)

(UMIACS CMSC422)

Linear algebra

(betterexplained.com)

(betterexplained.com)

(betterexplained.com)

(betterexplained.com)

(U. of Buffalo CSE574)

(medium.com)

(Stanford CS229)

Probability

(betterexplained.com)

(Stanford CS229)

(Stanford CS229)

(U. of Buffalo CSE574)

(U. of Toronto CSC411)

Calculus

(betterexplained.com)

(betterexplained.com)

(betterexplained.com)

(Stanford CS224n)

(readthedocs.io)


For more on machine learning, visit .

Machine Learning in Practice

Practical insights for executives, managers, and project managers eager to deploy machine learning inside their company.

Robbie Allen

Written by

CEO @InfiniaML, Exec Chairman @Ainsights, Lecturer at @kenanflagler, Ph.D. Student @UNCCS, Writing a book: http://machinelearninginpractice.com

Machine Learning in Practice

Practical insights for executives, managers, and project managers eager to deploy machine learning inside their company.