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DLS Course

Gentle Introduction into Machile Learning

2021

dls logo

Seminars and Lectures are available at appropriate folders


Extra materials

Prerequisite

Probability and statistics

Math statistics. Stepik course

Probability. Stepik course

MIPT.Statistics

Linear Algebra

Numerical Linear Algebra by FastAI

Numerical Linear Algebra by Skoltech

Matrix Cook Book. Formulas for Matrix Differentiation and much more

Basic Libraries for DS

Basic ones are: Numpy, Pandas, Sklearn, Matplotlib, Scipy. Check out their main sites - they are great and have a lot of tutorials

ScipyLecture Notes - it is unofficial site, but it has lot of information about each library

Numpy

Sklearn

Pandas

Matplotlib


Machine learning stuff (Great for beginners)

ODS. Open Data Science course

Coursera. Courses by Andrew NG

Coursera. Specialization by Yandex & MIPT


DeepLearning

Deep Learning Frameworks

Tensorflow. Deep Learning framework by Google

Pytorch. Deep Learning framework by Facebook (Meta). Arguably, the most popular one

JAX. Autograd framework by Google

Keras. High-level framework built on Tensorflow

Pytorch-lightning. High level framework built on Pytorch

Catalyst. High level framework built on Pytorch

Great bor begginers

Deep Learning School course

Machine Learning @ MIPT course

DeepLearningBook. Probably, the greatest book about deep learning

DiveIntoDeepLearning. Interactive book with lots of practice

Deep Learning @ NYU

CS231n. Computer Vision course @ Standford

CS224n. NLP course @ Standford

Advanced stuff

YSDA. NLP course

YSDA. CV course

YSDA. DL course

YSDA. RL course

Deep RL @ Berkley University


Full Stack Machine Learning Development

FullStackML course

MadeWithML course


Extra materials

ODS community. The biggest russian speaking community of ds&ml enthusiasts

The Batch. ML news forum

Papers with code. A great overview of the recent research in ml field

TowardsDataScience. Great blog-post about DS&ML (Friendly for beginners)


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Intro to ML. Intensive. 2021

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