Machine Learning RoadMap Part - I #61

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What is machine learning?

Machine learning is the study of computer algorithms that allow computer programs to improve automatically through experience.

Tom M. Mitchell


Why now?

Because it’s finally feasible and practicable. Exponentiation in the amount of data, as well as incredible hardware advances, has given life to machine learning ideas that once only lived on paper.


Learn Programming

Choose any programming languages (Go with python) and learn interactively. Also, learn CS fundamentals.

Resources

Cs50x by Harvard

Learnpython.org

Python crash course

Learn python the hard way

Pythonprogramming.net/

Check our 100daysofmcode


Linear algebra

Lot of ML Concept are tired up with linear algebra. It also helps us to understand concepts like PCA and also you can build algorithm from scratch.


Resources

Part 1 of deeplearningbbok.org

Math track of khan academy

mml-book.com

linear algebra by fast.ai

3blue1brwon

Check our 100daysimicode


Concept

Matrices

Eigenvectors & Eigenvalues.

Jacobin matrix

Gradient, Tensors

Curse of Dimensionality and matrix decomposition


Calculus and optimization

Calculus is very important concept if you want to understand how optimization works in ML and DL. It is important if your focus is on research side.


Resources 

3blue1brown

Khanacademy

Thomas calculus

Check our 100daysofmicode


Concepts

Differentiability, Maxima & minima, product & chain rule.

Gradient descent, SGD, mini DGD, Momentum, adage etc.


Statics and probability

Learn descriptive and inferential, associative, and differential

Statically concepts and also probability (conditional Probability and bay’s theorem).



Resources

Khan academy

Intro to statics by udacity

MIT OpenCoursewaare

Introduction to statically learning

Check our 100daysofmicod


Location: New Zealand

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