Machine Learning And Deep Learning
This Roadmap is divided into two major parts:
1 .Machine Learning 2. Deep Learning
Prerequisites
Before starting ML or DL you need some basic understnading of underlying topics : --
1.Linear Algebra
2.Calculus
3.Probability
4.Algorithms
5.Python , R or javascript (Depends on you)
Maths for ML
1.Linear Algebra
2.Calculus visual
3.Calculus Theory
4.Linear Algebra EBook
Note : you only need derivatives
5.Probability
Fameworks
1.Keras (Easy for beginners)
2.PyTorch
Learn PyTorch
3.Tensoflow
Learn Tensorflow
Machine Learning
Andrew Ng Coursera this highly recommended.
Andrew Ng Youtube alternative
Machine Learning Lectures- Stanford
Google ML Crash Course
Dive into Machine Learning
Deep Learning How Neural Networks Woks? Neural Network
Start Deep Learning Fast.ai provides you in depth knowledge of Neural Networks in a practical way without going to much detalis of maths behind of it.
Deep Learning Specialization by Coursera This is specialization consist of 5 courses :
1.Neural Networks and Deep Learning
2.Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
3.Structuring Machine Learning Projects
4.Convolutional Neural Networks
5.Sequence Models
NLP 1.Standford CS224N
SVM 1.MIT courseware
Blogs to Follow 1.Towards DataScience
2.christopher olah
3.andrew trask
AI Podcasts-- 1.Lex Fridman
Peoples to follow on Twitter- @ylecun
@rsalakhu
@karpathy
@hugo_larochelle
@goodfellow_ian
@drfeifei
@soumithchintala
@nandodf
@jeffdean