Machine Learning is one of the fastestgrowing fields in terms of job creation. Machine Learning comprises classical machine learning and deep learning. This field is also referred to as Artificial Intelligence (AI).
Machine Learning enables us to derive insight from data and build predictive models. Social media platforms use deep learning to serve content in your newsfeeds and recommend posts to watch. OTT platforms such as Netflix, Amazon Prime Video, Hotstar, Hulu, etc heavily leverages machine learning and deep learning to recommend content to users.
With the digitization and availability of lowcost internet, companies are producing terabytes of data every day and those data need to be analyzed to make business decisions or run their primary datarelated businesses.
Here in this discussion, we will go through how to learn Machine Learning and become an expert.
Basics skills required to learn Machine Learning
Machine Learning is a heavily mathdriven area, Building and implementing machine learning and deep learning algorithms requires a good understanding of maths.
One should have a good understanding of the following topics:

Linear Algebra

Probability

Optimization

Calculus
Linear Algebra
For processing or analyzing by computer, data are represented as a dense or sparse matrix. Major topics to understand in linear algebra are

Mathematical operations with matrices (addition, multiplication)

Matrix inverses and determinants

Solving systems of equations with matrices

Euclidean vector spaces

Eigenvalues and eigenvectors

Orthogonal matrices and orthogonal bases

Positive definite matrices

Linear transformations

Projections

Linear dependence and independence

Singular value decomposition

GramSchmidt process

Diagonalization of a matrix
Online course to learn linear algebra:
Probability
Probability is the foundation of any predictive modeling.
Some major topics are:

Conditional probability and Bayes theorem

Expectation and ExpectationMaximization Algorithm

Random variables

Discrete and Continuous Distributions and sampling

Stochastic convergence, the law of large numbers

Weak convergence, the CLT, Poisson process
Online course to learn Probability:
Optimization
Machine Learning problems such as classification, regression, segmentation, etc. all are optimization problems.
Understanding optimization theory will help you be an expert in Machine Learning.
Major topics in optimization are

Lagrangian and duality theory

Duality properties and applications for convex, nonconvex, and MDP

Block coordinate method and stochastic gradient

Convex Optimization

Nonconvex quadratic optimization
Online course to learn Optimization:
Calculus
Calculus is also a foundation of deep learning and optimization. Major topics in calculus are

Concepts of Function, Limits, and Continuity

Differentiation
Online course to learn Calculus:
Learning Machine Learning
The foundational theory of Machine Learning can be categorized into two subparts:

Classical machine learning

Deep Learning
To learn any machine learning or deep learning topic, you should follow the below steps:

Read the theory of the topic

Watch lecture videos

Implement using Python or Java
Implemention will help you understand any algorithm in depth.
Classical Machine Learning
Machine learning algorithms not developed using Neural networks or Deep neural networks are termed classical machine learning algorithms.
Usually, these algorithms don’t need too much data to learn.
Some classical machine learnings algorithms are:

SVM

Random Forest

Linear Regression

Logistic Regression

Principal Component Analysis

Decision tree

KNN

KMeans

XGboost

Naive Bayes
One of the BEST Lecture series to learn classical machine learning is taught by Yaser S. AbuMostafa, Caltech.
You can watch his lecture videos either on EdX or Youtube

Learning from Data (Youtube, Caltech)

Learning from Data (EdX Free Course)
Best reading resources:
Deep Learning
Deep learning algorithms are compute and dataheavy. For training deep learning models more data is better, and thousands of samples are required when training from scratch.
Any algorithm that uses a neural network (fully connected or convolutional) can be classified as a deep learning algorithm. Deep learning algorithms are optimized using backpropagation.
Some popular deep learningbased algorithms for image based applications are:

AlexNet (first deep network to revolutionize deep learning) [Classification]

Resnet [Classification]

EfficientNet [Classification]

Faster RCNN, Mask RCNN [Detection, Segmentation]

RetinaNet [Detection]

Yolo [Detection]

Deeplab [Segmentation]
Some popular deep learningbased algorithms for text applications are:

Transformer [Classifcation, Q/A, Pretraining]

BERT [Classification, Q/A, Translation etc]

GPT [Classification, Q/A, Translation etc]

FLAN [Classiciation, Q/A, Translation etc]
Best reading resources to learn Deep learning:
Best Courses to learn Deep learning:
Deep Learning frameworks

Tensorflow [Recommended]
We recommend Tensorflow for deep learning engineers. Tensorflow is a mature and stable framework developed and maintained by Google. Tensorflow is also productionready and scalable. Tensorflow supports for edge devices are inbuilt.
Tensorflow provides lots of learning resources to get started. Here are some useful TensorFlow tutorials