- Are you interested to know more about Machine learning?
- Are you finding it difficult to design and analyze algorithms?

Introduction to Machine learning

Machine learning is an extensive subsection of artificial intelligence that studies the methods for constructing algorithms that can be trained. Machine learning is at the junction of mathematical statistics, optimization methods and classical mathematical disciplines, but also has its own specifics related to the problems of computational efficiency.

Who should take this course?

This course is beneficial for students – who want to learn effective and efficient ways and techniques (rarely available and discussed in the course books at any institution) to learn algorithms and for programmers – who are interested in developing the skill of designing and analyzing algorithms practically rather than mathematical derivations. Basic calculus is the pre requisite for this course in order to get a flying start. 

What are the benefits of the course?

This efficient, convenient and time-saving guide will help you:

- Gain familiarity with computer programming languages and algorithms
- Solve basic linear algebra such as matrices, vectors, matrix-vector multiplication etc.
- Understanding basic probability (random variables, basic properties of probability) is assumed.

Table of contents:

- Introduction

What is Machine Learning?
Supervised Learning Introduction
Unsupervised Learning Introduction
Installing Octave

- Linear Regression I

Supervised Learning Introduction
Model Representation
Cost Function
Gradient Descent
Gradient Descent for Linear Regression
Vectorized Implementation

- Linear Regression II

Feature Scaling
Learning Rate
Features and Polynomial Regression
Normal Equations

- Logistic Regression

Classification
Model
Optimization Objective
Gradient Descent
Newton's Method
Gradient Descent vs. Newton’s method

- Regularization

The Problem Of Overfitting
Optimization Objective
Common variations
Regularized Linear Regression

- Naive Bayes

Generative Learning Algorithms
Text classification

Author Bio:

This guide has been designed and prepared by renowned professors and lecturers of different universities and colleges across the United States of America.