MP4 | Video: h264, 1280x720 | Audio: AAC, 48 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 44 lectures (5 hour, 18 mins) | Size: 1.91 GB

Spyder IDE, Python, SKlearn installed in the computer.

What you'll learn

Learn the Basics of Machine learning

Implement linear regression, polynomial regression, regularization, logistic regression using python from scratch and sklearn library

Linear Regression and mathematics behind linear regression

Polynomial regression and mathematics

Gradient descent technique

Ridge and Losso Regression

Bias - Variance Trade off and regularization

Logistic regression and mathematics behind logistic regression

Requirements

Basic Python

Basic Mathematical operations on matrix

Description

This course is for you if you are looking for the basics of machine learning.

If you want to know how to implement the linear regression, polynomial regression and logistic regression using python without using sklearn and understand these algorithms mathematically?

In this course you will learn the mathematics behind the linear regression, polynomial regression and logistic regression. Then you will implement these algorithms without using sklearn and using sklearn.

The course has the following topics

Section 1: Fundamentals of machine learning.

What is machine learning?,

When to use machine learning.

Supervised and unsupervised algorithms, Regression, classification and clustering

Section 2: Linear Regression

Linear Regression using normal equation

Implementing Simple linear regression, multiple linear regression using normal equation.

Model accuracy.

Implement linear regression using sklearn

Section 3: Linear regression using Gradient Descent

Explanation of Gradient descent and using the gradient descent to find the parameters.

Different types of gradient descent.

Python code for gradient descent without sklearn.

Python code for gradient descent using sklearn

Section 4: Polynomial regression

What is polynomial regression and when to use the polynomial regression.

Implement polynomial regression using python

Section 5: Bias and Variance

Understanding the bias and variance.

Effect of bias and variance on model accuracy.

Implementing regularisation to overcome variance.

Section 6: Logistic regression

What is logistic regression

Sigmoid function

Maximum likelihood estimation

Implementing gradient ascent to find the parameter values

Python code for logistic regression without sklearn

Python code for logistic regression with sklearn

Evaluating the model performance

Who this course is for:

Bner to Machine Learning

Those willing to understand maths behind linear regression, logistic regression.



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