Machine Learning using Python - A Beginner's Guide (Udemy.com)

( 6 Reviews )

Created by: Shashidhar Hombal

Produced in 2021

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Overall Score : 90 / 100

Course 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:

Beginner to Machine Learning
Those willing to understand maths behind linear regression, logistic regression.

Time: 5.5h

Instructor Details

Shashidhar Hombal

4.5 Rating
6 Reviews

I have over 10+ years of experience as trainer for various BSS/OSS products. I primarily worked on multiple charging and billing products which includes both online charging and offline charging. I Also spent lot of time on working on other BSS/OSS products like Order management, Mediation and provisioning systems. I have experience working with multiple BSS/OSS vendors.

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4.5

6 total reviews

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By Chandra Mani Pandey

By Clay Siefken

Audio quality is quite low. It sounds like this was recorded in a tin can. Please use some of your income from this course to purchase a better microphone and/or some soundproofing materials for your next course.

By Rajesh

All the examples only with numeric values not by data oriented like Account number,Problemcode,Troublecall reason and serverity.

Need an examples like above then everyone gets to know how the machine learning playing the role in python.

Thanks for explanation its great place to learn basic concepts with mathematical explanation.

Good Work

Thanks,

By Shivanand Yadav

this is one of the very good tutorial i came across for beginners as well as experienced people, i can easily understand the concept what instructor is explaining. even though tutorial is for linear regression but basic of machine learning explained very well, instructor has add more and more algorithms like KNN,K-means,SVM naural networks, decision tree, random forest , it will become one of the best tutorial and also example data should be some real one like telecom records or health, or banking sector data

By Andhe Dharani