Shashidhar Hombal
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.
Instructor Details
Courses : 3
Specification: Machine Learning using Python – A Beginner’s Guide
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6 reviews for Machine Learning using Python – A Beginner’s Guide
Add a review
| Price | $12.99 |
|---|---|
| Provider | |
| Duration | 5.5 hours |
| Year | 2019 |
| Level | All |
| Language | English |
| Certificate | Yes |
| Quizzes | No |
$12.99
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,
Chandra Mani Pandey –
<3 Direct to the point
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
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.
Andhe Dharani –
Yes was interesting
Rishabh Mehra –
Yes I like the course.