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.
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.
Courses : 3
Specification: Machine Learning using Python – A Beginner’s Guide
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