# Download Machine Learning using Python - A Beginner's Guide Course By Udemy

Machine learning basics, mathematically learn algorithms, algorithms using python from scratch and sklearn.

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

- Basic Python
- Basic Mathematical operations on matrix
- Spyder IDE, Python, SKlearn installed in the computer.

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

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

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