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.
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
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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