Machine Learning

• Introduction to Machine Learning​
• Machine Learning with Python

Linear Regrassion

Linear Regrassion

• Linear Regression
• Linear Regression Theory
• Model Selection Updates for Scikit Learn
• Linear Regression with Python
• Linear Regression Project Overview and Project Solution

Generalised Linear Models In Python

• Linear Regression
• Regularisation of Generalised Linear Models
• Ridge and Lasso Regression
• Methods of threshold determination and performance measures for classification score models
• Regularisation of Generalised Linear Models

K Nearest neighbours

K Nearest neighbours

• KNN Theory
• KNN with Python
• KNN Project Overview and Project Solutions
• Broadcasting

Decision Trees and Random Forests

• Introduction to Tree Methods
• Decision Trees and Random Forest Project Overview
• Decision Tree and Random Forest Solution Part-1
• Decision Tree and Random Forest Solution Part-2

K Means Clustering

• K means Algorithm Theory
• K Means with Python
• K Means Project Overview
• K Means Project Solutions

Machine Learning Basics

• Converting business problems to data problems
• Understanding supervised and unsupervised learning with examples
• Understanding bias associated with any machine learning algorithm
• Ways of reducing bias and in creasing generalisation capabilities
• Drivers of machine learning algorithm
• Cost functions
• List Brief introduction to gradient descent
• Importance of model validation
• Methods of model validation
• Testing Tool Pylint in Python
• Cross validation & average error

Logistic Regrassion

• Logistic Regression Theory
• Logistic Regression with Python-1
• Logistic Regression with Python-2
• Logistic Regression with Python-3

1 Project

2 Project

3 Project