Python for Machine Learning & Data Science Masterclass 

Anaconda Python and Jupyter Install and Setup

Environment Setup

Python Crash Course - Part One

Python Crash Course - Part Two

Python Crash Course - Part Three

Python Crash Course - Exercise Questions

Python Crash Course - Exercise Solutions

Machine learning pathway

Introduction to NumPy

NumPy Arrays

NumPy Indexing and Selection

NumPy Operations

NumPy Exercises

Numpy Exercises - Solutions

Introduction to Pandas

Series - Part One

Series - Part Two

DataFrames - Part One - Creating a DataFrame

DataFrames - Part Two - Basic Properties

DataFrames - Part Three - Working with Columns

DataFrames - Part Four - Working with Rows

Pandas - Conditional Filtering

Pandas - Useful Methods - Apply on Single Column

Pandas - Useful Methods - Apply on Multiple Columns

Pandas - Useful Methods - Statistical Information and Sorting

Missing Data - Overview

Missing Data - Pandas Operations

GroupBy Operations - Part One

GroupBy Operations - Part Two - MultiIndex

Combining DataFrames - Concatenation

Combining DataFrames - Inner Merge

Combining DataFrames - Left and Right Merge

Combining DataFrames - Outer Merge

Pandas - Text Methods for String Data

Pandas - Time Methods for Date and Time Data

Pandas Input and Output - CSV Files

Pandas Input and Output - HTML Tables

Pandas Input and Output - Excel Files

Pandas Input and Output - SQL Databases

Pandas Pivot Tables

Pandas Project Exercise Overview

Pandas Project Exercise Solutions

Introduction to Matplotlib

Matplotlib Basics

Matplotlib - Understanding the Figure Object

Matplotlib - Implementing Figures and Axes

Matplotlib - Figure Parameters

Matplotlib Styling - Legends

Matplotlib Styling - Colors and Styles

Advanced Matplotlib Commands (Optional)

Matplotlib Exercise Questions Overview

Matplotlib Exercise Questions - Solutions

Introduction to Seaborn

Scatterplots with Seaborn

Distribution Plots - Part One - Understanding Plot Types

Distribution Plots - Part Two - Coding with Seaborn

Categorical Plots - Statistics within Categories - Understanding Plot Types

Categorical Plots - Statistics within Categories - Coding with Seaborn

Categorical Plots - Distributions within Categories - Understanding Plot Types

Categorical Plots - Distributions within Categories - Coding with Seaborn

Seaborn - Comparison Plots - Understanding the Plot Types

Seaborn - Comparison Plots - Coding with Seaborn

Seaborn Grid Plots

Seaborn - Matrix Plots

Seaborn Plot Exercises Overview

Seaborn Plot Exercises Solutions

Capstone Project Overview

Capstone Project Solutions - Part One

Capstone Project Solutions - Part Two

Capstone Project Solutions - Part Three

Introduction to Machine Learning Overview Section

Why Machine Learning?

Types of Machine Learning Algorithms

Supervised Machine Learning Process

Companion Book - Introduction to Statistical Learning

Introduction to Linear Regression Section

Linear Regression - Algorithm History

Linear Regression - Understanding Ordinary Least Squares

Linear Regression - Cost Functions

Linear Regression - Gradient Descent

Python coding Simple Linear Regression

Overview of Scikit-Learn and Python

Linear Regression - Scikit-Learn Train Test Split

Linear Regression - Scikit-Learn Performance Evaluation - Regression

Linear Regression - Residual Plots

Linear Regression - Model Deployment and Coefficient Interpretation

Polynomial Regression - Theory and Motivation

Polynomial Regression - Creating Polynomial Features

Polynomial Regression - Training and Evaluation

Bias Variance Trade-Off

Polynomial Regression - Choosing Degree of Polynomial

Polynomial Regression - Model Deployment

Regularization Overview

Feature Scaling

Introduction to Cross Validation

Regularization Data Setup

L2 Regularization - Ridge Regression Theory

L2 Regularization - Ridge Regression - Python Implementation

L1 Regularization - Lasso Regression - Background and Implementation

L1 and L2 Regularization - Elastic Net

Linear Regression Project - Data Overview