Introduction to Course

Python Crash Course

➢ OPTIONAL: Python Crash Course

➢ 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

➢ Introduction to NumPy

➢ NumPy Arrays

➢ Coding Exercise Check-in: Creating NumPy Arrays

➢ NumPy Indexing and Selection

➢ Coding Exercise Check-in: Selecting Data from Numpy Array

➢ NumPy Operations

➢ Check-In: Operations on NumPy Array

➢ NumPy Exercises

➢ Numpy Exercises – Solutions

➢ Introduction to Pandas

➢ Series - Part One

➢ Check-in: Labeled Index in Pandas Series

➢ 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-Subplots Functionality

➢ 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

➢ A note from Jose on Feature Engineering and Data Preparation

➢ Introduction to Feature Engineering and Data Preparation

➢ Dealing with Outliers

➢ Dealing with Missing Data : Part One - Evaluation of Missing Data

➢ Dealing with Missing Data : Part Two - Filling or Dropping data based on Rows

➢ Dealing with Missing Data : Part 3 - Fixing data based on Columns

➢ Dealing with Categorical Data - Encoding Options

➢ Section Overview and Introduction

➢ Cross Validation - Test | Train Split

➢ Cross Validation - Test | Validation | Train Split

➢ Cross Validation - cross_val_score

➢ Cross Validation - cross_validate

➢ Grid Search

➢ Linear Regression Project Overview

➢ Linear Regression Project – Solutions

➢ Early Bird Note on Downloading .zip for Logistic Regression Notes

➢ Introduction to Logistic Regression Section

➢ Logistic Regression - Theory and Intuition - Part One: The Logistic Function

➢ Logistic Regression - Theory and Intuition - Part Two: Linear to Logistic

➢ Logistic Regression - Theory and Intuition - Linear to Logistic Math

➢ Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood

➢ Logistic Regression with Scikit-Learn - Part One - EDA

➢ Logistic Regression with Scikit-Learn - Part Two - Model Training

➢ Classification Metrics - Confusion Matrix and Accuracy

➢ Classification Metrics - Precison, Recall, F1-Score

➢ Classification Metrics - ROC Curves

➢ Logistic Regression with Scikit-Learn - Part Three - Performance Evaluation

➢ Multi-Class Classification with Logistic Regression - Part One - Data and EDA

➢ Multi-Class Classification with Logistic Regression - Part Two - Model

➢ Logistic Regression Exercise Project Overview

➢ Logistic Regression Project Exercise – Solutions

➢ Introduction to KNN Section

➢ KNN Classification - Theory and Intuition

➢ KNN Coding with Python - Part One

➢ KNN Coding with Python - Part Two - Choosing K

➢ KNN Classification Project Exercise Overview

➢ KNN Classification Project Exercise Solutions

➢ Introduction to Support Vector Machines

➢ History of Support Vector Machines

➢ SVM - Theory and Intuition - Hyperplanes and Margins

➢ SVM - Theory and Intuition - Kernel Intuition

➢ SVM - Theory and Intuition - Kernel Trick and Mathematics

➢ SVM with Scikit-Learn and Python - Classification Part One

➢ SVM with Scikit-Learn and Python - Classification Part Two

➢ SVM with Scikit-Learn and Python - Regression Tasks

➢ Support Vector Machine Project Overview

➢ Support Vector Machine Project Solutions

➢ Introduction to Tree Based Methods

➢ Decision Tree - History

➢ Decision Tree - Terminology

➢ Decision Tree - Understanding Gini Impurity

➢ Constructing Decision Trees with Gini Impurity - Part One

➢ Constructing Decision Trees with Gini Impurity - Part Two

➢ Coding Decision Trees - Part One - The Data

➢ Coding Decision Trees - Part Two -Creating the Model

➢ Introduction to Random Forests Section

➢ Random Forests - History and Motivation

➢ Random Forests - Key Hyperparameters

➢ Random Forests - Number of Estimators and Features in Subsets

➢ Random Forests - Bootstrapping and Out-of-Bag Error

➢ Coding Classification with Random Forest Classifier - Part One

➢ Coding Classification with Random Forest Classifier - Part Two

➢ Coding Regression with Random Forest Regressor - Part One - Data

➢ Coding Regression with Random Forest Regressor - Part Two - Basic Models

➢ Coding Regression with Random Forest Regressor - Part Three - Polynomials

➢ Coding Regression with Random Forest Regressor - Part Four - Advanced Models

➢ Introduction to Boosting Section

➢ Boosting Methods - Motivation and History

➢ AdaBoost Theory and Intuition

➢ AdaBoost Coding Part One - The Data

➢ AdaBoost Coding Part Two - The Model

➢ Gradient Boosting Theory

➢ Gradient Boosting Coding Walkthrough

➢ Introduction to Supervised Learning Capstone Project

➢ Solution Walkthrough - Supervised Learning Project - Data and EDA

➢ Solution Walkthrough - Supervised Learning Project - Cohort Analysis

➢ Solution Walkthrough - Supervised Learning Project - Tree Models

➢ Introduction to NLP and Naive Bayes Section

➢ Naive Bayes Algorithm - Part One - Bayes Theorem

➢ Naive Bayes Algorithm - Part Two - Model Algorithm

➢ Feature Extraction from Text - Part One - Theory and Intuition

➢ Feature Extraction from Text - Coding Count Vectorization Manually

➢ Feature Extraction from Text - Coding with Scikit-Learn

➢ Natural Language Processing - Classification of Text - Part One

➢ Natural Language Processing - Classification of Text - Part Two

➢ Text Classification Project Exercise Overview

➢ Text Classification Project Exercise Solutions

➢ Unsupervised Learning Overview

➢ Introduction to K-Means Clustering Section

➢ Clustering General Overview

➢ K-Means Clustering Theory

➢ K-Means Clustering - Coding Part One

➢ K-Means Clustering Coding Part Two

➢ K-Means Clustering Coding Part Three

➢ K-Means Color Quantization - Part One

➢ K-Means Color Quantization - Part Two

➢ K-Means Clustering Exercise Overview

➢ K-Means Clustering Exercise Solution - Part One

➢ K-Means Clustering Exercise Solution - Part Two

➢ K-Means Clustering Exercise Solution - Part Three

➢ Introduction to Hierarchical Clustering

➢ Hierarchical Clustering - Theory and Intuition

➢ Hierarchical Clustering - Coding Part One - Data and Visualization

➢ Hierarchical Clustering - Coding Part Two - Scikit-Learn

➢ Introduction to DBSCAN Section

➢ DBSCAN - Theory and Intuition

➢ DBSCAN versus K-Means Clustering

➢ DBSCAN - Hyperparameter Theory

➢ DBSCAN - Hyperparameter Tuning Methods

➢ DBSCAN - Outlier Project Exercise Overview

➢ DBSCAN - Outlier Project Exercise Solutions

➢ Introduction to Principal Component Analysis

➢ PCA Theory and Intuition - Part One

➢ PCA Theory and Intuition - Part Two

➢ PCA - Manual Implementation in Python

➢ PCA - SciKit-Learn

➢ PCA - Project Exercise Overview

➢ PCA - Project Exercise Solution

➢ Model Deployment Section Overview

➢ Model Deployment Considerations

➢ Model Persistence

➢ Model Deployment as an API - General Overview

➢ Note on Upcoming Video

➢ Model API - Creating the Script

➢ Testing the API

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