Machine Learning with Scikit-Learn Pathway

• ML Framework

Learn the correct conceptual frameworks to make sure your machine learning algorithms are applied correctly to your data sets.

• Predictive Models

Use Scikit-Learn's power supervised machine learning algorithms to create predictive models using real world data sets.

• Real World Case Studies

We use real world data sets to conduct project exercise based case students, so you learn how to best apply the theory learned in the course.

Course curriculum

• 1

Introduction to Machine Learning Pathway with Scikit-Learn

• Introduction to Machine Learning Pathway

• NOTE: Installing Python, Anaconda, and Jupyter Notebooks

• OPTIONAL: Installing Python, Anaconda, and Jupyter Notebooks

• 2

Machine Learning Overview

• Introduction to Machine Learning Overview

• Why Machine Learning?

• Types of Machine Learning Algorithms

• Supervised Learning Process

• Supervised Learning Process

• Introduction to Statistical Learning Book (ISLR)

• 3

Introduction to Linear Regression

• Introduction to Linear Regression

• History and Motivation

• Ordinary Least Squares Theory

• Cost Function Theory

• Coding Simply Linear Regression

• Scikit-Learn Overview

• Linear Regression with Scikit-Learn - Train | Test Splits and Training

• Linear Regression with Scikit-Learn - Performance Evaluation

• Linear Regression with Scikit-Learn - Residual Plots

• Linear Regression with Scikit-Learn - Coefficients and Deployment

• 4

Polynomial Regression

• Polynomial Regression - Motivation

• Polynomial Regression - Creating Polynomial Feature Set

• Polynomial Regression - Training and Evaluating Performance

• Polynomial Regression- Choosing Polynomial Order

• Polynomial Regression - Model Deployment

• 5

Regularization Methods (Ridge, Lasso, Elastic Net)

• Regularization Overview

• Feature Scaling

• Cross Validation

• Regularization - Data Setup

• Ridge Regression - Theory

• Ridge Regression - Implementation with Python and Scikit-Learn

• Lasso Regression

• Elastic Net

• Data Set Overview

• 6

Feature Engineering

• Introduction to Feature Engineering

• Dealing with Outliers

• Working with Missing Data - Part One - Evaluating Missing Data

• Working with Missing Data - Part Two - Filling Data for Rows

• Working with Missing Data - Part Three - Filling Data for Columns

• Working with Categorical Data

• 7

Cross Validation and Linear Regression Project

• Introduction to Cross Validation

• Train Test Splits

• Train Test Validation Splits

• Scikit-Learn's cross_val_score Function

• Scikit-Learn's cross_validate Function

• GridSearchCV with Scikit-Learn

• 8

Linear Regression Capstone Project

• Linear Regression Project Overview

• Linear Regression Project Solutions

• 9

Logistic Regression

• Introduction to Logistic Regression

• Logistic Regression Theory - Part One - The Logistic Regression

• Logistic Regression Theory - Part Two - Linear to Logistic Regression

• Logistic Regression Theory - Part Three - Coefficients

• Logistic Regression Theory - Part Four - Maximum Likelihood

• Logistic Regression with Python and Scikit-Learn - Part One

• Logistic Regression with Python and Scikit-Learn - Part Two

• Classification Metrics - Part One - Accuracy

• Classification Metrics - Part Two - Precision and Recall

• ROC Curves

• Logistic Regression Metrics with Python and Scikit-Learn

• Multi-class Classification with Logistic Regression Part One

• Multi-class Classification with Logistic Regression Part Two

• Logistic Regression Exercise Overview

• Logistic Regression Exercise Project Solution

• 10

KNN - K Nearest Neighbors

• Introduction to Machine Learning Overview

• KNN Theory

• Coding KNN with Scikit-Learn - Part One

• Coding KNN with Scikit-Learn - Part Two

• KNN Exercise Project

• KNN Exercise Project -Solutions

• 11

SVM - Support Vector Machines

• Introduction to Support Vector Machines

• History of Support Vector Machines

• SVM Theory - Hyperplanes and Margins

• SVM Theory - Kernel Intuition

• SVM Theory - Kernel Trick Mathematics

• SVM with Python and Scikit-Learn Classification Part One

• SVM with Python and Scikit-Learn Classification Part Two

• Support Vector Regression

• SVM Project Exercise

• SVM Project Exercise Solutions

• 12

Tree Based Methods - Decision Trees

• Introduction to Tree Based Methods

• Decision Tree Theory - History

• Decision Tree Theory - Terminology

• Decision Tree Theory - Gini Impurity

• Decision Tree Theory - Gini Impurity in Trees Part One

• Decision Tree Theory - Gini Impurity in Trees Part Two

• Decision Trees with Scikit-Learn Part One

• Decision Trees with Scikit-Learn Part Two

• 13

Tree Based Methods - Random Forests

• Introduction to Random Forests

• Random Forest Theory - History and Motivation

• Random Forest Theory - Hyperparameters Overview

• Random Forest Theory - Hyperparameters - Number of Estimators and Features

• Random Forest Theory - Hyperparameters - Bootstrapping

• Random Forest - Coding Classification with Scikit-Learn Part One

• Random Forest - Coding Classification with Scikit-Learn Part Two

• Random Forest Regression Overview

• Random Forest Regression - Coding with Scikit-Learn Part One

• Random Forest Regression - Coding with Scikit-Learn Part Two

• Random Forest Regression - Coding with Scikit-Learn Part Three

• 14

Tree Based Methods - Boosting

• Introduction to Boosting

• Boosting Theory - History and Motivation

• AdaBoost - Coding with Scikit-Learn Part One

• AdaBoost - Coding with Scikit-Learn Part Two

• Coding Gradient Boosting with Scikit-Learn

• 15

Supervised Learning Capstone Project

• Introduction to Supervised Learning Project

• Solution Walkthrough - Part One- Data and EDA

• Solution Walkthrough - Part Two - Cohort Analysis

• Solution Walkthrough - Part Three - Model

• 16

Naive Bayes and Natural Language Processing

• Introduction to Naive Bayes and NLP

• Naive Bayes - Part One - Bayes' Theorem

• Naive Bayes - Part Two

• Feature Extraction - Theory and Intuition

• Feature Extraction - Coding Part One - Manual Example

• Feature Extraction - Coding Part Two - Scikit-Learn

• Text Classification Example - Part One

• Text Classification Example - Part Two

• Text Classification Project Overview

• Text Classification Project Exercise Solution

• 17

Unsupervised Learning Overview

• Introduction to Unsupervised Learning

• 18

K-Means Clustering

• Introduction to K-Means Clustering

• 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 Clustering - Color Quantization - Part One

• K-Means Clustering - Color Quantization - Part Two

• K-Means Clustering Exercise Project Overview

• K-Means Exercise Project Solutions - Part One

• K-Means Exercise Project Solutions - Part Two

• K-Means Exercise Project Solutions - Part Three

• 19

Hierarchical Clustering

• Introduction to Hierarchical Clustering

• Hierarchical Clustering Theory

• Hierarchical Clustering Coding Example - Part One

• Hierarchical Clustering Coding Example - Part Two

• 20

DBSCAN - Density-Based Spatial Clustering of Applications with Noise

• Introduction to DBSCAN

• DBSCAN - Intuition and Theory

• DBSCAN vs. K-Means Clustering

• DBSCAN Hyperparameters - Theory

• DBSCAN Hyperparameter Search

• DBSCAN - Exercise Project

• DBSCAN - Exercise Project - Solutions

• 21

PCA - Principal Component Analysis

• Introduction to PCA

• PCA Theory - Part One

• PCA Theory - Part Two

• PCA - Manual Coding Implementation

• PCA - Scikit-Learn Implementation

• PCA - Exercise Project Overview

• PCA - Exercise Project Solutions

• 22

Model Deployment

• Introduction to Model Deployment

• Model Deployment - General Concepts

• Model Persistence

• Model Deployment API - Part One - General Overview

• Model Deployment API - Part Two - Creating API Script

• Model Deployment API - Part Three - Testing the API