## 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

• Gradient Descent 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

• Bias Variance Trade-Off

• 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

• Gradient Boosting Theory

• 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