Pierian Data

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)

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

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

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

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

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

Linear Regression Project Overview

Linear Regression Project Solutions

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

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

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

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

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

Introduction to Boosting

Boosting Theory - History and Motivation

Adaptive Boosting Theory - AdaBoost

AdaBoost - Coding with Scikit-Learn Part One

AdaBoost - Coding with Scikit-Learn Part Two

Gradient Boosting Theory

Coding Gradient Boosting with Scikit-Learn

Introduction to Supervised Learning Project

Solution Walkthrough - Part One- Data and EDA

Solution Walkthrough - Part Two - Cohort Analysis

Solution Walkthrough - Part Three - Model

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

Introduction to Unsupervised Learning

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

Introduction to Hierarchical Clustering

Hierarchical Clustering Theory

Hierarchical Clustering Coding Example - Part One

Hierarchical Clustering Coding Example - Part Two

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

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

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