#### Section 1 : Introduction to Course

 Lecture 1 EARLY BIRD INFO Pdf Lecture 2 COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP! copy 3:36 Lecture 3 Anaconda Python and Jupyter Install and Setup 13:43 Lecture 4 Note on Environment Setup - Please read me! Text Lecture 5 Environment Setup

#### Section 2 : OPTIONAL Python Crash Course

 Lecture 6 OPTIONAL Python Crash Course Text Lecture 7 Python Crash Course - Part One 15:54 Lecture 8 Python Crash Course - Part Two Lecture 9 Python Crash Course - Part Three 11:12 Lecture 10 Python Crash Course - Exercise Questions 1:22 Lecture 11 Python Crash Course - Exercise Solutions 9:16

#### Section 3 : Machine Learning Pathway Overview

 Lecture 12 Machine Learning Pathway 10:8

#### Section 4 : NumPy

 Lecture 13 Introduction to NumPy 2:6 Lecture 14 NumPy Arrays 20:46 Lecture 15 NumPy Indexing and Selection 10:56 Lecture 16 NumPy Operations 8:5 Lecture 17 NumPy Exercises 1:7 Lecture 18 Numpy Exercises - Solutions 6:58

#### Section 5 : Pandas

 Lecture 19 Introduction to Pandas 4:33 Lecture 20 Series - Part One 9:20 Lecture 21 Series - Part Two 10:33 Lecture 22 DataFrames - Part One - Creating a DataFrame 19:19 Lecture 23 DataFrames - Part Two - Basic Properties 8:1 Lecture 24 DataFrames - Part Three - Working with Columns 13:49 Lecture 25 DataFrames - Part Four - Working with Rows 14:22 Lecture 26 Pandas - Conditional Filtering 17:35 Lecture 27 Pandas - Useful Methods - Apply on Single Column 13:38 Lecture 28 Pandas - Useful Methods - Apply on Multiple Columns 17:16 Lecture 29 Pandas - Useful Methods - Statistical Information and Sorting 15:40 Lecture 30 Missing Data - Overview 11:50 Lecture 31 Missing Data - Pandas Operations 18:26 Lecture 32 GroupBy Operations - Part One 15:39 Lecture 33 GroupBy Operations - Part Two - MultiIndex 14:6 Lecture 34 Combining DataFrames - Concatenation Pdf Lecture 35 Combining DataFrames - Inner Merge 11:55 Lecture 36 Combining DataFrames - Left and Right Merge 6:0 Lecture 37 Combining DataFrames - Outer Merge 10:32 Lecture 38 Pandas - Text Methods for String Data 15:57 Lecture 39 Pandas - Time Methods for Date and Time Data 20:54 Lecture 40 Pandas Input and Output - CSV Files 10:12 Lecture 41 Pandas Input and Output - HTML Tables 14:34 Lecture 42 Pandas Input and Output - Excel Files 7:12 Lecture 43 Pandas Input and Output - SQL Databases 18:13 Lecture 44 Pandas Pivot Tables 21:9 Lecture 45 Pandas Project Exercise Overview 5:19 Lecture 46 Pandas Project Exercise Solutions 26:25

#### Section 6 : Matplotlib

 Lecture 47 Introduction to Matplotlib 3:59 Lecture 48 Matplotlib Basics 12:26 Lecture 49 Matplotlib - Understanding the Figure Object 7:24 Lecture 50 Matplotlib - Implementing Figures and Axes 14:22 Lecture 51 Matplotlib - Figure Parameters 4:47 Lecture 52 Matplotlib - Subplots Functionality 19:7 Lecture 53 Matplotlib Styling - Legends 6:55 Lecture 54 Matplotlib Styling - Colors and Styles 14:21 Lecture 55 Advanced Matplotlib Commands (Optional) 3:42 Lecture 56 Matplotlib Exercise Questions Overview 5:34 Lecture 57 Matplotlib Exercise Questions - Solutions 16:25

#### Section 7 : Seaborn Data Visualizations

 Lecture 58 Introduction to Seaborn 3:48 Lecture 59 Scatterplots with Seaborn 18:10 Lecture 60 Distribution Plots - Part One - Understanding Plot Types 9:28 Lecture 61 Distribution Plots - Part Two - Coding with Seaborn 16:7 Lecture 62 Categorical Plots - Statistics within Categories - Understanding Plot Types 5:32 Lecture 63 Categorical Plots - Statistics within Categories - Coding with Seaborn 9:7 Lecture 64 Categorical Plots - Distributions within Categories - Understanding Plot Types 13:12 Lecture 65 Categorical Plots - Distributions within Categories - Coding with Seaborn 17:45 Lecture 66 Seaborn - Comparison Plots - Understanding the Plot Types 5:24 Lecture 67 Seaborn - Comparison Plots - Coding with Seaborn 9:41 Lecture 68 Seaborn Grid Plots 13:31 Lecture 69 Seaborn - Matrix Plots 13:10 Lecture 70 Seaborn Plot Exercises Solutions 14:10 Lecture 71 Seaborn Plot Exercises Overview

#### Section 8 : Data Analysis and Visualization Capstone Project Exercise

 Lecture 72 Capstone Project Overview 12:35 Lecture 73 Capstone Project Solutions - Part One 17:7 Lecture 74 Capstone Project Solutions - Part Two 14:41 Lecture 75 Capstone Project Solutions - Part Three 19:38

#### Section 9 : Machine Learning Concepts Overview

 Lecture 76 Introduction to Machine Learning Overview Section 5:3 Lecture 77 Why Machine Learning 9:6 Lecture 78 Types of Machine Learning Algorithms 7:39 Lecture 79 Supervised Machine Learning Process 13:32 Lecture 80 Companion Book - Introduction to Statistical Learning 2:42

#### Section 10 : Linear Regression

 Lecture 81 Introduction to Linear Regression Section 1:34 Lecture 82 Linear Regression - Algorithm History 9:14 Lecture 83 Linear Regression - Understanding Ordinary Least Squares 15:37 Lecture 84 Linear Regression - Cost Functions 8:4 Lecture 85 Linear Regression - Gradient Descent 11:50 Lecture 86 Python coding Simple Linear Regression 19:25 Lecture 87 Overview of Scikit-Learn and Python 8:17 Lecture 88 Linear Regression - Scikit-Learn Train Test Split 15:41 Lecture 89 Linear Regression - Scikit-Learn Performance Evaluation - Regression 15:36 Lecture 90 Linear Regression - Residual Plots 13:48 Lecture 91 Linear Regression - Model Deployment and Coefficient Interpretation 17:36 Lecture 92 Polynomial Regression - Theory and Motivation 7:53 Lecture 93 Polynomial Regression - Creating Polynomial Features 10:49 Lecture 94 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf Lecture 95 Bias Variance Trade-Off 10:29 Lecture 96 Polynomial Regression - Choosing Degree of Polynomial 13:29 Lecture 97 Polynomial Regression - Model Deployment 5:59 Lecture 98 Regularization Overview 6:29 Lecture 99 Feature Scaling 9:49 Lecture 100 Introduction to Cross Validation 12:38 Lecture 101 Regularization Data Setup 8:29 Lecture 102 Regularization - Ridge Regression Theory 14:30 Lecture 103 Regularization Ridge Regression 17:33 Lecture 104 Regularization - Lasso Regression - Background and Implementation 14:50 Lecture 105 Regularization - Elastic Net 17:59 Lecture 106 Linear Regression Project - Data Overview 4:21

#### Section 11 : Feature Engineering and Data Preparation

 Lecture 107 A note from Jose on Feature Engineering and Data Preparation Text Lecture 108 Introduction to Feature Engineering and Data Preparation 15:20 Lecture 109 Dealing with Outliers 26:23 Lecture 110 Dealing with Missing Data Part One - Evaluation of Missing Data 10:36 Lecture 111 Dealing with Missing Data Part Two - Filling or Dropping data based on Rows 20:34 Lecture 112 Dealing with Missing Data Part 3 - Fixing data based on Columns 23:8 Lecture 113 Dealing with Categorical Data - Encoding Options 12:41

#### Section 12 : Cross Validation , Grid Search, and the Linear Regression Project

 Lecture 114 Section Overview and Introduction 3:7 Lecture 115 Cross Validation - Test Train Split 11:12 Lecture 116 Cross Validation - Test Validation Train Split 14:37 Lecture 117 Cross Validation - cross_val_score 11:32 Lecture 118 Cross Validation - cross_validate 6:48 Lecture 119 Grid Search 12:6 Lecture 120 Linear Regression Project Overview 2:40 Lecture 121 Linear Regression Project - Solutions 12:0

#### Section 13 : Logistic Regression

 Lecture 122 Early Bird Note on Downloading Pdf Lecture 123 Introduction to Logistic Regression Section 5:18 Lecture 124 Logistic Regression - Theory and Intuition - Part One The Logistic Function 5:29 Lecture 125 Logistic Regression - Theory and Intuition - Part Two Linear to Logistic 4:46 Lecture 126 Logistic Regression - Theory and Intuition - Linear to Logistic Math 16:54 Lecture 127 Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood 15:36 Lecture 128 Logistic Regression with Scikit-Learn - Part One - EDA Lecture 129 Logistic Regression with Scikit-Learn - Part Two - Model Training 6:31 Lecture 130 Classification Metrics - Confusion Matrix and Accuracy 9:38 Lecture 131 Classification Metrics - Precison, Recall, F1-Score 5:51 Lecture 132 Classification Metrics - ROC Curves 7:6 Lecture 133 Logistic Regression with Scikit-Learn - Part Three - Performance Evaluation 15:51 Lecture 134 Multi-Class Classification with Logistic Regression - Part One - Data and EDA 7:59 Lecture 135 Multi-Class Classification with Logistic Regression - Part Two - Model 15:41 Lecture 136 Logistic Regression Exercise Project Overview 3:51 Lecture 137 Logistic Regression Project Exercise - Solutions 21:17

#### Section 14 : KNN - K Nearest Neighbors

 Lecture 138 Introduction to KNN Section 2:3 Lecture 139 KNN Classification - Theory and Intuition 11:11 Lecture 140 KNN Coding with Python - Part One 13:32 Lecture 141 KNN Coding with Python - Part Two - Choosing K 23:16 Lecture 142 KNN Classification Project Exercise Overview 3:9 Lecture 143 KNN Classification Project Exercise Solutions 14:5

#### Section 15 : Support Vector Machines

 Lecture 144 Introduction to Support Vector Machines 1:24 Lecture 145 History of Support Vector Machines 4:34 Lecture 146 SVM - Theory and Intuition - Hyperplanes and Margins 13:17 Lecture 147 SVM - Theory and Intuition - Kernel Intuition 4:51 Lecture 148 SVM - Theory and Intuition - Kernel Trick and Mathematics 20:42 Lecture 149 SVM with Scikit-Learn and Python - Classification Part One 10:52 Lecture 150 SVM with Scikit-Learn and Python - Classification Part Two 15:57 Lecture 151 SVM with Scikit-Learn and Python - Regression Tasks 20:52 Lecture 152 Support Vector Machine Project Overview 4:20 Lecture 153 Support Vector Machine Project Solutions 18:24

#### Section 16 : Tree Based Methods Decision Tree Learning

 Lecture 154 Introduction to Tree Based Methods 1:16 Lecture 155 Decision Tree - History 8:58 Lecture 156 Decision Tree - Terminology 4:5 Lecture 157 Decision Tree - Understanding Gini Impurity 7:46 Lecture 158 Constructing Decision Trees with Gini Impurity - Part One 7:26 Lecture 159 Constructing Decision Trees with Gini Impurity - Part Two 11:17 Lecture 160 Coding Decision Trees - Part One - The Data 19:10 Lecture 161 Coding Decision Trees - Part Two -Creating the Model 20:50

#### Section 17 : Random Forests

 Lecture 162 Introduction to Random Forests Section 1:44 Lecture 163 Random Forests - History and Motivation 11:38 Lecture 164 Random Forests - Key Hyperparameters 3:0 Lecture 165 Random Forests - Number of Estimators and Fea 10:53 Lecture 166 Random Forests - Bootstrapping and Out-of-Bag 12:48 Lecture 167 Coding Classification with Random Forest Clas 12:58 Lecture 168 Coding Classification with Random Forest Clas 23:35 Lecture 169 Coding Regression with Random Forest Regresso 4:31 Lecture 170 Coding Regression with Random Forest Regresso Lecture 171 Coding Regression with Random Forest Regresso 21:8 Lecture 172 Coding Regression with Random Forest Regresso 11:0

#### Section 18 : Boosting Methods

 Lecture 173 Introduction to Boosting Section 1:59 Lecture 174 Boosting Methods - Motivation and History 6:9 Lecture 175 AdaBoost Theory and Intuition 19:53 Lecture 176 AdaBoost Coding Part One - The Data 11:11 Lecture 177 AdaBoost Coding Part Two - The Model 18:18 Lecture 178 Gradient Boosting Theory 12:33 Lecture 179 Gradient Boosting Coding Walkthrough 16:19

#### Section 19 : Supervised Learning Capstone Project

 Lecture 180 Introduction to Supervised Learning Capstone 15:50 Lecture 181 Solution Walkthrough - Supervised Learning Pr 23:2 Lecture 182 Solution Walkthrough - Supervised Learning Pr 31:5 Lecture 183 Solution Walkthrough - Supervised Learning 24:58

#### Section 20 : Naive Bayes Classification and Natural Language Processing

 Lecture 184 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf Lecture 185 About Proctor Testing Pdf Lecture 186 Naive Bayes Algorithm - Part Two - Model Algo 19:54 Lecture 187 Feature Extraction from Text - Part One - The 13:17 Lecture 188 Feature Extraction from Text - Coding Count V 23:7 Lecture 189 Feature Extraction from Text - Coding with Sc 19:24 Lecture 190 Natural Language Processing - Classification 17:20 Lecture 191 Natural Language Processing - Classification 12:53 Lecture 192 Text Classification Project Exercise Overview 7:9 Lecture 193 Text Classification Project Exercise Solution 21:52

#### Section 21 : Unsupervised Learning

 Lecture 194 Unsupervised Learning Overview 11:13

#### Section 22 : K-Means Clustering

 Lecture 195 Introduction to K-Means Clustering Section 3:2 Lecture 196 Clustering General Overview 12:5 Lecture 197 K-Means Clustering Theory 11:29 Lecture 198 K-Means Clustering - Coding Part One 33:57 Lecture 199 K-Means Clustering Coding Part Two 18:1 Lecture 200 K-Means Clustering Coding Part Three 17:28 Lecture 201 K-Means Color Quantization - Part One 14:16 Lecture 202 K-Means Color Quantization - Part Two 15:21 Lecture 203 K-Means Clustering Exercise Overview 11:40 Lecture 204 K-Means Clustering Exercise Solution - Part 13:4 Lecture 205 -Means Clustering Exercise Solution - Part 16:23 Lecture 206 K-Means Clustering Exercise Solution - Part T 9:0

#### Section 23 : Hierarchical Clustering

 Lecture 207 Introduction to Hierarchical Clustering 1:8 Lecture 208 Hierarchical Clustering - Theory and Intuitio 14:6 Lecture 209 Hierarchical Clustering - Coding Part One 19:59 Lecture 210 Hierarchical Clustering - Coding Part Two - S 32:31

#### Section 24 : DBSCAN Density-based spatial clustering of applications

 Lecture 211 Introduction to DBSCAN Section 1:15 Lecture 212 DBSCAN - Theory and Intuition 19:30 Lecture 213 DBSCAN versus K-Means Clustering 16:20 Lecture 214 DBSCAN - Hyperparameter Theory 8:16 Lecture 215 DBSCAN - Hyperparameter Tuning Methods 25:58 Lecture 216 DBSCAN - Outlier Project Exercise Overview 6:52 Lecture 217 DBSCAN - Outlier Project Exercise Solutions 25:40

#### Section 25 : PCA - Principal Component Analysis and Manifold Learning

 Lecture 218 Introduction to Principal Component Analysis. 2:45 Lecture 219 PCA Theory and Intuition - Part One 2:45 Lecture 220 PCA Theory and Intuition - Part Two 18:13