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