Section 1 : Introduction to Course
Lecture 1 EARLY BIRD INFO
Lecture 2 COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP! copy 00:03:36 Duration
Lecture 3 Anaconda Python and Jupyter Install and Setup 00:13:43 Duration
Lecture 4 Note on Environment Setup - Please read me!
Lecture 5 Environment Setup

Section 2 : OPTIONAL Python Crash Course
Lecture 1 OPTIONAL Python Crash Course
Lecture 2 Python Crash Course - Part One 00:15:54 Duration
Lecture 3 Python Crash Course - Part Two
Lecture 4 Python Crash Course - Part Three 00:11:12 Duration
Lecture 5 Python Crash Course - Exercise Questions 00:01:22 Duration
Lecture 6 Python Crash Course - Exercise Solutions 00:09:16 Duration

Section 3 : Machine Learning Pathway Overview
Lecture 1 Machine Learning Pathway 00:10:08 Duration

Section 4 : NumPy
Lecture 1 Introduction to NumPy 00:02:06 Duration
Lecture 2 NumPy Arrays 00:20:46 Duration
Lecture 3 NumPy Indexing and Selection 00:10:56 Duration
Lecture 4 NumPy Operations 00:08:05 Duration
Lecture 5 NumPy Exercises 00:01:07 Duration
Lecture 6 Numpy Exercises - Solutions 00:06:58 Duration

Section 5 : Pandas
Lecture 1 Introduction to Pandas 00:04:33 Duration
Lecture 2 Series - Part One 00:09:20 Duration
Lecture 3 Series - Part Two 00:10:33 Duration
Lecture 4 DataFrames - Part One - Creating a DataFrame 00:19:19 Duration
Lecture 5 DataFrames - Part Two - Basic Properties 00:08:01 Duration
Lecture 6 DataFrames - Part Three - Working with Columns 00:13:49 Duration
Lecture 7 DataFrames - Part Four - Working with Rows 00:14:22 Duration
Lecture 8 Pandas - Conditional Filtering 00:17:35 Duration
Lecture 9 Pandas - Useful Methods - Apply on Single Column 00:13:38 Duration
Lecture 10 Pandas - Useful Methods - Apply on Multiple Columns 00:17:16 Duration
Lecture 11 Pandas - Useful Methods - Statistical Information and Sorting 00:15:40 Duration
Lecture 12 Missing Data - Overview 00:11:50 Duration
Lecture 13 Missing Data - Pandas Operations 00:18:26 Duration
Lecture 14 GroupBy Operations - Part One 00:15:39 Duration
Lecture 15 GroupBy Operations - Part Two - MultiIndex 00:14:06 Duration
Lecture 16 Combining DataFrames - Concatenation
Lecture 17 Combining DataFrames - Inner Merge 00:11:55 Duration
Lecture 18 Combining DataFrames - Left and Right Merge 00:06:00 Duration
Lecture 19 Combining DataFrames - Outer Merge 00:10:32 Duration
Lecture 20 Pandas - Text Methods for String Data 00:15:57 Duration
Lecture 21 Pandas - Time Methods for Date and Time Data 00:20:54 Duration
Lecture 22 Pandas Input and Output - CSV Files 00:10:12 Duration
Lecture 23 Pandas Input and Output - HTML Tables 00:14:34 Duration
Lecture 24 Pandas Input and Output - Excel Files 00:07:12 Duration
Lecture 25 Pandas Input and Output - SQL Databases 00:18:13 Duration
Lecture 26 Pandas Pivot Tables 00:21:09 Duration
Lecture 27 Pandas Project Exercise Overview 00:05:19 Duration
Lecture 28 Pandas Project Exercise Solutions 00:26:25 Duration

Section 6 : Matplotlib
Lecture 1 Introduction to Matplotlib 00:03:59 Duration
Lecture 2 Matplotlib Basics 00:12:26 Duration
Lecture 3 Matplotlib - Understanding the Figure Object 00:07:24 Duration
Lecture 4 Matplotlib - Implementing Figures and Axes 00:14:22 Duration
Lecture 5 Matplotlib - Figure Parameters 00:04:47 Duration
Lecture 6 Matplotlib - Subplots Functionality 00:19:07 Duration
Lecture 7 Matplotlib Styling - Legends 00:06:55 Duration
Lecture 8 Matplotlib Styling - Colors and Styles 00:14:21 Duration
Lecture 9 Advanced Matplotlib Commands (Optional) 00:03:42 Duration
Lecture 10 Matplotlib Exercise Questions Overview 00:05:34 Duration
Lecture 11 Matplotlib Exercise Questions - Solutions 00:16:25 Duration

Section 7 : Seaborn Data Visualizations
Lecture 1 Introduction to Seaborn 00:03:48 Duration
Lecture 2 Scatterplots with Seaborn 00:18:10 Duration
Lecture 3 Distribution Plots - Part One - Understanding Plot Types 00:09:28 Duration
Lecture 4 Distribution Plots - Part Two - Coding with Seaborn 00:16:07 Duration
Lecture 5 Categorical Plots - Statistics within Categories - Understanding Plot Types 00:05:32 Duration
Lecture 6 Categorical Plots - Statistics within Categories - Coding with Seaborn 00:09:07 Duration
Lecture 7 Categorical Plots - Distributions within Categories - Understanding Plot Types 00:13:12 Duration
Lecture 8 Categorical Plots - Distributions within Categories - Coding with Seaborn 00:17:45 Duration
Lecture 9 Seaborn - Comparison Plots - Understanding the Plot Types 00:05:24 Duration
Lecture 10 Seaborn - Comparison Plots - Coding with Seaborn 00:09:41 Duration
Lecture 11 Seaborn Grid Plots 00:13:31 Duration
Lecture 12 Seaborn - Matrix Plots 00:13:10 Duration
Lecture 13 Seaborn Plot Exercises Solutions 00:14:10 Duration
Lecture 14 Seaborn Plot Exercises Overview

Section 8 : Data Analysis and Visualization Capstone Project Exercise
Lecture 1 Capstone Project Overview 00:12:35 Duration
Lecture 2 Capstone Project Solutions - Part One 00:17:07 Duration
Lecture 3 Capstone Project Solutions - Part Two 00:14:41 Duration
Lecture 4 Capstone Project Solutions - Part Three 00:19:38 Duration

Section 9 : Machine Learning Concepts Overview
Lecture 1 Introduction to Machine Learning Overview Section 00:05:03 Duration
Lecture 2 Why Machine Learning 00:09:06 Duration
Lecture 3 Types of Machine Learning Algorithms 00:07:39 Duration
Lecture 4 Supervised Machine Learning Process 00:13:32 Duration
Lecture 5 Companion Book - Introduction to Statistical Learning 00:02:42 Duration

Section 10 : Linear Regression
Lecture 1 Introduction to Linear Regression Section 00:01:34 Duration
Lecture 2 Linear Regression - Algorithm History 00:09:14 Duration
Lecture 3 Linear Regression - Understanding Ordinary Least Squares 00:15:37 Duration
Lecture 4 Linear Regression - Cost Functions 00:08:04 Duration
Lecture 5 Linear Regression - Gradient Descent 00:11:50 Duration
Lecture 6 Python coding Simple Linear Regression 00:19:25 Duration
Lecture 7 Overview of Scikit-Learn and Python 00:08:17 Duration
Lecture 8 Linear Regression - Scikit-Learn Train Test Split 00:15:41 Duration
Lecture 9 Linear Regression - Scikit-Learn Performance Evaluation - Regression 00:15:36 Duration
Lecture 10 Linear Regression - Residual Plots 00:13:48 Duration
Lecture 11 Linear Regression - Model Deployment and Coefficient Interpretation 00:17:36 Duration
Lecture 12 Polynomial Regression - Theory and Motivation 00:07:53 Duration
Lecture 13 Polynomial Regression - Creating Polynomial Features 00:10:49 Duration
Lecture 14 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 15 Bias Variance Trade-Off 00:10:29 Duration
Lecture 16 Polynomial Regression - Choosing Degree of Polynomial 00:13:29 Duration
Lecture 17 Polynomial Regression - Model Deployment 00:05:59 Duration
Lecture 18 Regularization Overview 00:06:29 Duration
Lecture 19 Feature Scaling 00:09:49 Duration
Lecture 20 Introduction to Cross Validation 00:12:38 Duration
Lecture 21 Regularization Data Setup 00:08:29 Duration
Lecture 22 Regularization - Ridge Regression Theory 00:14:30 Duration
Lecture 23 Regularization Ridge Regression 00:17:33 Duration
Lecture 24 Regularization - Lasso Regression - Background and Implementation 00:14:50 Duration
Lecture 25 Regularization - Elastic Net 00:17:59 Duration
Lecture 26 Linear Regression Project - Data Overview 00:04:21 Duration

Section 11 : Feature Engineering and Data Preparation
Lecture 1 A note from Jose on Feature Engineering and Data Preparation
Lecture 2 Introduction to Feature Engineering and Data Preparation 00:15:20 Duration
Lecture 3 Dealing with Outliers 00:26:23 Duration
Lecture 4 Dealing with Missing Data Part One - Evaluation of Missing Data 00:10:36 Duration
Lecture 5 Dealing with Missing Data Part Two - Filling or Dropping data based on Rows 00:20:34 Duration
Lecture 6 Dealing with Missing Data Part 3 - Fixing data based on Columns 00:23:08 Duration
Lecture 7 Dealing with Categorical Data - Encoding Options 00:12:41 Duration

Section 12 : Cross Validation , Grid Search, and the Linear Regression Project
Lecture 1 Section Overview and Introduction 00:03:07 Duration
Lecture 2 Cross Validation - Test Train Split 00:11:12 Duration
Lecture 3 Cross Validation - Test Validation Train Split 00:14:37 Duration
Lecture 4 Cross Validation - cross_val_score 00:11:32 Duration
Lecture 5 Cross Validation - cross_validate 00:06:48 Duration
Lecture 6 Grid Search 00:12:06 Duration
Lecture 7 Linear Regression Project Overview 00:02:40 Duration
Lecture 8 Linear Regression Project - Solutions 00:12:00 Duration

Section 13 : Logistic Regression
Lecture 1 Early Bird Note on Downloading
Lecture 2 Introduction to Logistic Regression Section 00:05:18 Duration
Lecture 3 Logistic Regression - Theory and Intuition - Part One The Logistic Function 00:05:29 Duration
Lecture 4 Logistic Regression - Theory and Intuition - Part Two Linear to Logistic 00:04:46 Duration
Lecture 5 Logistic Regression - Theory and Intuition - Linear to Logistic Math 00:16:54 Duration
Lecture 6 Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood 00:15:36 Duration
Lecture 7 Logistic Regression with Scikit-Learn - Part One - EDA
Lecture 8 Logistic Regression with Scikit-Learn - Part Two - Model Training 00:06:31 Duration
Lecture 9 Classification Metrics - Confusion Matrix and Accuracy 00:09:38 Duration
Lecture 10 Classification Metrics - Precison, Recall, F1-Score 00:05:51 Duration
Lecture 11 Classification Metrics - ROC Curves 00:07:06 Duration
Lecture 12 Logistic Regression with Scikit-Learn - Part Three - Performance Evaluation 00:15:51 Duration
Lecture 13 Multi-Class Classification with Logistic Regression - Part One - Data and EDA 00:07:59 Duration
Lecture 14 Multi-Class Classification with Logistic Regression - Part Two - Model 00:15:41 Duration
Lecture 15 Logistic Regression Exercise Project Overview 00:03:51 Duration
Lecture 16 Logistic Regression Project Exercise - Solutions 00:21:17 Duration

Section 14 : KNN - K Nearest Neighbors
Lecture 1 Introduction to KNN Section 00:02:03 Duration
Lecture 2 KNN Classification - Theory and Intuition 00:11:11 Duration
Lecture 3 KNN Coding with Python - Part One 00:13:32 Duration
Lecture 4 KNN Coding with Python - Part Two - Choosing K 00:23:16 Duration
Lecture 5 KNN Classification Project Exercise Overview 00:03:09 Duration
Lecture 6 KNN Classification Project Exercise Solutions 00:14:05 Duration

Section 15 : Support Vector Machines
Lecture 1 Introduction to Support Vector Machines 00:01:24 Duration
Lecture 2 History of Support Vector Machines 00:04:34 Duration
Lecture 3 SVM - Theory and Intuition - Hyperplanes and Margins 00:13:17 Duration
Lecture 4 SVM - Theory and Intuition - Kernel Intuition 00:04:51 Duration
Lecture 5 SVM - Theory and Intuition - Kernel Trick and Mathematics 00:20:42 Duration
Lecture 6 SVM with Scikit-Learn and Python - Classification Part One 00:10:52 Duration
Lecture 7 SVM with Scikit-Learn and Python - Classification Part Two 00:15:57 Duration
Lecture 8 SVM with Scikit-Learn and Python - Regression Tasks 00:20:52 Duration
Lecture 9 Support Vector Machine Project Overview 00:04:20 Duration
Lecture 10 Support Vector Machine Project Solutions 00:18:24 Duration

Section 16 : Tree Based Methods Decision Tree Learning
Lecture 1 Introduction to Tree Based Methods 00:01:16 Duration
Lecture 2 Decision Tree - History 00:08:58 Duration
Lecture 3 Decision Tree - Terminology 00:04:05 Duration
Lecture 4 Decision Tree - Understanding Gini Impurity 00:07:46 Duration
Lecture 5 Constructing Decision Trees with Gini Impurity - Part One 00:07:26 Duration
Lecture 6 Constructing Decision Trees with Gini Impurity - Part Two 00:11:17 Duration
Lecture 7 Coding Decision Trees - Part One - The Data 00:19:10 Duration
Lecture 8 Coding Decision Trees - Part Two -Creating the Model 00:20:50 Duration

Section 17 : Random Forests
Lecture 1 Introduction to Random Forests Section 00:01:44 Duration
Lecture 2 Random Forests - History and Motivation 00:11:38 Duration
Lecture 3 Random Forests - Key Hyperparameters 00:03:00 Duration
Lecture 4 Random Forests - Number of Estimators and Fea 00:10:53 Duration
Lecture 5 Random Forests - Bootstrapping and Out-of-Bag 00:12:48 Duration
Lecture 6 Coding Classification with Random Forest Clas 00:12:58 Duration
Lecture 7 Coding Classification with Random Forest Clas 00:23:35 Duration
Lecture 8 Coding Regression with Random Forest Regresso 00:04:31 Duration
Lecture 9 Coding Regression with Random Forest Regresso
Lecture 10 Coding Regression with Random Forest Regresso 00:21:08 Duration
Lecture 11 Coding Regression with Random Forest Regresso 00:11:00 Duration

Section 18 : Boosting Methods
Lecture 1 Introduction to Boosting Section 00:01:59 Duration
Lecture 2 Boosting Methods - Motivation and History 00:06:09 Duration
Lecture 3 AdaBoost Theory and Intuition 00:19:53 Duration
Lecture 4 AdaBoost Coding Part One - The Data 00:11:11 Duration
Lecture 5 AdaBoost Coding Part Two - The Model 00:18:18 Duration
Lecture 6 Gradient Boosting Theory 00:12:33 Duration
Lecture 7 Gradient Boosting Coding Walkthrough 00:16:19 Duration

Section 19 : Supervised Learning Capstone Project
Lecture 1 Introduction to Supervised Learning Capstone 00:15:50 Duration
Lecture 2 Solution Walkthrough - Supervised Learning Pr 00:23:02 Duration
Lecture 3 Solution Walkthrough - Supervised Learning Pr 00:31:05 Duration
Lecture 4 Solution Walkthrough - Supervised Learning 00:24:58 Duration

Section 20 : Naive Bayes Classification and Natural Language Processing
Lecture 1 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 2 About Proctor Testing
Lecture 3 Naive Bayes Algorithm - Part Two - Model Algo 00:19:54 Duration
Lecture 4 Feature Extraction from Text - Part One - The 00:13:17 Duration
Lecture 5 Feature Extraction from Text - Coding Count V 00:23:07 Duration
Lecture 6 Feature Extraction from Text - Coding with Sc 00:19:24 Duration
Lecture 7 Natural Language Processing - Classification 00:17:20 Duration
Lecture 8 Natural Language Processing - Classification 00:12:53 Duration
Lecture 9 Text Classification Project Exercise Overview 00:07:09 Duration
Lecture 10 Text Classification Project Exercise Solution 00:21:52 Duration

Section 21 : Unsupervised Learning
Lecture 1 Unsupervised Learning Overview 00:11:13 Duration

Section 22 : K-Means Clustering
Lecture 1 Introduction to K-Means Clustering Section 00:03:02 Duration
Lecture 2 Clustering General Overview 00:12:05 Duration
Lecture 3 K-Means Clustering Theory 00:11:29 Duration
Lecture 4 K-Means Clustering - Coding Part One 00:33:57 Duration
Lecture 5 K-Means Clustering Coding Part Two 00:18:01 Duration
Lecture 6 K-Means Clustering Coding Part Three 00:17:28 Duration
Lecture 7 K-Means Color Quantization - Part One 00:14:16 Duration
Lecture 8 K-Means Color Quantization - Part Two 00:15:21 Duration
Lecture 9 K-Means Clustering Exercise Overview 00:11:40 Duration
Lecture 10 K-Means Clustering Exercise Solution - Part 00:13:04 Duration
Lecture 11 -Means Clustering Exercise Solution - Part 00:16:23 Duration
Lecture 12 K-Means Clustering Exercise Solution - Part T 00:09:00 Duration

Section 23 : Hierarchical Clustering
Lecture 1 Introduction to Hierarchical Clustering 00:01:08 Duration
Lecture 2 Hierarchical Clustering - Theory and Intuitio 00:14:06 Duration
Lecture 3 Hierarchical Clustering - Coding Part One 00:19:59 Duration
Lecture 4 Hierarchical Clustering - Coding Part Two - S 00:32:31 Duration

Section 24 : DBSCAN Density-based spatial clustering of applications
Lecture 1 Introduction to DBSCAN Section 00:01:15 Duration
Lecture 2 DBSCAN - Theory and Intuition 00:19:30 Duration
Lecture 3 DBSCAN versus K-Means Clustering 00:16:20 Duration
Lecture 4 DBSCAN - Hyperparameter Theory 00:08:16 Duration
Lecture 5 DBSCAN - Hyperparameter Tuning Methods 00:25:58 Duration
Lecture 6 DBSCAN - Outlier Project Exercise Overview 00:06:52 Duration
Lecture 7 DBSCAN - Outlier Project Exercise Solutions 00:25:40 Duration

Section 25 : PCA - Principal Component Analysis and Manifold Learning
Lecture 1 Introduction to Principal Component Analysis. 00:02:45 Duration
Lecture 2 PCA Theory and Intuition - Part One 00:02:45 Duration
Lecture 3 PCA Theory and Intuition - Part Two 00:18:13 Duration