Section 1 : Getting Started

 		Lecture 1 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 2 About Certification Pdf
Lecture 3 Installation Getting Started Text
Lecture 4 [Activity] WINDOWS Installing and Using Anaconda & Course Materials 12:37
Lecture 5 [Activity] MAC Installing and Using Anaconda & Course Materials 10:3
Lecture 6 [Activity] LINUX Installing and Using Anaconda & Course Materials 10:57
Lecture 7 Python Basics, Part 1 [Optional] 4:59
Lecture 8 [Activity] Python Basics, Part 2 [Optional] 5:17
Lecture 9 [Activity] Python Basics, Part 3 [Optional] 2:46
Lecture 10 [Activity] Python Basics, Part 4 [Optional] 4:2
Lecture 11 Introducing the Pandas Library [Optional] 10:8

Section 2 : Statistics and Probability Refresher, and Python Practice

 		Lecture 12 Types of Data (Numerical, Categorical, Ordinal) 6:59
Lecture 13 Mean, Median, Mode 5:26
Lecture 14 [Activity] Using mean, median, and mode in Python 8:21
Lecture 15 [Activity] Variation and Standard Deviation 11:12
Lecture 16 Probability Density Function; Probability Mass Function 3:27
Lecture 17 Common Data Distributions (Normal, Binomial, Poisson, etc) 7:45
Lecture 18 [Activity] Percentiles and Moments 12:33
Lecture 19 [Activity] A Crash Course in matplotlib 13:46
Lecture 20 [Activity] Advanced Visualization with Seaborn 17:30
Lecture 21 [Activity] Covariance and Correlation 11:31
Lecture 22 [Exercise] Conditional Probability 16:4
Lecture 23 Exercise Solution Conditional Probability of Purchase by Age 2:20
Lecture 24 Bayes' Theorem 5:23

Section 3 : Predictive Models

 		Lecture 25 [Activity] Linear Regression
Lecture 26 [Activity] Polynomial Regression 8:5
Lecture 27 [Activity] Multiple Regression, and Predicting Car Prices 11:26
Lecture 28 Multi-Level Models 4:37

Section 4 : Machine Learning with Python

 		Lecture 29 Supervised vs 8:57
Lecture 30 [Activity] Using TrainTest to Prevent Overfitting a Polynomial Regression 5:47
Lecture 31 Bayesian Methods Concepts 4:0
Lecture 32 [Activity] Implementing a Spam Classifier with Naive Bayes 8:6
Lecture 33 K-Means Clustering 7:23
Lecture 34 [Activity] Clustering people based on income and age 5:14
Lecture 35 Measuring Entropy 3:10
Lecture 36 [Activity] WINDOWS Installing Graphviz 0:22
Lecture 37 [Activity] MAC Installing Graphviz 1:16
Lecture 38 [Activity] LINUX Installing Graphviz 0:54
Lecture 39 Decision Trees Concepts 8:43
Lecture 40 [Activity] Decision Trees Predicting Hiring Decisions 9:47
Lecture 41 Ensemble Learning 5:59
Lecture 42 [Activity] XGBoost 15:30
Lecture 43 Support Vector Machines (SVM) Overview 4:27
Lecture 44 [Activity] Using SVM to cluster people using scikit-learn 9:30

Section 5 : Recommender Systems

 		Lecture 45 User-Based Collaborative Filtering 7:57
Lecture 46 Item-Based Collaborative Filtering 8:16
Lecture 47 [Activity] Finding Movie Similarities using Cosine Similarity 9:8
Lecture 48 [Activity] Improving the Results of Movie Similarities 8:0
Lecture 49 About Proctor Testing Pdf
Lecture 50 [Exercise] Improve the recommender's results 5:30

Section 6 : More Data Mining and Machine Learning Techniques

 		Lecture 51 K-Nearest-Neighbors Concepts 3:45
Lecture 52 [Activity] Using KNN to predict a rating for a movie
Lecture 53 Dimensionality Reduction; Principal Component Analysis (PCA) 5:44
Lecture 54 [Activity] PCA Example with the Iris data set 9:5
Lecture 55 Data Warehousing Overview ETL and ELT 9:5
Lecture 56 Reinforcement Learning 12:44
Lecture 57 [Activity] Reinforcement Learning & Q-Learning with Gym 12:57
Lecture 58 Understanding a Confusion Matrix 5:18
Lecture 59 Measuring Classifiers (Precision, Recall, F1, ROC, AUC) 6:35

Section 7 : Dealing with Real-World Data

 		Lecture 60 BiasVariance Tradeoff
Lecture 61 [Activity] K-Fold Cross-Validation to avoid overfitting 10:26
Lecture 62 Data Cleaning and Normalization 7:10
Lecture 63 [Activity] Cleaning web log data 10:56
Lecture 64 Normalizing numerical data 3:22
Lecture 65 [Activity] Detecting outliers 6:22
Lecture 66 Feature Engineering and the Curse of Dimensionality 6:4
Lecture 67 Imputation Techniques for Missing Data 7:49
Lecture 68 Handling Unbalanced Data Oversampling, Undersampling, and SMOTE 5:35
Lecture 69 Binning, Transforming, Encoding, Scaling, and Shuffling 7:51

Section 8 : Apache Spark Machine Learning on Big Data

 		Lecture 70 Warning about Java 11 and Spark 3! Text
Lecture 71 Spark installation notes for MacOS and Linux users Text
Lecture 72 [Activity] Installing Spark - Part 1 7:0
Lecture 73 [Activity] Installing Spark - Part 2 7:21
Lecture 74 Spark Introduction 9:11
Lecture 75 Spark and the Resilient Distributed Dataset (RDD) 11:42
Lecture 76 Introducing MLLib 5:9
Lecture 77 Introduction to Decision Trees in Spark 16:15
Lecture 78 [Activity] K-Means Clustering in Spark 11:23
Lecture 79 TF IDF 6:44
Lecture 80 [Activity] Searching Wikipedia with Spark 8:22
Lecture 81 [Activity] Using the Spark 2 8:7

Section 9 : Experimental Design ML in the Real World

 		Lecture 82 Deploying Models to Real-Time Systems 8:42
Lecture 83 AB Testing Concepts 8:23
Lecture 84 T-Tests and P-Values 6:0
Lecture 85 [Activity] Hands-on With T-Tests 6:4
Lecture 86 Determining How Long to Run an Experiment 3:25
Lecture 87 AB Test Gotchas 9:27

Section 10 : Deep Learning and Neural Networks

 		Lecture 88 Deep Learning Pre-Requisites 11:43
Lecture 89 The History of Artificial Neural Networks 11:15
Lecture 90 [Activity] Deep Learning in the Tensorflow Playground 12:0
Lecture 91 Deep Learning Details
Lecture 92 Introducing Tensorflow 11:30
Lecture 93 Important note about Tensorflow 2 Text
Lecture 94 [Activity] Using Tensorflow, Part 1 13:11
Lecture 95 [Activity] Using Tensorflow, Part 2 12:3
Lecture 96 mp4 13:33
Lecture 97 [Activity] Using Keras to Predict Political Affiliations 12:6
Lecture 98 Convolutional Neural Networks (CNN's) 11:28
Lecture 99 [Activity] Using CNN's for handwriting recognition
Lecture 100 Recurrent Neural Networks (RNN's) 11:2
Lecture 101 [Activity] Using a RNN for sentiment analysis 9:37
Lecture 102 [Activity] Transfer Learning 12:14
Lecture 103 Tuning Neural Networks Learning Rate and Batch Size Hyperparameters 4:39
Lecture 104 Deep Learning Regularization with Dropout and Early Stopping 6:21
Lecture 105 The Ethics of Deep Learning 11:2
Lecture 106 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf

Section 11 : Final Project

 		Lecture 107 Your final project assignment Mammogram Classification 6:20
Lecture 108 Final project review 10:26