#### 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