Section 1 : Getting Started
Lecture 1 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 2 About Certification
Lecture 3 Installation Getting Started
Lecture 4 [Activity] WINDOWS Installing and Using Anaconda & Course Materials 00:12:37 Duration
Lecture 5 [Activity] MAC Installing and Using Anaconda & Course Materials 00:10:03 Duration
Lecture 6 [Activity] LINUX Installing and Using Anaconda & Course Materials 00:10:57 Duration
Lecture 7 Python Basics, Part 1 [Optional] 00:04:59 Duration
Lecture 8 [Activity] Python Basics, Part 2 [Optional] 00:05:17 Duration
Lecture 9 [Activity] Python Basics, Part 3 [Optional] 00:02:46 Duration
Lecture 10 [Activity] Python Basics, Part 4 [Optional] 00:04:02 Duration
Lecture 11 Introducing the Pandas Library [Optional] 00:10:08 Duration

Section 2 : Statistics and Probability Refresher, and Python Practice
Lecture 1 Types of Data (Numerical, Categorical, Ordinal) 00:06:59 Duration
Lecture 2 Mean, Median, Mode 00:05:26 Duration
Lecture 3 [Activity] Using mean, median, and mode in Python 00:08:21 Duration
Lecture 4 [Activity] Variation and Standard Deviation 00:11:12 Duration
Lecture 5 Probability Density Function; Probability Mass Function 00:03:27 Duration
Lecture 6 Common Data Distributions (Normal, Binomial, Poisson, etc) 00:07:45 Duration
Lecture 7 [Activity] Percentiles and Moments 00:12:33 Duration
Lecture 8 [Activity] A Crash Course in matplotlib 00:13:46 Duration
Lecture 9 [Activity] Advanced Visualization with Seaborn 00:17:30 Duration
Lecture 10 [Activity] Covariance and Correlation 00:11:31 Duration
Lecture 11 [Exercise] Conditional Probability 00:16:04 Duration
Lecture 12 Exercise Solution Conditional Probability of Purchase by Age 00:02:20 Duration
Lecture 13 Bayes' Theorem 00:05:23 Duration

Section 3 : Predictive Models
Lecture 1 [Activity] Linear Regression
Lecture 2 [Activity] Polynomial Regression 00:08:05 Duration
Lecture 3 [Activity] Multiple Regression, and Predicting Car Prices 00:11:26 Duration
Lecture 4 Multi-Level Models 00:04:37 Duration

Section 4 : Machine Learning with Python
Lecture 1 Supervised vs 00:08:57 Duration
Lecture 2 [Activity] Using TrainTest to Prevent Overfitting a Polynomial Regression 00:05:47 Duration
Lecture 3 Bayesian Methods Concepts 00:04:00 Duration
Lecture 4 [Activity] Implementing a Spam Classifier with Naive Bayes 00:08:06 Duration
Lecture 5 K-Means Clustering 00:07:23 Duration
Lecture 6 [Activity] Clustering people based on income and age 00:05:14 Duration
Lecture 7 Measuring Entropy 00:03:10 Duration
Lecture 8 [Activity] WINDOWS Installing Graphviz 00:00:22 Duration
Lecture 9 [Activity] MAC Installing Graphviz 00:01:16 Duration
Lecture 10 [Activity] LINUX Installing Graphviz 00:00:54 Duration
Lecture 11 Decision Trees Concepts 00:08:43 Duration
Lecture 12 [Activity] Decision Trees Predicting Hiring Decisions 00:09:47 Duration
Lecture 13 Ensemble Learning 00:05:59 Duration
Lecture 14 [Activity] XGBoost 00:15:30 Duration
Lecture 15 Support Vector Machines (SVM) Overview 00:04:27 Duration
Lecture 16 [Activity] Using SVM to cluster people using scikit-learn 00:09:30 Duration

Section 5 : Recommender Systems
Lecture 1 User-Based Collaborative Filtering 00:07:57 Duration
Lecture 2 Item-Based Collaborative Filtering 00:08:16 Duration
Lecture 3 [Activity] Finding Movie Similarities using Cosine Similarity 00:09:08 Duration
Lecture 4 [Activity] Improving the Results of Movie Similarities 00:08:00 Duration
Lecture 5 About Proctor Testing
Lecture 6 [Exercise] Improve the recommender's results 00:05:30 Duration

Section 6 : More Data Mining and Machine Learning Techniques
Lecture 1 K-Nearest-Neighbors Concepts 00:03:45 Duration
Lecture 2 [Activity] Using KNN to predict a rating for a movie
Lecture 3 Dimensionality Reduction; Principal Component Analysis (PCA) 00:05:44 Duration
Lecture 4 [Activity] PCA Example with the Iris data set 00:09:05 Duration
Lecture 5 Data Warehousing Overview ETL and ELT 00:09:05 Duration
Lecture 6 Reinforcement Learning 00:12:44 Duration
Lecture 7 [Activity] Reinforcement Learning & Q-Learning with Gym 00:12:57 Duration
Lecture 8 Understanding a Confusion Matrix 00:05:18 Duration
Lecture 9 Measuring Classifiers (Precision, Recall, F1, ROC, AUC) 00:06:35 Duration

Section 7 : Dealing with Real-World Data
Lecture 1 BiasVariance Tradeoff
Lecture 2 [Activity] K-Fold Cross-Validation to avoid overfitting 00:10:26 Duration
Lecture 3 Data Cleaning and Normalization 00:07:10 Duration
Lecture 4 [Activity] Cleaning web log data 00:10:56 Duration
Lecture 5 Normalizing numerical data 00:03:22 Duration
Lecture 6 [Activity] Detecting outliers 00:06:22 Duration
Lecture 7 Feature Engineering and the Curse of Dimensionality 00:06:04 Duration
Lecture 8 Imputation Techniques for Missing Data 00:07:49 Duration
Lecture 9 Handling Unbalanced Data Oversampling, Undersampling, and SMOTE 00:05:35 Duration
Lecture 10 Binning, Transforming, Encoding, Scaling, and Shuffling 00:07:51 Duration

Section 8 : Apache Spark Machine Learning on Big Data
Lecture 1 Warning about Java 11 and Spark 3!
Lecture 2 Spark installation notes for MacOS and Linux users
Lecture 3 [Activity] Installing Spark - Part 1 00:07:00 Duration
Lecture 4 [Activity] Installing Spark - Part 2 00:07:21 Duration
Lecture 5 Spark Introduction 00:09:11 Duration
Lecture 6 Spark and the Resilient Distributed Dataset (RDD) 00:11:42 Duration
Lecture 7 Introducing MLLib 00:05:09 Duration
Lecture 8 Introduction to Decision Trees in Spark 00:16:15 Duration
Lecture 9 [Activity] K-Means Clustering in Spark 00:11:23 Duration
Lecture 10 TF IDF 00:06:44 Duration
Lecture 11 [Activity] Searching Wikipedia with Spark 00:08:22 Duration
Lecture 12 [Activity] Using the Spark 2 00:08:07 Duration

Section 9 : Experimental Design ML in the Real World
Lecture 1 Deploying Models to Real-Time Systems 00:08:42 Duration
Lecture 2 AB Testing Concepts 00:08:23 Duration
Lecture 3 T-Tests and P-Values 00:06:00 Duration
Lecture 4 [Activity] Hands-on With T-Tests 00:06:04 Duration
Lecture 5 Determining How Long to Run an Experiment 00:03:25 Duration
Lecture 6 AB Test Gotchas 00:09:27 Duration

Section 10 : Deep Learning and Neural Networks
Lecture 1 Deep Learning Pre-Requisites 00:11:43 Duration
Lecture 2 The History of Artificial Neural Networks 00:11:15 Duration
Lecture 3 [Activity] Deep Learning in the Tensorflow Playground 00:12:00 Duration
Lecture 4 Deep Learning Details
Lecture 5 Introducing Tensorflow 00:11:30 Duration
Lecture 6 Important note about Tensorflow 2
Lecture 7 [Activity] Using Tensorflow, Part 1 00:13:11 Duration
Lecture 8 [Activity] Using Tensorflow, Part 2 00:12:03 Duration
Lecture 9 mp4 00:13:33 Duration
Lecture 10 [Activity] Using Keras to Predict Political Affiliations 00:12:06 Duration
Lecture 11 Convolutional Neural Networks (CNN's) 00:11:28 Duration
Lecture 12 [Activity] Using CNN's for handwriting recognition
Lecture 13 Recurrent Neural Networks (RNN's) 00:11:02 Duration
Lecture 14 [Activity] Using a RNN for sentiment analysis 00:09:37 Duration
Lecture 15 [Activity] Transfer Learning 00:12:14 Duration
Lecture 16 Tuning Neural Networks Learning Rate and Batch Size Hyperparameters 00:04:39 Duration
Lecture 17 Deep Learning Regularization with Dropout and Early Stopping 00:06:21 Duration
Lecture 18 The Ethics of Deep Learning 00:11:02 Duration
Lecture 19 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM

Section 11 : Final Project
Lecture 1 Your final project assignment Mammogram Classification 00:06:20 Duration
Lecture 2 Final project review 00:10:26 Duration