Section 1 : Course Overview, Installs, and Setup
Lecture 1 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 2 Installation and Environment Setup 00:18:16 Duration

Section 2 : Crash Course NumPy
Lecture 1 Introduction to NumPy
Lecture 2 NumPy Arrays 00:10:39 Duration
Lecture 3 NumPy Arrays Part Two 00:08:04 Duration
Lecture 4 Numpy Index Selection 00:11:30 Duration
Lecture 5 NumPy Operations 00:06:39 Duration
Lecture 6 Numpy Exercises 00:01:07 Duration
Lecture 7 Numpy Exercises - Solutions 00:06:57 Duration

Section 3 : Crash Course Pandas
Lecture 1 Pandas Overview 00:01:04 Duration
Lecture 2 Pandas Series 00:09:56 Duration
Lecture 3 Pandas DataFrames - Part One 00:13:18 Duration
Lecture 4 Pandas DataFrames - Part Two 00:11:04 Duration
Lecture 5 GroupBy Operations 00:05:36 Duration
Lecture 6 Pandas Operations
Lecture 7 Data Input and Output 00:10:11 Duration
Lecture 8 Pandas Exercises 00:03:32 Duration
Lecture 9 Pandas Exercises - Solutions 00:08:29 Duration

Section 4 : PyTorch Basics
Lecture 1 PyTorch Basics Introduction 00:03:15 Duration
Lecture 2 Tensor Basics 00:08:04 Duration
Lecture 3 Tensor Basics - Part Two 00:15:07 Duration
Lecture 4 Tensor Operations 00:13:24 Duration
Lecture 5 Tensor Operations - Part Two 00:06:21 Duration
Lecture 6 PyTorch Basics - Exercise 00:02:27 Duration
Lecture 7 PyTorch Basics - Exercise Solutions 00:05:16 Duration

Section 5 : Machine Learning Concepts Overview
Lecture 1 What is Machine Learning 00:03:33 Duration
Lecture 2 Supervised Learning 00:08:16 Duration
Lecture 3 Overfitting 00:07:52 Duration
Lecture 4 Evaluating Performance - Classification Error Metrics 00:16:37 Duration
Lecture 5 Evaluating Performance - Regression Error Metrics 00:05:31 Duration
Lecture 6 Unsupervised Learning 00:04:39 Duration

Section 6 : ANN - Artificial Neural Networks
Lecture 1 Introduction to ANN Section 00:01:39 Duration
Lecture 2 Theory - Perceptron Model 00:10:34 Duration
Lecture 3 Theory - Neural Network
Lecture 4 Theory - Activation Functions 00:10:34 Duration
Lecture 5 Multi-Class Classification 00:10:29 Duration
Lecture 6 Theory - Cost Functions and Gradient Descent 00:18:08 Duration
Lecture 7 Theory - BackPropagation 00:14:42 Duration
Lecture 8 PyTorch Gradients 00:12:17 Duration
Lecture 9 Linear Regression with PyTorch 00:10:56 Duration
Lecture 10 Linear Regression with PyTorch - Part Two 00:20:25 Duration
Lecture 11 DataSets with PyTorch 00:15:53 Duration
Lecture 12 Basic Pytorch ANN - Part One 00:11:28 Duration
Lecture 13 Basic PyTorch ANN - Part Two 00:15:28 Duration
Lecture 14 Basic PyTorch ANN - Part Three 00:14:17 Duration
Lecture 15 Introduction to Full ANN with PyTorch 00:06:47 Duration
Lecture 16 Full ANN Code Along - Regression - Part One - Feature Engineering 00:19:29 Duration
Lecture 17 Full ANN Code Along - Regression - Part 2 - Categorical and Continuous Features 00:19:37 Duration
Lecture 18 Full ANN Code Along - Regression - Part Three - Tabular Model 00:17:03 Duration
Lecture 19 Full ANN Code Along - Regression - Part Four - Training and Evaluation 00:16:36 Duration
Lecture 20 Full ANN Code Along - Classification Example 00:06:46 Duration
Lecture 21 ANN - Exercise Overview 00:05:24 Duration
Lecture 22 ANN - Exercise Solutions 00:16:19 Duration

Section 7 : CNN - Convolutional Neural Networks
Lecture 1 Introduction to CNNs
Lecture 2 Understanding the MNIST data set 00:03:19 Duration
Lecture 3 ANN with MNIST - Part One - Data 00:19:16 Duration
Lecture 4 ANN with MNIST - Part Two - Creating the Network 00:10:28 Duration
Lecture 5 ANN with MNIST - Part Three - Training 00:15:22 Duration
Lecture 6 ANN with MNIST - Part Four - Evaluation 00:09:09 Duration
Lecture 7 Image Filters and Kernels 00:11:30 Duration
Lecture 8 Convolutional Layers 00:13:55 Duration
Lecture 9 Pooling Layers 00:06:42 Duration
Lecture 10 MNIST Data Revisited 00:02:06 Duration
Lecture 11 MNIST with CNN - Code Along - Part One 00:18:16 Duration
Lecture 12 MNIST with CNN - Code Along - Part Two 00:18:13 Duration
Lecture 13 About Certification
Lecture 14 CIFAR-10 DataSet with CNN - Code Along - Part One 00:07:08 Duration
Lecture 15 CIFAR-10 DataSet with CNN - Code Along - Part Two 00:18:35 Duration
Lecture 16 Loading Real Image Data - Part One 00:16:07 Duration
Lecture 17 Loading Real Image Data - Part Two 00:18:21 Duration
Lecture 18 CNN on Custom Images - Part One - Loading Data 00:22:14 Duration
Lecture 19 About Proctor Testing
Lecture 20 CNN on Custom Images - Part Three - PreTrained Networks 00:14:08 Duration
Lecture 21 CNN Exercise 00:02:44 Duration
Lecture 22 CNN Exercise Solutions 00:07:47 Duration

Section 8 : Recurrent Neural Networks
Lecture 1 Introduction to Recurrent Neural Networks 00:01:55 Duration
Lecture 2 RNN Basic Theory 00:07:36 Duration
Lecture 3 Vanishing Gradients 00:06:42 Duration
Lecture 4 LSTMS and GRU 00:11:16 Duration
Lecture 5 RNN Batches Theory 00:07:44 Duration
Lecture 6 RNN - Creating Batches with Data 00:12:04 Duration
Lecture 7 Basic RNN - Creating the LSTM Model 00:12:50 Duration
Lecture 8 Basic RNN - Training and Forecasting
Lecture 9 RNN on a Time Series - Part One 00:14:30 Duration
Lecture 10 RNN on a Time Series - Part Two 00:18:39 Duration
Lecture 11 RNN Exercise 00:04:09 Duration
Lecture 12 RNN Exercise - Solutions 00:11:26 Duration

Section 9 : Using a GPU with PyTorch and CUDA
Lecture 1 Why do we need GPUs 00:13:01 Duration
Lecture 2 Using GPU for PyTorch 00:17:34 Duration

Section 10 : NLP with PyTorch
Lecture 1 Introduction to NLP with PyTorch 00:02:31 Duration
Lecture 2 Encoding Text Data 00:15:44 Duration
Lecture 3 Generating Training Batches 00:14:34 Duration
Lecture 4 Creating the LSTM Model 00:12:28 Duration
Lecture 5 Training the LSTM Model 00:11:49 Duration
Lecture 6 OUR MODEL FOR DOWNLOAD
Lecture 7 Generating Predictions 00:10:26 Duration