Section 1 : Course Overview, Installs, and Setup
Lecture 1 INTRODUCTION TO BRAINMEASURES PROCT
Lecture 2 About Certification
Lecture 3 Course Setup and Installation
Lecture 4 FAQ - Frequently Asked Questions

Section 2 : NumPy Crash Course
Lecture 1 Introduction to NumPy 00:02:18 Duration
Lecture 2 umPy Arrays 00:18:50 Duration
Lecture 3 Numpy Index Selection 00:11:00 Duration
Lecture 4 NumPy Operations 00:08:08 Duration
Lecture 5 NumPy Exercises 00:01:08 Duration
Lecture 6 Numpy Exercises - Solutions 00:07:00 Duration

Section 3 : Pandas Crash Course
Lecture 1 Introduction to Pandas 00:03:52 Duration
Lecture 2 Pandas Series 00:08:35 Duration
Lecture 3 Pandas DataFrames - Part One 00:11:08 Duration
Lecture 4 Pandas DataFrames - Part Two
Lecture 5 Pandas Missing Data 00:10:02 Duration
Lecture 6 GroupBy Operations 00:09:35 Duration
Lecture 7 Pandas Operations 00:13:44 Duration
Lecture 8 Data Input and Output 00:11:43 Duration
Lecture 9 Pandas Exercises 00:02:21 Duration
Lecture 10 Pandas Exercises - Solutions 00:07:10 Duration

Section 4 : Visualization Crash Course
Lecture 1 Introduction to Python Visualization 00:01:09 Duration
Lecture 2 Matplotlib Basics 00:08:55 Duration
Lecture 3 Seaborn Basics 00:16:38 Duration
Lecture 4 Data Visualization Exercises 00:02:40 Duration
Lecture 5 Data Visualization Exercises - Solutions 00:07:30 Duration

Section 5 : Machine Learning Concepts Overview
Lecture 1 About Certification
Lecture 2 Supervised Learning Overview 00:08:15 Duration
Lecture 3 Overfitting 00:07:53 Duration
Lecture 4 Evaluating Performance - Classification Error 00:16:26 Duration
Lecture 5 Evaluating Performance - Regression Error Met 00:05:30 Duration
Lecture 6 Unsupervised Learning 00:04:38 Duration

Section 6 : Basic Artificial Neural Networks - ANNs
Lecture 1 Introduction to ANN Section 00:02:09 Duration
Lecture 2 Perceptron Model 00:10:34 Duration
Lecture 3 Neural Networks 00:07:13 Duration
Lecture 4 Activation Functions 00:10:32 Duration
Lecture 5 Multi-Class Classification Considerations 00:10:27 Duration
Lecture 6 Cost Functions and Gradient Descent 00:18:08 Duration
Lecture 7 Backpropagation 00:14:42 Duration
Lecture 8 TensorFlow vs. Keras Explained 00:02:07 Duration
Lecture 9 Keras Syntax Basics - Part One - Preparing the 00:10:43 Duration
Lecture 10 Keras Syntax Basics - Part Two - Creating and 00:13:54 Duration
Lecture 11 Keras Syntax Basics - Part Three - Model Evalu 00:12:51 Duration
Lecture 12 Keras Regression Code Along - Exploratory Data 00:18:45 Duration
Lecture 13 Keras Regression Code Along - Exploratory Data 00:13:16 Duration
Lecture 14 Keras Regression Code Along - Data Preprocessi 00:08:37 Duration
Lecture 15 Keras Regression Code Along - Model Evaluation 00:11:17 Duration
Lecture 16 Keras Classification Code Along - EDA and Prep 00:07:59 Duration
Lecture 17 Keras Classification - Dealing with Overfittin 00:16:45 Duration
Lecture 18 TensorFlow 2.0 Keras Project Options Overview 00:01:34 Duration
Lecture 19 TensorFlow 2.0 Keras Project Notebook Overview 00:07:36 Duration
Lecture 20 Keras Project Solutions - Exploratory Data Ana 00:20:27 Duration
Lecture 21 Keras Project Solutions - Dealing with Missing 00:14:41 Duration
Lecture 22 Keras Project Solutions - Dealing with Missing 00:11:57 Duration
Lecture 23 Keras Project Solutions - Categorical Data 00:17:17 Duration
Lecture 24 Keras Project Solutions - Data PreProcessing 00:03:39 Duration
Lecture 25 Keras Project Solutions - Creating and Trainin 00:03:51 Duration
Lecture 26 Keras Project Solutions - Model Evaluation 00:09:36 Duration
Lecture 27 Tensorboard 00:18:16 Duration

Section 7 : Convolutional Neural Networks - CNNs
Lecture 1 CNN Section Overview 00:01:25 Duration
Lecture 2 Image Filters and Kernels 00:11:30 Duration
Lecture 3 Convolutional Layers 00:13:55 Duration
Lecture 4 Pooling Layers 00:06:41 Duration
Lecture 5 MNIST Data Set Overview 00:04:34 Duration
Lecture 6 CNN on MNIST - Part One - The Data 00:12:51 Duration
Lecture 7 CNN on MNIST - Part Two - Creating and Traini 00:16:09 Duration
Lecture 8 CNN on MNIST - Part Three - Model Evaluation 00:06:47 Duration
Lecture 9 CNN on CIFAR-10 - Part One - The Data 00:11:18 Duration
Lecture 10 CNN on CIFAR-10 - Part Two - Evaluating the Mo 00:07:00 Duration
Lecture 11 Downloading Data Set for Real Image Lectures 00:05:18 Duration
Lecture 12 CNN on Real Image Files - Part One - Reading 00:14:49 Duration
Lecture 13 CNN on Real Image Files - Part Two - Data Proc 00:15:31 Duration
Lecture 14 CNN on Real Image Files - Part Three - Creatin 00:13:31 Duration
Lecture 15 CNN on Real Image Files - Part Four - Evaluat 00:08:43 Duration
Lecture 16 CNN Exercise Overview
Lecture 17 CNN Exercise Solutions 00:08:23 Duration

Section 8 : Recurrent Neural Networks - RNNs
Lecture 1 RNN Section Overview 00:02:31 Duration
Lecture 2 RNN Basic Theory 00:07:35 Duration
Lecture 3 Vanishing Gradients 00:06:40 Duration
Lecture 4 LSTMS and GRU 00:11:16 Duration
Lecture 5 RNN Batches 00:07:44 Duration
Lecture 6 RNN on a Sine Wave - The Data 00:08:22 Duration
Lecture 7 RNN on a Sine Wave - Batch Generator 00:08:09 Duration
Lecture 8 RNN on a Sine Wave - Creating the Model 00:15:15 Duration
Lecture 9 RNN on a Sine Wave - LSTMs and Forecasting 00:13:19 Duration
Lecture 10 RNN on a Time Series - Part One 00:09:44 Duration
Lecture 11 RNN on a Time Series - Part Two 00:21:31 Duration
Lecture 12 RNN Exercise 00:03:55 Duration
Lecture 13 RNN Exercise - Solutions 00:21:42 Duration
Lecture 14 Bonus - Multivariate Time Series - RNN and LST 00:16:03 Duration

Section 9 : Natural Language Processing
Lecture 1 Introduction to NLP Section 00:05:52 Duration
Lecture 2 NLP - Part One - The Data 00:04:26 Duration
Lecture 3 NLP - Part Two - Text Processing 00:04:29 Duration
Lecture 4 NLP - Part Three - Creating Batches 00:13:04 Duration
Lecture 5 NLP - Part Four - Creating the Model 00:10:17 Duration
Lecture 6 NLP - Part Five - Training the Model 00:09:42 Duration
Lecture 7 NLP - Part Six - Generating Text 00:09:12 Duration

Section 10 : AutoEncoders
Lecture 1 Introduction to Autoencoders 00:03:07 Duration
Lecture 2 Autoencoder Basics 00:07:46 Duration
Lecture 3 Autoencoder for Dimensionality Reduction
Lecture 4 Autoencoder for Images - Part One 00:16:58 Duration
Lecture 5 Autoencoder for Images - Part Two - Noise Rem 00:08:12 Duration
Lecture 6 Autoencoder Exercise Overview 00:03:25 Duration
Lecture 7 Autoencoder Exercise - Solutions 00:10:24 Duration

Section 11 : Generative Adversarial Networks
Lecture 1 GANs Overview 00:08:47 Duration
Lecture 2 Creating a GAN - Part One- The Data 00:04:30 Duration
Lecture 3 Creating a GAN - Part Two - The Model 00:12:14 Duration
Lecture 4 Creating a GAN - Part Three - Model Training.
Lecture 5 DCGAN - Deep Convolutional Generative Adversa 00:06:26 Duration

Section 12 : Deployment
Lecture 1 Introduction to Deployment 00:03:22 Duration
Lecture 2 Creating the Model 00:16:47 Duration
Lecture 3 Model Prediction Function 00:09:28 Duration
Lecture 4 Running a Basic Flask Application 00:10:32 Duration
Lecture 5 Flask Postman API 00:11:09 Duration
Lecture 6 Flask API - Using Requests Programmatically 00:03:48 Duration
Lecture 7 Flask Front End 00:19:36 Duration
Lecture 8 Live Deployment to the Web 00:17:22 Duration