#### Section 1 : Introduction

 Lecture 1 Introduction and Outline 10:30 Lecture 2 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf Lecture 3 Are You Beginner, Intermediate, or Advanced All are OK! 4:56

#### Section 2 : Vector Models and Text Preprocessing

 Lecture 4 Vector Models & Text Preprocessing Intro 3:28 Lecture 5 Basic Definitions for NLP 4:51 Lecture 6 What is a Vector Lecture 7 Bag of Words 2:22 Lecture 8 Count Vectorizer (Theory) 13:34 Lecture 9 Tokenization 14:34 Lecture 10 Stopwords 4:42 Lecture 11 Stemming and Lemmatization 11:52 Lecture 12 Stemming and Lemmatization Demo 13:16 Lecture 13 Count Vectorizer (Code) 15:32 Lecture 14 Vector Similarity 11:25 Lecture 15 TF-IDF (Theory) 14:6 Lecture 16 (Interactive) Recommender Exercise Prompt 2:26 Lecture 17 TF-IDF (Code) 20:15 Lecture 18 Word-to-Index Mapping 10:45 Lecture 19 How to Build TF-IDF From Scratch 14:58 Lecture 20 Neural Word Embeddings 10:5 Lecture 21 Neural Word Embeddings Demo 11:15 Lecture 22 Vector Models & Text Preprocessing Summary 3:40 Lecture 23 Text Summarization Preview 1:11 Lecture 24 How To Do NLP In Other Languages 10:31 Lecture 25 About Certification Pdf

#### Section 3 : Probabilistic Models (Introduction)

 Lecture 26 Probabilistic Models (Introduction) 4:36

#### Section 4 : Markov Models (Intermediate)

 Lecture 27 Markov Models Section Introduction 2:32 Lecture 28 The Markov Property 7:24 Lecture 29 The Markov Model 12:20 Lecture 30 Probability Smoothing and Log-Probabilities 7:40 Lecture 31 Building a Text Classifier (Theory) 7:19 Lecture 32 Building a Text Classifier (Exercise Prompt) 6:23 Lecture 33 Building a Text Classifier (Code pt 1) 10:22 Lecture 34 Building a Text Classifier (Code pt 2) 11:57 Lecture 35 Language Model (Theory) 10:5 Lecture 36 Language Model (Exercise Prompt) 6:41 Lecture 37 Language Model (Code pt 1) 10:35 Lecture 38 Language Model (Code pt 2) 9:15 Lecture 39 Markov Models Section Summary 2:50

#### Section 5 : Article Spinner (Intermediate)

 Lecture 40 Article Spinning - Problem Description 7:45 Lecture 41 Article Spinning - N-Gram Approach 4:14 Lecture 42 Article Spinner Exercise Prompt 5:35 Lecture 43 Article Spinner in Python (pt 1) 17:22 Lecture 44 Article Spinner in Python (pt 2) 9:48 Lecture 45 Case Study Article Spinning Gone Wrong 5:32

#### Section 6 : Cipher Decryption (Advanced)

 Lecture 46 Section Introduction 4:40 Lecture 47 Ciphers 3:49 Lecture 48 Language Models (Review) 15:56 Lecture 49 Genetic Algorithms 21:10 Lecture 50 Code Preparation 4:35 Lecture 51 Code pt 1 2:57 Lecture 52 Code pt 2 7:9 Lecture 53 Code pt 3 4:39 Lecture 54 Code pt 4 3:53 Lecture 55 Code pt 5 Lecture 56 Code pt 6 5:15 Lecture 57 Cipher Decryption - Additional Discussion 2:46 Lecture 58 Section Conclusion 5:50

#### Section 7 : Machine Learning Models (Introduction)

 Lecture 59 Machine Learning Models (Introduction) 5:40

#### Section 8 : Spam Detection

 Lecture 60 Spam Detection - Problem Description 6:22 Lecture 61 Naive Bayes Intuition 11:26 Lecture 62 Spam Detection - Exercise Prompt 1:57 Lecture 63 Aside Class Imbalance, ROC, AUC, and F1 Score (pt 1) 12:15 Lecture 64 Aside Class Imbalance, ROC, AUC, and F1 Score (pt 2) 10:52 Lecture 65 Spam Detection in Python 16:13

#### Section 9 : Sentiment Analysis

 Lecture 66 Sentiment Analysis - Problem Description 7:17 Lecture 67 Logistic Regression Intuition (pt 1) 17:26 Lecture 68 Multiclass Logistic Regression (pt 2) 6:42 Lecture 69 Logistic Regression Training and Interpretation (pt 3) 8:5 Lecture 70 Sentiment Analysis - Exercise Prompt 3:50 Lecture 71 Sentiment Analysis in Python (pt 1) 10:28 Lecture 72 Sentiment Analysis in Python (pt 2) 8:17

#### Section 10 : Text Summarization

 Lecture 73 Text Summarization Section Introduction 5:23 Lecture 74 Text Summarization Using Vectors 5:20 Lecture 75 Text Summarization Exercise Prompt 1:39 Lecture 76 Text Summarization in Python Lecture 77 TextRank Intuition 7:52 Lecture 78 TextRank - How It Really Works (Advanced) 10:39 Lecture 79 TextRank Exercise Prompt (Advanced) 1:13 Lecture 80 TextRank in Python (Advanced) 14:23 Lecture 81 Text Summarization in Python - The Easy Way (Beginner) Lecture 82 Text Summarization Section Summary 3:12

#### Section 11 : Topic Modeling

 Lecture 83 Topic Modeling Section Introduction 2:56 Lecture 84 Latent Dirichlet Allocation (LDA) - Essentials 10:44 Lecture 85 LDA - Code Preparation 3:30 Lecture 86 LDA - Maybe Useful Picture (Optional) 1:42 Lecture 87 Latent Dirichlet Allocation (LDA) - Intuition (Advanced) 14:44 Lecture 88 Topic Modeling with Latent Dirichlet Allocation (LDA) in Python 11:28 Lecture 89 Non-Negative Matrix Factorization (NMF) Intuition 10:10 Lecture 90 Topic Modeling with Non-Negative Matrix Factorization (NMF) in Python 5:22 Lecture 91 Topic Modeling Section Summary 1:26

#### Section 12 : Latent Semantic Analysis (Latent Semantic Indexing)

 Lecture 92 LSA LSI Section Introduction 3:56 Lecture 93 SVD (Singular Value Decomposition) Intuition 12:0 Lecture 94 LSA LSI Applying SVD to NLP 7:36 Lecture 95 Latent Semantic Analysis Latent Semantic Indexing in Python 9:5 Lecture 96 LSA LSI Exercises 5:50

#### Section 13 : Deep Learning (Introduction)

 Lecture 97 Deep Learning Introduction (Intermediate-Advanced) 4:47

#### Section 14 : The Neuron

 Lecture 98 The Neuron - Section Introduction 2:10 Lecture 99 Fitting a Line 14:11 Lecture 100 Classification Code Preparation 7:10 Lecture 101 Text Classification in Tensorflow 11:59 Lecture 102 The Neuron 9:47 Lecture 103 How does a model learn 10:43 Lecture 104 The Neuron - Section Summary 1:41

#### Section 15 : Feedforward Artificial Neural Networks

 Lecture 105 ANN - Section Introduction Lecture 106 Forward Propagation 9:26 Lecture 107 The Geometrical Picture 9:33 Lecture 108 Activation Functions 17:8 Lecture 109 Multiclass Classification 8:30 Lecture 110 ANN Code Preparation 4:25 Lecture 111 Text Classification ANN in Tensorflow 5:32 Lecture 112 Text Preprocessing Code Preparation 11:23 Lecture 113 Text Preprocessing in Tensorflow 5:19 Lecture 114 Embeddings 10:3 Lecture 115 CBOW (Advanced) 3:57 Lecture 116 CBOW Exercise Prompt 0:47 Lecture 117 CBOW in Tensorflow (Advanced) 19:14 Lecture 118 ANN - Section Summary 1:22 Lecture 119 Aside How to Choose Hyperparameters (Optional) 6:12

#### Section 16 : Convolutional Neural Networks

 Lecture 120 CNN - Section Introduction 4:24 Lecture 121 What is Convolution 16:27 Lecture 122 What is Convolution (Pattern Matching) 5:46 Lecture 123 What is Convolution (Weight Sharing) 6:30 Lecture 124 Convolution on Color Images 15:48 Lecture 125 CNN Architecture 20:47 Lecture 126 CNNs for Text 7:57 Lecture 127 Convolutional Neural Network for NLP in Tensorflow 5:21 Lecture 128 CNN - Section Summary 1:16

#### Section 17 : Recurrent Neural Networks

 Lecture 129 RNN - Section Introduction 4:35 Lecture 130 Simple RNN Elman Unit (pt 1) 9:10 Lecture 131 Simple RNN Elman Unit (pt 2) 9:32 Lecture 132 RNN Code Preparation 9:34 Lecture 133 RNNs Paying Attention to Shapes 8:16 Lecture 134 GRU and LSTM (pt 1) 17:25 Lecture 135 GRU and LSTM (pt 2) 11:25 Lecture 136 RNN for Text Classification in Tensorflow 5:46 Lecture 137 Parts-of-Speech (POS) Tagging in Tensorflow 19:39 Lecture 138 Named Entity Recognition (NER) in Tensorflow 5:3 Lecture 139 RNN - Section Summary 1:48

#### Section 18 : Extras

 Lecture 140 About Proctor Testing Pdf

#### Section 19 : Setting Up Your Environment FAQ

 Lecture 141 Anaconda Environment Setup 20:13 Lecture 142 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow 17:14

#### Section 20 : Extra Help With Python Coding for Beginners FAQ

 Lecture 143 How to Code by Yourself (part 1) 15:49 Lecture 144 How to Code by Yourself (part 2) 9:23 Lecture 145 Proof that using Jupyter Notebook is the same as not using it 12:24

#### Section 21 : Effective Learning Strategies For Machine Learning FAQ

 Lecture 146 How to Succeed in this Course (Long Version) 10:17 Lecture 147 Is this for Beginners or Experts Academic or Practical Fast or slow-paced 21:57 Lecture 148 Machine Learning and AI Prerequisite Roadmap (pt 1) 11:13