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