Section 1 : Introduction
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Lecture 1 | Introduction and Outline | 00:10:30 Duration |
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Lecture 2 | INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM | |
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Lecture 3 | Are You Beginner, Intermediate, or Advanced All are OK! | 00:04:56 Duration |
Section 2 : Vector Models and Text Preprocessing
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Lecture 1 | Vector Models & Text Preprocessing Intro | 00:03:28 Duration |
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Lecture 2 | Basic Definitions for NLP | 00:04:51 Duration |
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Lecture 3 | What is a Vector | |
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Lecture 4 | Bag of Words | 00:02:22 Duration |
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Lecture 5 | Count Vectorizer (Theory) | 00:13:34 Duration |
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Lecture 6 | Tokenization | 00:14:34 Duration |
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Lecture 7 | Stopwords | 00:04:42 Duration |
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Lecture 8 | Stemming and Lemmatization | 00:11:52 Duration |
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Lecture 9 | Stemming and Lemmatization Demo | 00:13:16 Duration |
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Lecture 10 | Count Vectorizer (Code) | 00:15:32 Duration |
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Lecture 11 | Vector Similarity | 00:11:25 Duration |
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Lecture 12 | TF-IDF (Theory) | 00:14:06 Duration |
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Lecture 13 | (Interactive) Recommender Exercise Prompt | 00:02:26 Duration |
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Lecture 14 | TF-IDF (Code) | 00:20:15 Duration |
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Lecture 15 | Word-to-Index Mapping | 00:10:45 Duration |
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Lecture 16 | How to Build TF-IDF From Scratch | 00:14:58 Duration |
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Lecture 17 | Neural Word Embeddings | 00:10:05 Duration |
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Lecture 18 | Neural Word Embeddings Demo | 00:11:15 Duration |
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Lecture 19 | Vector Models & Text Preprocessing Summary | 00:03:40 Duration |
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Lecture 20 | Text Summarization Preview | 00:01:11 Duration |
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Lecture 21 | How To Do NLP In Other Languages | 00:10:31 Duration |
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Lecture 22 | About Certification |
Section 3 : Probabilistic Models (Introduction)
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Lecture 1 | Probabilistic Models (Introduction) | 00:04:36 Duration |
Section 4 : Markov Models (Intermediate)
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Lecture 1 | Markov Models Section Introduction | 00:02:32 Duration |
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Lecture 2 | The Markov Property | 00:07:24 Duration |
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Lecture 3 | The Markov Model | 00:12:20 Duration |
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Lecture 4 | Probability Smoothing and Log-Probabilities | 00:07:40 Duration |
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Lecture 5 | Building a Text Classifier (Theory) | 00:07:19 Duration |
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Lecture 6 | Building a Text Classifier (Exercise Prompt) | 00:06:23 Duration |
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Lecture 7 | Building a Text Classifier (Code pt 1) | 00:10:22 Duration |
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Lecture 8 | Building a Text Classifier (Code pt 2) | 00:11:57 Duration |
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Lecture 9 | Language Model (Theory) | 00:10:05 Duration |
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Lecture 10 | Language Model (Exercise Prompt) | 00:06:41 Duration |
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Lecture 11 | Language Model (Code pt 1) | 00:10:35 Duration |
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Lecture 12 | Language Model (Code pt 2) | 00:09:15 Duration |
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Lecture 13 | Markov Models Section Summary | 00:02:50 Duration |
Section 5 : Article Spinner (Intermediate)
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Lecture 1 | Article Spinning - Problem Description | 00:07:45 Duration |
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Lecture 2 | Article Spinning - N-Gram Approach | 00:04:14 Duration |
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Lecture 3 | Article Spinner Exercise Prompt | 00:05:35 Duration |
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Lecture 4 | Article Spinner in Python (pt 1) | 00:17:22 Duration |
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Lecture 5 | Article Spinner in Python (pt 2) | 00:09:48 Duration |
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Lecture 6 | Case Study Article Spinning Gone Wrong | 00:05:32 Duration |
Section 6 : Cipher Decryption (Advanced)
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Lecture 1 | Section Introduction | 00:04:40 Duration |
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Lecture 2 | Ciphers | 00:03:49 Duration |
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Lecture 3 | Language Models (Review) | 00:15:56 Duration |
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Lecture 4 | Genetic Algorithms | 00:21:10 Duration |
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Lecture 5 | Code Preparation | 00:04:35 Duration |
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Lecture 6 | Code pt 1 | 00:02:57 Duration |
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Lecture 7 | Code pt 2 | 00:07:09 Duration |
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Lecture 8 | Code pt 3 | 00:04:39 Duration |
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Lecture 9 | Code pt 4 | 00:03:53 Duration |
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Lecture 10 | Code pt 5 | |
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Lecture 11 | Code pt 6 | 00:05:15 Duration |
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Lecture 12 | Cipher Decryption - Additional Discussion | 00:02:46 Duration |
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Lecture 13 | Section Conclusion | 00:05:50 Duration |
Section 7 : Machine Learning Models (Introduction)
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Lecture 1 | Machine Learning Models (Introduction) | 00:05:40 Duration |
Section 8 : Spam Detection
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Lecture 1 | Spam Detection - Problem Description | 00:06:22 Duration |
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Lecture 2 | Naive Bayes Intuition | 00:11:26 Duration |
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Lecture 3 | Spam Detection - Exercise Prompt | 00:01:57 Duration |
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Lecture 4 | Aside Class Imbalance, ROC, AUC, and F1 Score (pt 1) | 00:12:15 Duration |
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Lecture 5 | Aside Class Imbalance, ROC, AUC, and F1 Score (pt 2) | 00:10:52 Duration |
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Lecture 6 | Spam Detection in Python | 00:16:13 Duration |
Section 9 : Sentiment Analysis
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Lecture 1 | Sentiment Analysis - Problem Description | 00:07:17 Duration |
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Lecture 2 | Logistic Regression Intuition (pt 1) | 00:17:26 Duration |
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Lecture 3 | Multiclass Logistic Regression (pt 2) | 00:06:42 Duration |
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Lecture 4 | Logistic Regression Training and Interpretation (pt 3) | 00:08:05 Duration |
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Lecture 5 | Sentiment Analysis - Exercise Prompt | 00:03:50 Duration |
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Lecture 6 | Sentiment Analysis in Python (pt 1) | 00:10:28 Duration |
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Lecture 7 | Sentiment Analysis in Python (pt 2) | 00:08:17 Duration |
Section 10 : Text Summarization
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Lecture 1 | Text Summarization Section Introduction | 00:05:23 Duration |
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Lecture 2 | Text Summarization Using Vectors | 00:05:20 Duration |
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Lecture 3 | Text Summarization Exercise Prompt | 00:01:39 Duration |
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Lecture 4 | Text Summarization in Python | |
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Lecture 5 | TextRank Intuition | 00:07:52 Duration |
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Lecture 6 | TextRank - How It Really Works (Advanced) | 00:10:39 Duration |
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Lecture 7 | TextRank Exercise Prompt (Advanced) | 00:01:13 Duration |
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Lecture 8 | TextRank in Python (Advanced) | 00:14:23 Duration |
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Lecture 9 | Text Summarization in Python - The Easy Way (Beginner) | |
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Lecture 10 | Text Summarization Section Summary | 00:03:12 Duration |
Section 11 : Topic Modeling
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Lecture 1 | Topic Modeling Section Introduction | 00:02:56 Duration |
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Lecture 2 | Latent Dirichlet Allocation (LDA) - Essentials | 00:10:44 Duration |
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Lecture 3 | LDA - Code Preparation | 00:03:30 Duration |
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Lecture 4 | LDA - Maybe Useful Picture (Optional) | 00:01:42 Duration |
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Lecture 5 | Latent Dirichlet Allocation (LDA) - Intuition (Advanced) | 00:14:44 Duration |
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Lecture 6 | Topic Modeling with Latent Dirichlet Allocation (LDA) in Python | 00:11:28 Duration |
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Lecture 7 | Non-Negative Matrix Factorization (NMF) Intuition | 00:10:10 Duration |
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Lecture 8 | Topic Modeling with Non-Negative Matrix Factorization (NMF) in Python | 00:05:22 Duration |
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Lecture 9 | Topic Modeling Section Summary | 00:01:26 Duration |
Section 12 : Latent Semantic Analysis (Latent Semantic Indexing)
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Lecture 1 | LSA LSI Section Introduction | 00:03:56 Duration |
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Lecture 2 | SVD (Singular Value Decomposition) Intuition | 00:12:00 Duration |
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Lecture 3 | LSA LSI Applying SVD to NLP | 00:07:36 Duration |
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Lecture 4 | Latent Semantic Analysis Latent Semantic Indexing in Python | 00:09:05 Duration |
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Lecture 5 | LSA LSI Exercises | 00:05:50 Duration |
Section 13 : Deep Learning (Introduction)
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Lecture 1 | Deep Learning Introduction (Intermediate-Advanced) | 00:04:47 Duration |
Section 14 : The Neuron
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Lecture 1 | The Neuron - Section Introduction | 00:02:10 Duration |
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Lecture 2 | Fitting a Line | 00:14:11 Duration |
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Lecture 3 | Classification Code Preparation | 00:07:10 Duration |
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Lecture 4 | Text Classification in Tensorflow | 00:11:59 Duration |
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Lecture 5 | The Neuron | 00:09:47 Duration |
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Lecture 6 | How does a model learn | 00:10:43 Duration |
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Lecture 7 | The Neuron - Section Summary | 00:01:41 Duration |
Section 15 : Feedforward Artificial Neural Networks
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Lecture 1 | ANN - Section Introduction | |
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Lecture 2 | Forward Propagation | 00:09:26 Duration |
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Lecture 3 | The Geometrical Picture | 00:09:33 Duration |
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Lecture 4 | Activation Functions | 00:17:08 Duration |
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Lecture 5 | Multiclass Classification | 00:08:30 Duration |
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Lecture 6 | ANN Code Preparation | 00:04:25 Duration |
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Lecture 7 | Text Classification ANN in Tensorflow | 00:05:32 Duration |
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Lecture 8 | Text Preprocessing Code Preparation | 00:11:23 Duration |
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Lecture 9 | Text Preprocessing in Tensorflow | 00:05:19 Duration |
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Lecture 10 | Embeddings | 00:10:03 Duration |
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Lecture 11 | CBOW (Advanced) | 00:03:57 Duration |
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Lecture 12 | CBOW Exercise Prompt | 00:00:47 Duration |
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Lecture 13 | CBOW in Tensorflow (Advanced) | 00:19:14 Duration |
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Lecture 14 | ANN - Section Summary | 00:01:22 Duration |
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Lecture 15 | Aside How to Choose Hyperparameters (Optional) | 00:06:12 Duration |
Section 16 : Convolutional Neural Networks
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Lecture 1 | CNN - Section Introduction | 00:04:24 Duration |
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Lecture 2 | What is Convolution | 00:16:27 Duration |
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Lecture 3 | What is Convolution (Pattern Matching) | 00:05:46 Duration |
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Lecture 4 | What is Convolution (Weight Sharing) | 00:06:30 Duration |
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Lecture 5 | Convolution on Color Images | 00:15:48 Duration |
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Lecture 6 | CNN Architecture | 00:20:47 Duration |
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Lecture 7 | CNNs for Text | 00:07:57 Duration |
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Lecture 8 | Convolutional Neural Network for NLP in Tensorflow | 00:05:21 Duration |
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Lecture 9 | CNN - Section Summary | 00:01:16 Duration |
Section 17 : Recurrent Neural Networks
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Lecture 1 | RNN - Section Introduction | 00:04:35 Duration |
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Lecture 2 | Simple RNN Elman Unit (pt 1) | 00:09:10 Duration |
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Lecture 3 | Simple RNN Elman Unit (pt 2) | 00:09:32 Duration |
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Lecture 4 | RNN Code Preparation | 00:09:34 Duration |
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Lecture 5 | RNNs Paying Attention to Shapes | 00:08:16 Duration |
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Lecture 6 | GRU and LSTM (pt 1) | 00:17:25 Duration |
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Lecture 7 | GRU and LSTM (pt 2) | 00:11:25 Duration |
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Lecture 8 | RNN for Text Classification in Tensorflow | 00:05:46 Duration |
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Lecture 9 | Parts-of-Speech (POS) Tagging in Tensorflow | 00:19:39 Duration |
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Lecture 10 | Named Entity Recognition (NER) in Tensorflow | 00:05:03 Duration |
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Lecture 11 | RNN - Section Summary | 00:01:48 Duration |
Section 18 : Extras
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Lecture 1 | About Proctor Testing |
Section 19 : Setting Up Your Environment FAQ
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Lecture 1 | Anaconda Environment Setup | 00:20:13 Duration |
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Lecture 2 | How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow | 00:17:14 Duration |
Section 20 : Extra Help With Python Coding for Beginners FAQ
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Lecture 1 | How to Code by Yourself (part 1) | 00:15:49 Duration |
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Lecture 2 | How to Code by Yourself (part 2) | 00:09:23 Duration |
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Lecture 3 | Proof that using Jupyter Notebook is the same as not using it | 00:12:24 Duration |
Section 21 : Effective Learning Strategies For Machine Learning FAQ
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Lecture 1 | How to Succeed in this Course (Long Version) | 00:10:17 Duration |
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Lecture 2 | Is this for Beginners or Experts Academic or Practical Fast or slow-paced | 00:21:57 Duration |
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Lecture 3 | Machine Learning and AI Prerequisite Roadmap (pt 1) | 00:11:13 Duration |
