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From 0 to 1: Machine Learning, NLP & Python-Cut to the Chase

Video Description

A down-to-earth, shy but confident take on machine learning techniques that you can put to work today

About This Video

  • What's Covered:Machine Learning, Natural Language Processing with Python
  • Sentiment Analysis, Mitigating Overfitting with Ensemble Learning
  • Recommendations: Content based filtering, Collaborative filtering and Association Rules learning
  • Get started with Deep learning: Apply Multi-layer perceptrons to the MNIST Digit recognition problem

In Detail

This course is a down-to-earth, shy but confident take on machine learning techniques that you can put to work today. Let’s parse that. The course is down-to-earth: it makes everything as simple as possible - but not simpler. The course is shy but confident: It is authoritative, drawn from decades of practical experience -but shies away from needlessly complicating stuff. You can put ML to work today: If Machine Learning is a car, this car will have you driving today. It won't tell you what the carburetor is. The course is very visual: most of the techniques are explained with the help of animations to help you understand better. This course is practical as well: There are hundreds of lines of source code with comments that can be used directly to implement natural language processing and machine learning for text summarization, text classification in Python. The course is also quirky. The examples are irreverent. Lots of little touches: repetition, zooming out so we remember the big picture, active learning with plenty of quizzes. There’s also a peppy soundtrack, and art - all shown by studies to improve cognition and recall.

Table of Contents

  1. Chapter 1 : Introduction
    1. You, This Course and Us 00:02:24
    2. A sneak peek at what's coming up 00:04:12
  2. Chapter 2 : Jump right in: Machine learning for Spam detection
    1. Solving problems with computers 00:02:12
    2. Machine Learning: Why should you jump on the bandwagon? 00:07:28
    3. Plunging In - Machine Learning Approaches to Spam Detection 00:11:48
    4. Spam Detection with Machine Learning Continued 00:11:07
    5. Get the Lay of the Land: Types of Machine Learning Problems 00:09:46
  3. Chapter 3 : Solving Classification Problems
    1. Solving Classification Problems 00:00:59
    2. Random Variables 00:11:27
    3. Bayes Theorem 00:11:55
    4. Naive Bayes Classifier 00:05:26
    5. Naive Bayes Classifier: An example 00:09:19
    6. K-Nearest Neighbours 00:13:09
    7. K-Nearest Neighbours: A few wrinkles 00:14:47
    8. Support Vector Machines Introduced 00:08:16
    9. Support Vector Machines: Maximum Margin Hyperplane and Kernel Trick 00:16:23
    10. Artificial Neural Networks: Perceptrons Introduced 00:11:18
  4. Chapter 4 : Clustering as a form of Unsupervised learning
    1. Clustering: Introduction 00:19:08
    2. Clustering: K-Means and DBSCAN 00:13:43
  5. Chapter 5 : Association Detection
    1. Association Rules Learning 00:09:13
  6. Chapter 6 : Dimensionality Reduction
    1. Dimensionality Reduction 00:10:17
    2. Principal Component Analysis 00:18:54
  7. Chapter 7 : Regression as a form of supervised learning
    1. Regression Introduced: Linear and Logistic Regression 00:13:54
    2. Bias Variance Trade-off 00:10:14
  8. Chapter 8 : Natural Language Processing and Python
    1. Applying ML to Natural Language Processing 00:00:57
    2. Installing Python - Anaconda and Pip 00:09:00
    3. Natural Language Processing with NLTK 00:07:26
    4. Natural Language Processing with NLTK - See it in action 00:14:14
    5. Web Scraping with BeautifulSoup 00:18:09
    6. A Serious NLP Application: Text Auto Summarization using Python 00:11:34
    7. Python Drill: Autosummarize News Articles I 00:18:34
    8. Python Drill: Autosummarize News Articles II 00:11:28
    9. Python Drill: Autosummarize News Articles III 00:10:24
    10. Put it to work: News Article Classification using K-Nearest Neighbors 00:19:29
    11. Put it to work : News Article Classification using Naive Bayes Classifier 00:19:25
    12. Python Drill: Scraping News Websites 00:15:46
    13. Python Drill: Feature Extraction with NLTK 00:18:51
    14. Python Drill: Classification with KNN 00:04:15
    15. Python Drill: Classification with Naive Bayes 00:08:08
    16. Document Distance using TF-IDF 00:11:04
    17. Put it to work: News Article Clustering with K-Means and TF-IDF 00:14:32
    18. Python Drill: Clustering with K Means 00:08:32
  9. Chapter 9 : Sentiment Analysis
    1. Solve Sentiment Analysis using Machine Learning 00:02:36
    2. Sentiment Analysis - What's all the fuss about? 00:17:17
    3. ML Solutions for Sentiment Analysis - the devil is in the details 00:19:57
    4. Sentiment Lexicons (with an introduction to WordNet and SentiWordNet) 00:18:49
    5. Regular Expressions 00:17:54
    6. Regular Expressions in Python 00:05:41
    7. Put it to work: Twitter Sentiment Analysis 00:17:48
    8. Twitter Sentiment Analysis - Work the API 00:20:00
    9. Twitter Sentiment Analysis - Regular Expressions for Preprocessing 00:12:24
    10. Twitter Sentiment Analysis - Naive Bayes, SVM and Sentiwordnet 00:19:41
  10. Chapter 10 : Decision Trees
    1. Using Tree Based Models for Classification 00:01:01
    2. Planting the seed - What are Decision Trees? 00:17:02
    3. Growing the Tree - Decision Tree Learning 00:18:04
    4. Branching out - Information Gain 00:18:51
    5. Decision Tree Algorithms 00:07:51
    6. Titanic: Decision Trees predict Survival (Kaggle) – I 00:19:22
    7. Titanic: Decision Trees predict Survival (Kaggle) - II 00:14:16
    8. Titanic: Decision Trees predict Survival (Kaggle) – III 00:13:00
  11. Chapter 11 : A Few Useful Things to Know About Overfitting
    1. Overfitting - the bane of Machine Learning 00:19:04
    2. Overfitting Continued 00:11:20
    3. Cross Validation 00:18:55
    4. Simplicity is a virtue – Regularization 00:07:18
    5. The Wisdom of Crowds - Ensemble Learning 00:16:39
    6. Ensemble Learning continued - Bagging, Boosting and Stacking 00:18:03
  12. Chapter 12 : Random Forests
    1. Random Forests - Much more than trees 00:12:28
    2. Back on the Titanic - Cross Validation and Random Forests 00:20:03
  13. Chapter 13 : Recommendation Systems
    1. Solving Recommendation Problems 00:00:57
    2. What do Amazon and Netflix have in common? 00:16:44
    3. Recommendation Engines - A look inside 00:10:45
    4. What are you made of? - Content-Based Filtering 00:13:36
    5. With a little help from friends - Collaborative Filtering 00:10:27
    6. A Neighbourhood Model for Collaborative Filtering 00:17:51
    7. Top Picks for You! - Recommendations with Neighbourhood Models 00:09:42
    8. Discover the Underlying Truth - Latent Factor Collaborative Filtering 00:20:13
    9. Latent Factor Collaborative Filtering contd. 00:12:09
    10. Gray Sheep and Shillings - Challenges with Collaborative Filtering 00:08:12
    11. The Apriori Algorithm for Association Rules 00:18:32
  14. Chapter 14 : Recommendation Systems in Python
    1. Back to Basics: Numpy in Python 00:18:06
    2. Back to Basics: Numpy and Scipy in Python 00:14:19
    3. Movielens and Pandas 00:16:45
    4. Code Along - What's my favourite movie? - Data Analysis with Pandas 00:06:19
    5. Code Along - Movie Recommendation with Nearest Neighbour CF 00:18:10
    6. Code Along - Top Movie Picks (Nearest Neighbour CF) 00:06:16
    7. Code Along - Movie Recommendations with Matrix Factorization 00:17:56
    8. Code Along - Association Rules with the Apriori Algorithm 00:09:51
  15. Chapter 15 : A Taste of Deep Learning and Computer Vision
    1. Computer Vision - An Introduction 00:18:09
    2. Perceptron Revisited 00:16:02
    3. Deep Learning Networks Introduced 00:17:02
    4. Code Along - Handwritten Digit Recognition -I 00:14:30
    5. Code Along - Handwritten Digit Recognition - II 00:17:37
    6. Code Along - Handwritten Digit Recognition – III 00:06:02