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Machine Learning and Natural Language Processing Tutorial

Created by Stanford and IIT alumni with work experience in Google and Microsoft, this Machine Learning tutorial teaches Sentiment Analysis, Recommendation Systems, Deep Learning Networks, and Computer Vision.

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420 learners

Course Introduction:

Wondering 'What is Machine Learning'?

"Machine Learning is a field of study that gives computers the ability to learn without being explicitly programmed" as defined by Arthur Samuel. Machine Learning probes around studying and constructing algorithms that can learn from and make predictions on data.

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Course Objectives

The following are the few advantages in taking up this Machine Learning Tutorial:

  • Students will be able to spot situations where Machine Learning can be used, and deploy the appropriate solutions.
  • Discover various real life Machine Learning Applications.
  • Hundreds of lines of source code with comments are provided in the course, which can be directly used to implement Natural Language Processing and Machine Learning for text summarization, text classification in Python.
  • Product managers and executives will learn to intelligently converse with their data science counterparts, without being constrained by it.
  • NOTE: The coding language used in this tutorial is Python 2.7.


If you are wondering whether you can enroll in this course without any pre-requisite, then the answer is definitely a YES! This course has no pre-requisites but knowledge of undergraduate level mathematics will help you understand the course better. Moreover, having a basic knowledge of Python would be an added advantage to run the source code used in the course.

Course Plan
Certificate of completion

1. Introduction
1 video
Introduction to the course 03:16
2. Jump right in : Machine learning for Spam detection
4 videos
Machine Learning: Why should you jump on the bandwagon? 16:31

Spam Detection with Machine Learning Continued 17:04

Get the Lay of the Land : Types of Machine Learning Problems 17:27
3. Naive Bayes Classifier
4 videos
Random Variables 20:11

Bayes Theorem 18:36

Naive Bayes Classifier 08:50

Naive Bayes Classifier : An example 14:04
4. K-Nearest Neighbors
2 videos
K-Nearest Neighbors 13:09

K-Nearest Neighbors : A few wrinkles 14:47
5. Support Vector Machines
2 videos
Support Vector Machines : Maximum Margin Hyperplane and Kernel Trick 16:23
6. Clustering as a form Unsupervised learning
2 videos
Clustering : Introduction 19:08

Clustering : K-Means and DBSCAN 13:43
7. Association Detection
1 video
Association Rules Learning 09:13
8. Association Detection
2 videos
Dimensionality Reduction 10:22

9. Artificial Neural Networks
1 video
Artificial Neural Networks I: Perceptron introduced 11:18
10. Regression as a form of supervised learning
2 videos
Regression Introduced : Linear and Logistic Regression 13:54

Bias Variance Trade-off 10:14
11. Natural Language Processing and Python
16 videos
Natural Language Processing with NLTK 07:26

Natural Language Processing with NLTK - See it in action 14:14

Web Scraping with BeautifulSoup 18:09

A Serious NLP Application : Text Auto Summarization using Python 11:34

Python Drill : Autosummarize News Articles I 18:34

Python Drill : Autosummarize News Articles II 11:28

Python Drill : Autosummarize News Articles III 10:24

Put it to work : News Article Classification using Naive Bayes Classifier 19:25

Python Drill : Scraping News Websites 15:46

Python Drill : Feature Extraction with NLTK 17:43

Python Drill : Classification with KNN 03:44

Document Distance using TF-IDF 11:04

Put it to work : News Article Clustering with K-Means and TF-IDF 14:32

Python Drill : Clustering with K Means 06:54
12. Sentiment Analysis
10 videos
A Sneak Peek at what's coming up 02:36

Sentiment Analysis - What's all the fuss about? 17:17

ML Solutions for Sentiment Analysis - the devil is in the details 19:57

Sentiment Lexicons ( with an introduction to WordNet and SentiWordNet) 18:49

Regular Expressions 17:54

Regular Expressions in Python 05:41

Put it to work : Twitter Sentiment Analysis 17:48

Twitter Sentiment Analysis - Work the API 20:00

Twitter Sentiment Analysis - Regular Expressions for Preprocessing 12:24

Twitter Sentiment Analysis - Naive Bayes, SVM and Sentiwordnet 19:41
13. Decision Trees
7 videos
Planting the seed - What are Decision Trees? 17:01

Growing the Tree - Decision Tree Learning 18:03

Branching out - Information Gain 18:51

Titanic : Decision Trees predict Survival (Kaggle) - I 19:22

Titanic : Decision Trees predict Survival (Kaggle) - II 14:16

Titanic : Decision Trees predict Survival (Kaggle) - III 13:00
14. A Few Useful Things to Know About Overfitting
6 videos
Overfitting - the bane of Machine Learning 19:04

Overfitting Continued 11:20

Cross Validation 18:55

Simplicity is a virtue - Regularization 07:18

The Wisdom of Crowds - Ensemble Learning 16:39

Ensemble Learning continued - Bagging, Boosting and Stacking 18:03
15. Random Forests
2 videos
Random Forests - Much more than trees 12:28

Back on the Titanic - Cross Validation and Random Forests 20:03
16. Recommendation Systems
10 videos
What do Amazon and Netflix have in common? 16:44

Recommendation Engines - A look inside 10:45

What are you made of? - Content-Based Filtering 13:36

With a little help from friends - Collaborative Filtering 10:27

A Neighbourhood Model for Collaborative Filtering 17:51

Top Picks for You! - Recommendations with Neighbourhood Models 09:42

Latent Factor Collaborative Filtering contd. 12:09

Gray Sheep and Shillings - Challenges with Collaborative Filtering 08:12

The Apriori Algorithm for Association Rules 18:32
17. Recommendation Systems in Python
8 videos
Back to Basics : Numpy in Python 18:06

Back to Basics : Numpy and Scipy in Python 14:19

Movielens and Pandas 16:45

Code Along - What's my favorite movie? - Data Analysis with Pandas 06:19

Code Along - Movie Recommendation with Nearest Neighbour CF 18:10

Code Along - Top Movie Picks (Nearest Neighbour CF) 06:16

Code Along - Movie Recommendations with Matrix Factorization 17:56

Code Along - Association Rules with the Apriori Algorithm 09:51
18. A Taste of Deep Learning and Computer Vision
6 videos
Computer Vision: An Introduction 18:08

Perceptron Revisited 16:00

Deep Learning Networks Introduced 17:01

Code Along - Handwritten Digit Recognition -I 14:30

Code Along - Handwritten Digit Recognition - II 17:36

Code Along - Handwritten Digit Recognition - III 06:01

Meet the Author

4 Alumni of Stanford, IIM-A, IITs and Google, Microsoft, Flipkart

Loonycorn is a team of 4 people who graduated from reputed top universities. Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh have spent years (decades, actually) working in the Tech sector across the world.

  • Janani: Graduated from Stanford and has worked for 7 years at Google (New York, Singapore). She also worked at Flipkart and Microsoft.
  • Vitthal: Studied at Stanford; worked at Google (Singapore), Flipkart, Credit Suisse, and INSEAD.
  • Swetha: An IIM Ahmedabad and IIT Madras alumnus having experience of working in Flipkart.
  • Navdeep: An IIT Guwahati alumnus and Longtime Flipkart employee.
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