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Course curriculum

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

Course Description

What will I learn?

  • Identify situations that call for the use of Machine Learning.
  • Understand which type of Machine learning problem you are solving and choose the appropriate solution.
  • Use Machine Learning and Natural Language processing to solve problems like text classification, text summarization in Python


About the course

This course is taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce.

  • 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.


What's Covered

Machine Learning

  • Supervised/Unsupervised learning, Classification, Clustering, Association Detection, Anomaly Detection, Dimensionality Reduction, Regression.
  • Naive Bayes, K-nearest neighbours, Support Vector Machines, Artificial Neural Networks, K-means, Hierarchical clustering, Principal Components Analysis, Linear regression, Logistics regression, Random variables, Bayes theorem, Bias-variance tradeoff.


Natural Language Processing with Python

  • Corpora, stopwords, sentence and word parsing, auto-summarization, sentiment analysis (as a special case of classification), TF-IDF, Document Distance, Text summarization, Text classification with Naive Bayes and K-Nearest Neighbours and Clustering with K-Means.


Sentiment Analysis

  • Why it's useful, Approaches to solving - Rule-Based , ML-Based , Training , Feature Extraction, Sentiment Lexicons, Regular Expressions, Twitter API, Sentiment Analysis of Tweets with Python.


Mitigating Overfitting with Ensemble Learning

  • Decision trees and decision tree learning, Overfitting in decision trees, Techniques to mitigate overfitting (cross validation, regularization), Ensemble learning and Random forests.


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.


A Note on Python: The code-alongs in this class all use Python 2.7. Source code (with copious amounts of comments) is attached as a resource with all the code-alongs. The source code has been provided for both Python 2 and Python 3 wherever possible.


Who should take the course?

  • Analytics professionals, modelers, big data professionals who haven't had exposure to machine learning.
  • Engineers who want to understand or learn machine learning and apply it to problems they are solving.
  • Product managers who want to have intelligent conversations with data scientists and engineers about machine learning.
  • Tech executives and investors who are interested in big data, machine learning or natural language processing.
  • MBA graduates or business professionals who are looking to move to a heavily quantitative role.


Pre-requisites & Requirements

  • No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided.

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