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Course curriculum

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.


  • 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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