Data PRO or Pay-Per-Course
Pick a plan that right's for you!
Jump right in : Machine learning for Spam detection
Solving Classification Problems
Clustering as a Form of Unsupervised Learning
Regression as a Form of Supervised Learning
Natural Language Processing and Python
A Few Useful Things to Know About Overfitting
Recommendation Systems in Python
A Taste of Deep Learning and Computer Vision
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
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.
- 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.
- 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.