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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 KNearest Neighbours KNearest 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 : KMeans and DBSCAN Downloads 
Association Rules Learning Downloads 
Dimensionality Reduction Principal Component Analysis Downloads 
Regression Introduced : Linear and Logistic Regression Bias Variance Tradeoff 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 KNearest 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 TFIDF Put it to work : News Article Clustering with KMeans and TFIDF 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?  ContentBased 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 Stanfordeducated, exGoogler and an IIT, IIM  educated exFlipkart lead analyst. This team has decades of practical experience in quant trading, analytics and ecommerce.
 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, Knearest neighbours, Support Vector Machines, Artificial Neural Networks, Kmeans, Hierarchical clustering, Principal Components Analysis, Linear regression, Logistics regression, Random variables, Bayes theorem, Biasvariance tradeoff.
Natural Language Processing with Python
 Corpora, stopwords, sentence and word parsing, autosummarization, sentiment analysis (as a special case of classification), TFIDF, Document Distance, Text summarization, Text classification with Naive Bayes and KNearest Neighbours and Clustering with KMeans.
Sentiment Analysis
 Why it's useful, Approaches to solving  RuleBased , MLBased , 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 Multilayer perceptrons to the MNIST Digit recognition problem.
A Note on Python: The codealongs in this class all use Python 2.7. Source code (with copious amounts of comments) is attached as a resource with all the codealongs. 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.
Prerequisites & 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.