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

Solving Classification Problems

Random Variables

Bayes Theorem

Naive Bayes Classifier

Naive Bayes Classifier : An example

K-Nearest Neighbors

K-Nearest Neighbors : A few wrinkles

Support Vector Machines Introduced

Support Vector Machines : Maximum Margin Hyperplane and Kernel Trick

Artificial Neural Networks:Perceptrons Introduced

Clustering : Introduction

Clustering : K-Means and DBSCAN

Association Rules Learning

Dimensionality Reduction

Principal Component Analysis

Regression Introduced : Linear and Logistic Regression

Bias Variance Trade-off

Applying ML to Natural Language Processing

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

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

Document Distance using TF-IDF

Python Drill : Classification with Naive Bayes

Python Drill : Classification with KNN

Python Drill : Feature Extraction with NLTK

Python Drill : Scraping News Websites

Put it to work : News Article Classification using K-Nearest Neighbors

Installing Python – Anaconda and Pip

Python Drill : Autosummarize News Articles III

Python Drill : Autosummarize News Articles II

Python Drill : Autosummarize News Articles I

A Serious NLP Application : Text Auto Summarization using Python

Web Scraping with BeautifulSoup

Natural Language Processing with NLTK – See it in action

Natural Language Processing with NLTK

Python Drill : Clustering with K Means

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

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

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

Random Forests – Much more than trees

Back on the Titanic – Cross Validation and Random Forests

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

Back to Basics : Numpy in Python

Back to Basics : Numpy and Scipy in Python

Movielens and Pandas

What’s my favorite movie? – Data Analysis with Pandas

Movie Recommendation with Nearest Neighbour CF

Top Movie Picks (Nearest Neighbour CF)

Movie Recommendations with Matrix Factorization

Association Rules with the Apriori Algorithm

Computer Vision – An Introduction

Perceptron Revisited

Deep Learning Networks Introduced

Handwritten Digit Recognition -I

Handwritten Digit Recognition – II

Handwritten Digit Recognition – III