Workshop material for Machine Learning in Python by Amit Kapoor and Bargava Subramanian

0. Overview

1. Time Series (8 hours, Case - Peeling the Onion)

   • Linear Trend Model
   • Random Walk
   • Moving Average
   • Exponential Smoothing
   • Decomposition
   • ARIMA Models
   • Tweaking Model Parameters

2. Association Rule Mining (4 hours, Case - Grocery)

   • Apriori Algorithm
   • Market Basket Analysis

3. Random Forest / Gradient Boosting (4 hours, Case - Bank Marketing)

   • Intro to Ensemble Models, Bagging and Boosting
   • Gradient Boosting Classifier & Regressor
   • Random Forest Classifier & Regressor
   • Tuning Model Parameters

4. Text Mining (6 hours, Case - DataTau)

   • Regular Expression
   • Stopword Removal, Stemming
   • Word Cloud
   • Creating features from text
   • Term Frequency and Inverse Document Frequency (TF-IDF)
   • Topic Modeling - Latent Dirichlet Allocation (LDA)
   • Sentiment Analysis

###Script to check if requisite libraries for the workshop are present Please execute the following at the command prompt

$ python check_env.py

If any library has a FAIL message, please install/upgrade that library.

Installation instructions can be found here

Machine Learning using Python by Amit Kapoor and Bargava Subramanian is licensed under a MIT License.