/machine-learning

Workshop on Machine Learning in Python

Primary LanguageHTMLMIT LicenseMIT

Machine Learning

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

  1. Overview

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

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

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

    • Apriori Algorithm
    • Market Basket Analysis

  4. 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

  5. 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

Licensing

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