This course will start on 12th December 2019.

About the Course - Retail Demand Prediction Using Machine Learning

Are you wondering how is machine learning being used in the industry today? Are you looking for some hands-on experience on a real-world dataset? Do you want to learn how to solve a business problem using machine learning?

If yes, then you have landed at the right place. This course is the perfect balance of theoretical learning and hands-on exercises.

The course is aimed at enabling you to build an end-to-end Machine Learning model on a real-world dataset - starting from converting a business objective into a machine learning problem, to building a complete machine learning model.

The business problem addressed as a part of the course is that of demand forecasting. 


Key Takeaways from the Course

  • Understand the importance of demand forecasting in the industry

  • Learn how to Structure a Business Problem

  • Converting a Business Problem to Machine Learning Problem

  • Derive Interesting Insights from the data

  • We will cover the following steps in ML Lifecycle:

    • Hypothesis Generation

    • Exploratory Data Analysis 

    • Data Cleaning and Preprocessing

    • Feature Engineering

    • Model Building

    • Evaluating Model Performance 

  • Create ML solution for real-world problems

  • Presenting Model results


Prerequisites

This course assumes that you are familiar with the basic concepts of Machine learning and comfortable with coding in python.

Course curriculum

  • 1
    Welcome to the course
    • Introduction to the course
    • Prerequisites for the course
  • 2
    Project: Problem Statement and Hypothesis Generation
    • Defining the Business Problem
    • Defining the Business Objective
    • Possible Data Problems that could help achieve business objective
    • Choosing the Data Problem
    • Hypothesis Generation
    • Datasets available & problem statement
  • 3
    Data Exploration
    • Creating Test Set
    • Creating Validation Set
    • EDA: Univariate
    • EDA: Bivariate
    • Insights from Data Exploration
  • 4
    Data preprocessing
    • Preprocessing: Categorical
    • Preprocessing: Numerical
  • 5
    Baseline Model and Feature Engineering
    • Defining Evaluation Metric
    • Testing with Baseline Model (GLM)
    • Feature Engineering
  • 6
    Model Building
    • Linear Model (GLM)
    • Tree Based Models
  • 7
    Model and Hyperparameter Selection
    • Selecting the Best model
    • Hyperparameter Tuning
  • 8
    Ensemble Models
    • Basics of Ensemble Learning
    • Ensemble Techniques
  • 9
    Final Model Test
    • Test on holdout set
    • Sharing Model Results & Report