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 Retail Demand Prediction Course
    • Learning Objectives of the Course FREE PREVIEW
    • Overview of the Course FREE PREVIEW
    • Instructor Introduction
    • Optional Content
    • Introduction to Predictive Modeling
    • Types of Predictive Models
    • Quiz: Introduction to Predictive Modeling
    • Stages of Predictive Modeling
    • Quiz: Types of Prediction Models
    • Quiz: Stages of Predictive Modeling
    • Understanding Hypothesis Generation
    • Quiz: Hypothesis Generation
    • Data Extraction
    • Understanding Data Exploration
    • Quiz: Data Extraction and Exploration
  • 2
    Problem Statement Definition
    • Defining the Business Problem FREE PREVIEW
    • Quiz: Defining the Business Problem
    • Reaching the Right Business Objective - Revenue vs Costs FREE PREVIEW
    • Quiz: Reaching the Right Business Objective - Revenue vs Costs
    • Reaching the Right Business Objective - Identifying objectives from revenue
    • Quiz: Reaching the Right Business Objective - Identifying objectives from revenue
    • Reaching the right business objective - Identifying objective to reduce cost
    • Reaching the right business objective - Comparing Impact & Effort
    • Quiz: Reaching the right business objective - Comparing Impact & Effort
    • Final Problem Statement
    • Hypothesis Generation
    • Data Extraction
    • Available Datasets
    • Quiz: Available Datasets
    • Make your own Data Science Problem Statement
  • 3
    Exploratory Data Analysis
    • Understanding and Validating Data
    • Understanding Product related Features
    • Understanding Store related Features
    • Data Exploration Summary
    • Validating the Hypothesis
    • Summarising Key Insights
  • 4
    Data preprocessing
    • Preprocessing: Categorical
    • Preprocessing: Numerical
  • 5
    Baseline Model and Validation Strategy
    • Defining Evaluation Metric
    • Mean Prediction Model
    • Decision Tree & Linear Regression
    • Implementation of Validation with Random Forest
  • 6
    Gradient Boosting Methods & Hyperparameter Tuning
    • XGBoost Single Model
    • Understanding Hyperparameter Tuning
    • XGboost Hyperparameter Tuning
    • CatBoost Single Model
    • Catboost Hyperparameter Tuning
  • 7
    Advanced Ensemble Methods
    • Introduction to Ensemble Learning
    • Implementation for Ensemble Techniques
  • 8
    Sharing Model Results & Reporting
    • Test on holdout set
    • Sharing Model Results & Report