Machine Learning

This course gives you the opportunity to develop a broad understanding of machine learning, with no technical coding or programming knowledge required. The main focus is to empower you to leverage the techniques of machine learning to sort data and optimize business operations within your organization.

Case study analysis enables you to formulate a clear problem statement, inspect closely the due process, and examine the practices associated with data handling.

Module 1

Introduction to Machine Learning

• Introduction to Big Data and Machine Learning
• Understanding Python

Module 2

Supervised Learning: Classification

• Classification fundamentals: Data and Models
• Understanding Decision Trees and Naive Bayes
• Feature selection and evaluating models
• Linear classifiers and Decision boundaries
• A case study

Module 3

Machine Learning Techniques

• Types of Learning
  • Supervvised Learning
  • Unsupervised Learning
• Advice for applying Machine Learning
• Machine Learning System Design

Module 4

Unsupervised Learning

• Clustering
• Recommendation
• Deep Learning

Module 5

Supervised Learning: Regression

• Types of Regression Models: Linear, Logistic
• Predicting house prices: A case study in Regression
• Linear Regression and Logistic Regressions: A Model-based approach
• Overfitting, Feature Selection, etc.

Module 6

Machine Learning Advanced Topics

• Advanced Topics In Machine Learning
• Reinforcement Learning
• Deep Learning
• Capstone Project 1: Predict churned customers.
• Capstone Project 2: Forecasting Ecommerce Sales