Solving Problems with Machine Learning

Introduction

Learn how to solve problems using the power of Machine Learning. These algorithms are able to discern patterns and infer deductions that may not be picked up by humans. These deductions can then be used for decision making and action.

Use a Graphical User Interface (GUI) based Machine Learning tool to understand Machine Learning concepts and algorithms. Also, understand the process of problem solving using machine learning – including details such as data preparation, algorithm spot check, fine-tuning results and presenting outcomes.

Course Content

  1. Introduction to Machine Learning
    • What is Machine Learning
    • Machine Learning in Real life
    • Types of Machine Learning
    • Key ML Models
    • Installing Weka
    • Load Dataset to Weka
    • Build Your First Classifier
  2. Classification
    • What is Classification?
    • K-Nearest Neighbours (KNN)
    • Support Vector Machine (SVM)
    • Naive Bayes
    • Decision Tree (DT)
  3. Regression
    • Introduction to Regression
    • Linear Regression
    • Support Vector Regression
    • K-Nearest Neighbour Regression
  4. Ensemble Methods
    • What is Ensemble Methods?
    • Bagging
    • Random Forest
    • Stacking
  5. Clustering
    • What is Clustering?
    • K-Means Clustering
    • Hierarchical Clustering
  6. Neural Network
    • What is Neural Network?
    • Multilayer Perceptron Classifier
  7. Problem Solving through Machine Learning
    • Problem Definition
    • Data Conceptualization
    • Data Gathering
    • Feature Engineering
    • Algorithm Spot Check
    • Fine Tuning Model
    • Pitfalls