Solving Problems with Machine Learning

machine learning

Introduction

Embark on your AI learning journey with our comprehensive Machine Learning course. Gain practical skills to tackle real-world problems without the need for programming. Machine learning, a subset of artificial intelligence (AI), equips computers to learn from data, identifying intricate patterns beyond human comprehension. Our course, featuring a Graphical User Interface (GUI)-based tool, empowers you to delve into regression, classification, and ensemble machine learning algorithms. Learn to solve real-world challenges through a systematic process, covering problem definition, data preparation, algorithm selection, results refinement, and outcome presentation. Upskill and deepen your knowledge in AI with our practical approach.

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