Unit Structure

            
Validation Techniques & Supervised Learning Algorithms
|
├── 1. Validation Techniques
│   ├── Hold-Out Method
│   ├── K-Fold Cross Validation
│   ├── Leave-One-Out Validation
│   └── Bootstrapping
│
└── 2. Supervised Learning Algorithms
    ├── Linear Regression
    ├── Logistic Regression
    ├── Decision Trees
    ├── Support Vector Machine (SVM)
    ├── K-Nearest Neighbours (KNN)
    ├── CN2 Algorithm
    ├── Naive Bayes
    └── Artificial Neural Networks (ANN)
            
        

• When you're building machine learning models, it's not just about choosing the right algorithm — it's also about making sure your model actually works well on unseen data. That's why in this unit, we cover two essential parts of any ML project: Validation Techniques and Supervised Learning Algorithms.
• We begin with validation techniques, which help us evaluate our models properly. You'll learn about simple methods like the hold-out method, where data is split into training and testing sets, and more advanced ones like K-Fold Cross Validation, Leave-One-Out, and Bootstrapping — all of which help ensure your results are trustworthy and not just lucky guesses.
• Then, we move into the world of supervised learning algorithms, where the model learns from labeled data. We’ll explore popular techniques like Linear Regression and Logistic Regression for prediction tasks, Decision Trees for making rule-based decisions, and Support Vector Machines (SVM) for handling complex boundaries between classes. You’ll also get to know K-Nearest Neighbours (KNN) for simple yet effective classification, Naive Bayes for probabilistic prediction, the CN2 Algorithm for rule learning, and Artificial Neural Networks (ANNs) — the building blocks of deep learning.