CourseGenix

Machine Learning Fundamental

3 Units11 Lessons

Unit 1

Basics

Introduction to Machine Learning Concepts

Types of Machine Learning

Understanding Data in Machine Learning

Real-World Application: Predicting Weather Patterns

Unit 2

Supervised Learning

Core Principles of Supervised Learning

Regression Techniques in Supervised Learning

Classification Methods in Supervised Learning

Real-World Project: Disease Diagnosis Prediction

Unit 3

Model Evaluation and Optimization

Key Metrics for Model Evaluation

Techniques for Model Optimization

Real-World Application: Optimizing a Recommendation System

Unit 2•Chapter 3

Classification Methods in Supervised Learning

Summary

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Concept Check

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What distinguishes bagging from boosting in ensemble methods?

Bagging reduces variance through averaging.

Boosting focuses on reducing bias sequentially.

Boosting weights misclassified instances.

Bagging builds models in parallel.

How does SVM handle non-linear classification problems?

Using kernel functions to map to higher dimensions.

Via neural networks for feature transformation.

By applying linear separation in original space.

Through decision trees for non-linearity.

What is the cost function used in logistic regression?

Binary cross-entropy for probability estimation.

Mean squared error for linear predictions.

Hinge loss for margin maximization.

Log loss to minimize classification error.

How can overfitting be mitigated in decision trees?

Using more complex impurity measures.

By pruning branches or limiting tree depth.

Increasing the number of splits aggressively.

Adding more features without regularization.

What assumption underlies the Naive Bayes classifier?

All features are equally important.

Features are conditionally independent given the class.

Classes are linearly separable.

The data follows a normal distribution.

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