Introduction to Machine Learning

This course introduces the principles and practices of machine learning, covering learning algorithms, feature representation, model training, generalization, and optimization techniques for building accurate and reliable predictive models

No Certificate / Course on Audit Track

About Course

This course provides a comprehensive introduction to the core principles and practical techniques of Machine Learning. Learners explore how machines learn from data through various learning algorithms, understand the importance of feature representation, and gain hands-on insight into model training and evaluation. The course emphasizes concepts such as generalization, optimization, and performance improvement to build accurate, reliable, and efficient predictive models. Designed for beginners and aspiring data professionals, this course builds a strong foundation for applying machine learning methods to real-world problems across different domains.

Authorship and Attribution

This course has been curated by Riphah International University faculty and staff using publicly available third-party content and Open Educational Resources (OER) for self-paced learning. Learners will engage with curated open-access materials to achieve the course learning outcomes. All third-party content is used under open-access or fair-use policies, while any original materials are developed specifically for this learning experience.

Source and Credits 1:

Instructor: Prof. Leslie Kaelbling, Prof. Tomás Lozano-Pérez
Provider: MIT OpenCourseWare
License: Creative Commons BY-NC-SA

Source and Credits 2:

Instructor: CodeWithHarry
Provider: Youtube (@codewithharry)
License: Standard YouTube license

What You'll Learn

By completing this course, you will be able to:

Explain the fundamental principles of machine learning, including learning paradigms, model representation, and generalization.
Analyze different machine learning algorithms (e.g., classification, regression, neural networks, reinforcement learning) and their strengths and limitations.
Apply mathematical and computational techniques to train machine learning models and evaluate their performance on real-world datasets.
Design and implement machine learning solutions using appropriate algorithms, feature representations, and optimization methods.
Evaluate and improve machine learning models by addressing issues such as overfitting, bias–variance tradeoff, and model optimization.

Prerequisites

To be successful in this course, learners should have:

Basic foundation in programming, preferably in Python
Understanding of variables, control structures, functions, and data structures
Working knowledge of linear algebra (recommended)
Familiarity with probability concepts (recommended)
Basic understanding of calculus (recommended)
Willingness to learn essential mathematical concepts introduced during the course

Who Can Take This Course?

This course is designed for:

Students who want to understand the foundations of machine learning
Learners interested in how data-driven models make predictions and decisions
Individuals pursuing artificial intelligence, data science, or computer science
Learners interested in robotics and related technology fields
Anyone aiming to build a strong base in machine learning for future applications

Course Outline

Course Introduction

Introduction

Introduction to Machine Learning (Reading)

Module 1: Introduction to Machine Learning

Introduction and Foundations of ML

Introduction and Foundations of ML (Video)

Introduction to ML – Estimation and Generalization (Video)

Supervised Learning Basics

Supervised Learning Basics (Video)

Supervised Learning – Hypotheses (Video)

Model Evaluation and Learning Algorithms

Evaluating Predictions – Loss Functions (Video)

Evaluating Hypotheses – Training Set Error (Video)

Learning Algorithms (Video)

Module 2: Linear Classifiers

Linear Classification Models

Linear Classifiers (Video)

The Random Linear Classifier Algorithm (Video)

Module 3: Logistic Regression

Logistic Regression Fundamentals

Logistic Regression (Video)

Logistic Regression – Setting and Sigmoid Function (Video)

Linear Logistic Classifier – Hypothesis Class (Video)

Module 4: Optimization in Machine Learning

Gradient Descent

Gradient Descent in One Dimension (Reading)

Gradient Descent in Multiple Dimensions (Video)

Module 5: Regression

Regression Models

Regression and the Ordinary Least Squares Problem (Video)

Ordinary Least Squares Solution Using Optimization (Video)

Regularization by Ridge Regression (Video)

Module 6: Non-Parametric Models

Non-Parametric Learning Techniques

Introduction to Non-Parametric Models (Video)

Decision Trees (Video)

Decision Trees – The Good and the Bad (Video)

Module 7: Ensemble Learning Methods

Ensemble Learning Methods

Bagging – Bootstrap Aggregation of Models (Video)

Random Forest Models (Video)

Nearest Neighbor Models (Video)

Module 8: Recommender Systems and Neural Networks

Recommender Systems

Recommender Systems – Introduction (Video)

Neural Networks Fundamentals

Neural Networks – Basic Element (Video)

Neural Networks – Layer Definition (Video)

Module 9: Machine Learning Project

End-to-End Machine Learning Project

End-to-End Machine Learning Project (Video)

Course Wrap-Up

Course Summary

Summary (Reading)

Skills You Will Gain

ML Fundamentals Feature Engineering Model Training Model Evaluation Optimization Techniques Ensemble Methods

Course Information

Duration

Approximately 6 Hours

Course Information

Category: Computing
Type: Self-paced
Start Date: Feb 02, 2026

Difficulty Level

Intermediate

Learning Mode

Fully Online (Asynchronous)

Learning Type

Self Paced

Language

English Only

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