About this course
AI & ML Foundations is a comprehensive introductory module designed to equip learners with the essential knowledge and practical skills required to begin their journey in Artificial Intelligence and Machine Learning. As part of a structured learning path, this module provides a strong conceptual and technical foundation for students, academic learners, and early-stage professionals aiming to build careers in data science and intelligent systems.
The course begins with a clear and engaging introduction to Artificial Intelligence, exploring its evolution, key principles, and its growing impact across industries such as healthcare, finance, transportation, and e-commerce. Learners will understand how intelligent systems are designed to simulate human decision-making and how Machine Learning, as a subset of AI, enables systems to learn from data without explicit programming.
Moving beyond theory, the module introduces core Machine Learning concepts including supervised and unsupervised learning, basic model types, and real-world use cases. These concepts are explained in a simplified, beginner-friendly manner, ensuring accessibility even for those with no prior technical background.
A major focus of this course is developing programming skills using Python, the most widely used language in AI and data science. Learners will gain hands-on experience with Python fundamentals such as variables, data types, conditional statements, loops, and functions. These programming concepts are taught through practical examples and exercises, enabling learners to build confidence in writing and understanding code.
The course also introduces learners to Jupyter Notebook, an essential tool used by data scientists and AI practitioners for writing and executing code in an interactive environment. Participants will learn how to create notebooks, combine code with explanations, and run experiments efficiently—an important skill for real-world AI workflows.
To reinforce learning, the module includes interactive quizzes, coding exercises, and a mini-project that allows learners to apply their knowledge in a practical scenario. By the end of the course, participants will be able to write basic Python programs, understand fundamental Machine Learning concepts, and work within a notebook-based development environment.
AI & ML Foundations serves as the first step in a progressive AI learning pathway, preparing learners for more advanced topics such as data analysis, model development, and deep learning. With a balance of theory and hands-on practice, this module ensures learners build a solid base for future specialization and career growth in Artificial Intelligence and Machine Learning.
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COURSE OBJECTIVES:
1. To train the students to understand different types of AI agents.
2. To understand various AI search algorithms.
3. Fundamentals of knowledge representation, building of simple knowledge-based systems and to apply knowledge representation.
4. To introduce the basic concepts and techniques of machine learning and the need for Machine learning techniques for real world problem
5. To provide understanding of various Machine learning algorithms and the way to evaluate the performance of ML algorithms
UNIT - I:
Introduction: AI problems, Agents and Environments, Structure of Agents, Problem Solving Agents Basic Search Strategies: Problem Spaces, Uninformed Search (Breadth-First, Depth-First Search, Depth-first with Iterative Deepening), Heuristic Search (Hill Climbing, Generic Best-First, A*), Constraint Satisfaction (Backtracking, Local Search)
UNIT - II:
Advanced Search: Constructing Search Trees, Stochastic Search, AO* Search Implementation, Minimax Search, Alpha-Beta Pruning Basic Knowledge Representation and Reasoning: Propositional Logic, First-Order Logic, Forward Chaining and Backward Chaining, Introduction to Probabilistic Reasoning, Bayes Theorem
UNIT - III:
Machine-Learning : Introduction. Machine Learning Systems, Forms of Learning: Supervised and Unsupervised Learning, reinforcement – theory of learning – feasibility of learning – Data Preparation– training versus testing and split.
UNIT - IV:
Supervised Learning: Regression: Linear Regression, multi linear regression, Polynomial Regression, logistic regression, Non-linear Regression, Model evaluation methods. Classification: – support vector machines ( SVM) , Naïve Bayes classification
UNIT - V: Unsupervised learning
Nearest neighbor models – K-means – clustering around medoids – silhouettes – hierarchical clustering – k-d trees ,Clustering trees – learning ordered rule lists – learning unordered rule .
Reinforcement learning- Example: Getting Lost -State and Action Spaces.
An optional guide to configuring your Microsoft Azure environment for AI/ML projects.
Learn how to choose effective AI/ML model deployment strategies within Microsoft Azure.
Optional guidance on articulating reasons behind selecting specific AI/ML models.
An overview of the course structure and key topics covered in AI and ML infrastructure
Understand the architecture and importance of data sources and pipelines in AI workflows.
A detailed look at different types of data sources and pipeline architectures in AI.
Explore popular AI/ML frameworks and their practical uses in model development.
Learn the factors influencing the choice of AI/ML development frameworks.
The purpose of this assignment is to help learners evaluate and select the most suitable AI/ML development framework for a realistic, complex business problem. Learners will apply knowledge of framework capabilities, scalability, performance, and practical considerations to justify their choice.
Learn essential strategies for acquiring, cleaning, organizing, and securing data to ensure high-quality inputs for effective AI and Machine Learning models.
Effective data management is the backbone of successful Artificial Intelligence and Machine Learning projects. This module delves into the critical processes involved in acquiring, cleaning, organizing, and securing data to build reliable and accurate AI models. Learners will gain a comprehensive understanding of how data flows through AI systems and why managing it properly is essential for performance and trustworthiness.
The course begins by exploring various data sources commonly used in AI and ML, including structured databases, unstructured text, images, and sensor data. It covers practical methods for acquiring data responsibly, emphasizing legal and ethical considerations such as privacy and compliance. Learners will understand the importance of sourcing relevant, high-quality data as a foundation for model training.
Next, the module highlights data cleaning and preprocessing techniques that prepare raw data for analysis. Topics include handling missing values, removing duplicates, normalizing data, and encoding categorical variables. These steps are essential to improve data quality and ensure that models learn meaningful patterns rather than noise or bias.
A key feature of the module is the introduction to advanced AI tools and concepts such as Retrieval-Augmented Generation (RAG), which combines data retrieval with generative modeling to enhance AI capabilities. Learners will also discover best practices for maintaining efficient and scalable data sources tailored for RAG and other AI workflows.
Security and privacy considerations form another vital component of the course. Through expert insights, learners will understand how to protect sensitive data, prevent breaches, and comply with regulations like GDPR and HIPAA.
Finally, real-world case studies demonstrate how organizations implement robust data management strategies to achieve AI success. By the end of this module, learners will be equipped with the knowledge and skills needed to manage data effectively, ensuring their AI projects are built on a strong, trustworthy foundation.
Understand different types of data sources used in AI/ML systems and their role in building intelligent models.
Learn how data is collected using different techniques such as APIs, scraping, and data pipelines.
Discover why cleaning and preparing data is essential for accurate and reliable machine learning model.
An industry expert explains the importance of consistent data labeling and categorization
Learn the basics of Retrieval-Augmented Generation (RAG) and its role in modern AI systems
Explore strategies to manage and optimize data sources for RAG-based systems.
An expert discusses key security and privacy considerations in handling AI/ML data.
A recap of key concepts related to data management in AI/ML systems.
Real-world case study showcasing how organizations manage data for AI success
Audit a machine learning codebase to identify security vulnerabilities and recommend practical mitigation strategies to ensure safe and reliable AI/ML deployment.
This lesson introduces the core types of machine learning models including regression, classification, and clustering. Students will understand how models learn from data and how different model types are used to solve real-world problems across industries.
This lesson explains the difference between supervised and unsupervised learning models. Students will learn how labeled and unlabeled data influence model training and how these approaches are applied in real-world scenarios.
This lesson focuses on regression models, explaining how they predict continuous values. Students will learn about linear regression, its applications, and how it is used in real-world prediction problems.
This lesson explains classification models, focusing on predicting categories. Students will learn how classification works, common algorithms, and how it is applied in areas like fraud detection and spam filtering.
This lesson introduces clustering techniques used in unsupervised learning. Students will learn how data is grouped based on similarity and how clustering is applied in customer segmentation and recommendation systems.
This lesson explains strategies for selecting the right model based on problem type, data, and performance requirements. Students will learn practical approaches used by data scientists.
This lesson introduces Scikit-learn, a widely used Python library for machine learning. Students will learn how it simplifies model development, training, and evaluation, making it an ideal starting point for beginners.
This lesson introduces TensorFlow and PyTorch, two powerful frameworks used for deep learning. Students will understand their features, differences, and real-world applications in building advanced AI systems.
This lesson explains the process of training and validating machine learning models. Students will learn how to split data, avoid overfitting, and ensure models perform well on unseen data.
This lesson focuses on evaluating and comparing machine learning models using performance metrics. Students will learn how to select the best model based on accuracy, efficiency, and real-world requirements.
This assessment is designed to evaluate learners’ understanding of fundamental machine learning concepts and their ability to apply these concepts in practical scenarios. It covers key topics such as supervised and unsupervised learning, regression, classification, clustering, and model evaluation techniques. Learners will analyze real-world problems, identify appropriate model types, and justify their selection of algorithms based on the given scenarios. Additionally, the assessment introduces commonly used machine learning frameworks such as Scikit-learn, TensorFlow, and PyTorch, enabling learners to understand their roles and differences. By completing this assignment, learners will strengthen their conceptual knowledge while developing the ability to make informed decisions in selecting suitable machine learning models and tools for real-world applications.
Learn the key factors and considerations for deploying AI/ML platforms effectively in real-world environments.
This document covers security best practices for AI/ML deployments, including risk assessment, secure data handling, and compliance recommendations — essential considerations for deploying ML platforms.
Learn key strategies for packaging and containerizing AI/ML models for reliable deployment.
Explore essential tools and frameworks used to deploy AI/ML models efficiently.
Step-by-step instructions on preparing a model for deployment in production environments.
Learn how Git and DVC help manage code, datasets, and machine learning models to ensure reproducibility, collaboration, and traceability in AI/ML projects
Practical guidance on applying version control to ensure reproducible AI/ML experiments.
In this assessment, learners will apply Git and DVC to a sample AI/ML project. They will practice committing code, tracking datasets, versioning models, and ensuring the entire workflow is reproducible. By completing this activity, learners gain practical experience with essential tools for maintaining reliability and consistency in ML development
Learn the key criteria for assessing AI/ML deployment platforms for different project needs.
Analyze examples of successful AI/ML model deployments in real-world applications.
Basic understanding of AI/ML deployment platforms, project requirements, and business considerations
This lesson explains the complete lifecycle of a machine learning project, from problem definition to deployment. Students will learn how real-world ML systems are built step by step using a structured workflow approach.
This lesson explores how recommendation systems work in real-world applications like Netflix and Amazon. Students will understand how machine learning models analyze user behavior to provide personalized recommendations.
This lesson explains how machine learning is used to detect spam emails. Students will learn how classification models are applied in real-world systems to improve communication security.
This lesson discusses common challenges faced in real-world machine learning systems, including data quality, bias, scalability, and model performance issues. Students will understand practical limitations and solutions.
This lesson guides students through building their own machine learning project. It integrates all concepts learned in the course and prepares students for real-world AI applications.
