Course Details

This course is appropriate for a wide range of professionals but not limited to:

• IT professionals and software engineers looking to upskill in AI/ML.
• Data analysts or data scientists entering the AI/ML space.
• Technical project managers overseeing AI/ML projects.
• Business analysts seeking to understand AI-driven decision-making.
• Academics and researchers interested in AI applications.
• Anyone with a strong interest in learning AI/ML fundamentals (some programming background recommended).

• Expert-led sessions with dynamic visual aids
• Comprehensive course manual to support practical application and reinforcement
• Interactive discussions addressing participants’ real-world projects and challenges
• Insightful case studies and proven best practices to enhance learning

By the end of this course, participants should be able to:

• Understand the fundamental concepts and history of AI and ML.
• Distinguish between different types of learning: supervised, unsupervised, and reinforcement learning.
• Explore key ML algorithms and techniques (e.g., regression, classification, clustering).
• Build and evaluate basic ML models using Python and popular libraries (e.g., Scikit-learn, TensorFlow).
• Understand the ethical implications and responsible use of AI technologies.
• Apply AI and ML concepts to real-world problems and business use cases.

DAY 1

Introduction to Artificial Intelligence and Machine Learning

• Welcome and introduction
• Pre-test
• What is AI? What is ML? Key differences
• History and evolution of AI
• Types of AI (Narrow, General, Super)
• Applications of AI in real-world industries
• Overview of the ML pipeline
• Data, models, and learning types
• Lab session

DAY 2

Supervised Learning Techniques

• Supervised learning concepts
• Regression (Linear, Polynomial)
• Classification (Logistic Regression, KNN, Decision Trees)
• Model training, validation, and testing
• Evaluation metrics (Accuracy, Precision, Recall, F1 Score)
• Lab session

DAY 3

Unsupervised Learning and Clustering

• Introduction to unsupervised learning
• Clustering: K-Means, Hierarchical Clustering
• Dimensionality Reduction: PCA, t-SNE
• Anomaly detection techniques
• Lab session

DAY 4

Neural Networks and Deep Learning

• Basics of neural networks and perceptrons
• Deep learning and backpropagation
• Introduction to TensorFlow/Keras
• Convolutional Neural Networks (CNNs) for image processing
• Lab session

DAY 5

AI Applications, Ethics, and Future Trends

• Natural Language Processing (NLP) basics
• AI in business, healthcare, finance, and manufacturing
• Generative AI (e.g., ChatGPT, DALL·E) and its impact
• Ethics and responsible AI (bias, fairness, transparency)
• Future trends in AI & ML
• Final group project
• Q&A and wrap-up
• Post-test
• Certificate ceremony