WHAT YOU WILL LEARN

This course provides an in-depth understanding of Artificial Intelligence and Machine Learning, covering foundational concepts, key algorithms, practical applications, and hands-on experience. It is designed for individuals seeking to gain comprehensive knowledge in AIML and its impact on various industries. The course comprises 40 hours of instruction, including lectures, hands-on labs, and a project.

COURSE OBJECTIVES

METHODOLOGY

This program will be conducted with interactive lectures, PowerPoint presentations, discussions, and practical exercises.

COURSE CONTENTS

• Introduction to AI/ML (1 hour)
• Definition of AI and ML.
• Overview of AI/ML applications.
• History and Evolution of AI/ML (1 hour)
• Early milestones in AI/ML.
• Key developments leading to current technologies.
• Types of Machine Learning (2 hours)
• Supervised, unsupervised, and reinforcement learning.
• Examples and use cases.
• Common AI/ML Algorithms (2 hours)
• Overview of popular algorithms like linear regression, logistic regression, decision trees, and neural networks.
• Key developments leading to current technologies.
• Applications of AI/ML (2 hour)
• Real-world examples in various industries.

• Deep Dive into Supervised Learning (2 hours)
• Detailed exploration of common supervised learning algorithms.
• Implementation of linear regression, logistic regression, etc.
• Exploring Unsupervised Learning (2 hours)
• Introduction to clustering, dimensionality reduction, and principal component analysis (PCA).
• Hands-on exercises with unsupervised learning techniques.
• Deep Learning Basics (1 hour)
• Introduction to neural networks.
• Overview of Convolutional Neural Networks(CNNs) and Recurrent Neural Networks (RNNs).
• Case Studies in AI/ML (3 hour)
• Practical examples demonstrating AI/ML in real-world scenarios.

• Development Environment Setup (2 hour)
• Setting up Python, Jupyter Notebooks, Scikit-learn, TensorFlow, and PyTorch.
• Building Basic ML Models (2 hours)
• Implementing simple supervised learning models.
• Introduction to model training and evaluation.
• Exploring Deep Learning (2 hours)
• Understanding backpropagation and gradient descent.
• Model Evaluation and Optimization (2 hours)
• Metrics for model evaluation (accuracy, precision, recall).
• Hyperparameter tuning and model optimization techniques.

• Advanced Machine Learning Techniques (2 hours)
• Overview of advanced algorithms and techniques.
• Exploring ensemble learning, bagging, and boosting.
• Generative AI (1 hour)
• Demonstration/Hands-on on how to generate images, audio, codes using certain tools
• Prompt Engineering (1 hour)
• Tips on the best way to make prompts
• AI in Automation and Robotics (1.5 hours)
• Applications of AI in robotics and process automation.
• Discussion on AI/ML-driven optimization in industry.
• Emerging Trends in AI/ML (1.5 hours)
• Generative AI, AI-driven analytics, and other recent advancements.
• Discussion on AI in the cloud and AI powered services.
• Ethical Considerations in AI/ML (1 hour)
• Addressing bias, fairness, and ethical implications.
• Ensuring compliance and responsible AI/ML practices.

• Mini-Project Introduction (1 hour)
• Explanation of the final project and case study context.
• Guidance on project structure and requirements.
• Project Development (4 hours)
• Teams work on defining the problem and designing a solution.
• Application of AI/ML techniques to a real world scenario.
• Project Presentations (2 hours)
• Teams present their project findings and results.
• Feedback and discussion on project outcomes.
• Course Wrap-Up and Certification (1 hour)
• Summary of key concepts and learning outcomes.
• Final remarks and course certificates