Stay Ahead with Artificial Intelligence & Data Science

This course serves as a comprehensive introduction to the fundamental concepts and techniques of Artificial Intelligence (AI). It covers a wide range of topics related to Machine Learning, Natural Language Processing, Computer Vision, and problem-solving strategies.

Course Goals

• Understanding Artificial Intelligence Foundations: An overview of AI’s development, history, goals, and challenges.

AI Fundamentals

• An overview of both Supervised and Unsupervised Learning.
• Introduction to Classification and Regression algorithms.
• Hands-on experience training models for Machine Learning.

Learning by Doing

• Introduction to Neural Networks and Deep Learning.
• Understanding Convolutional (CNN) and Recurrent Neural Networks (RNN).
• Deep learning’s practical applications in speech and image recognition.

Natural Language Processing (NLP)

• Basics of NLP and its applications.
• Text preprocessing, sentiment analysis, and language translation.
• Introduction to Chatbots and Virtual Assistants.

Computer Vision

• Foundations in image processing.
• Object recognition and image classification using Computer Vision.
• Applications of Computer Vision in real-world scenarios.

Problem Solving and AI Ethics

• AI-based strategies for solving complex problems.
• Ethical considerations in AI development and deployment.
• Ethical dilemmas highlighted in industry case studies.

Robotics and AI Applications

• Introduction to Robotics and its integration with AI.
• AI’s practical applications in various sectors.
• Emerging trends and future directions in Artificial Intelligence.

Real-world Projects

• Implementation of AI algorithms using popular programming languages (e.g., Python).
• Creating and deploying AI models in real-world scenarios.

Assessments and Evaluation

• Tests, assignments, and a final capstone project.
• Evaluation of practical skills to apply AI concepts.

Prerequisites

Fundamental programming skills (ideally in Python), comprehension of essential mathematics (Linear Algebra, Probability), and a strong interest in Artificial Intelligence.

Suggested Reading Material

• "Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig
• "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron

Assessment Strategy

• Tests and Assignments: 40%
• Project: 30%
• Final Exam: 30%