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✅ Prerequisites for Learners

To ensure participants can engage effectively with the course material, the following foundational knowledge and skills are recommended:

🧠 1. Programming Skills

• Language: Proficiency in Python is highly recommended due to its dominance in AI development.
• Familiarity with Python libraries such as:
• NumPy and Pandas (data handling)
• Matplotlib and Seaborn (visualization)
• Scikit-learn (machine learning)
• TensorFlow or PyTorch (for deep learning, optional but helpful)

📊 2. Mathematics & Statistics

A strong grasp of the mathematical foundations is essential, especially in:

• Linear Algebra: Vectors, matrices, dot products
• Calculus: Derivatives and gradients (especially for neural networks)
• Probability & Statistics: Distributions, Bayes Theorem, statistical inference

🖥️ 3. Computer Science Concepts

• Basic knowledge of data structures and algorithms (e.g., trees, graphs, recursion)
• Understanding of how data flows through programs
• Awareness of time and space complexity (not mandatory but helpful)

📁 4. Data Analysis Basics

• Understanding of data cleaning, feature selection, and transformation techniques
• Familiarity with exploratory data analysis (EDA)

🧾 Course Title: Artificial Intelligence – Principles and Practice

Course Description:

This comprehensive Artificial Intelligence (AI) course is designed to introduce students and professionals to the foundational theories, cutting-edge tools, and practical applications of AI in modern industries. The course blends academic rigor with hands-on learning, equipping participants to build intelligent systems capable of decision-making, problem-solving, perception, and learning.

Starting from the basic concepts of AI, the course delves into key domains such as machine learning, natural language processing, computer vision, expert systems, and deep learning. Through interactive coding exercises, real-world datasets, and industry-relevant case studies, learners will gain practical skills in implementing AI algorithms using Python and popular frameworks like Scikit-learn, TensorFlow, and PyTorch.

By the end of this course, participants will not only understand how AI systems are built and trained but also how they are deployed in sectors like healthcare, finance, marketing, robotics, and more.

🎯 Key Learning Objectives:

• Understand the core concepts and history of Artificial Intelligence.
• Learn the differences between narrow AI, general AI, and strong AI.
• Develop practical skills in machine learning using supervised and unsupervised algorithms.
• Implement neural networks and deep learning models with frameworks like TensorFlow or PyTorch.
• Gain insight into natural language processing (NLP) techniques for text and speech analysis.
• Explore computer vision fundamentals, including object detection and image classification.
• Learn about reinforcement learning, intelligent agents, and decision-making models.
• Examine real-world use cases and ethical implications of AI technologies.

👨‍💼 Who Should Enroll:

• Students and graduates from computer science, engineering, mathematics, or IT backgrounds.
• Software developers or data professionals seeking to pivot into AI.
• Business analysts or product managers looking to leverage AI in decision-making.
• Enthusiasts aiming to build a strong foundation in intelligent systems development.

💼 Career Pathways After Completion:

• AI Engineer
• Machine Learning Engineer
• Data Scientist
• NLP Specialist
• Computer Vision Engineer
• AI Research Assistant
• Automation Engineer