Course:Artificial Intelligence (AI) - Online

→ Prerequisite:A strong understanding of programming, mathematics, and statistics is recommended. Knowledge of Python programming is essential.

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→ Rationale: Artificial Intelligence is a rapidly growing field with vast applications. This course will introduce you to the foundations of AI, machine learning, and deep learning, and equip you with the knowledge and skills needed to work in this exciting field.

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	content
→	Unit 1: Introduction to AI 1.1 - Overview of Artificial Intelligence. 1.2 - Historical development and significance. 1.3 - Ethical and societal considerations in AI.
→	Unit 2: Machine Learning Fundamentals 2.1 - Machine learning vs. traditional programming. 2.2 - Types of machine learning (supervised, unsupervised, reinforcement learning). 2.3 - Data preprocessing and feature engineering.
→	Unit 3: Supervised Learning 3.1 - Regression and classification. 3.2 - Linear and logistic regression. 3.3 - Decision trees, support vector machines, and ensemble methods.
→	Unit 4: Unsupervised Learning 4.1 - Clustering algorithms (K-Means, Hierarchical, DBSCAN). 4.2 - Dimensionality reduction techniques (PCA). 4.3 - Association rule mining and anomaly detection.
→	Unit 5: Deep Learning and Neural Networks 5.1 - Introduction to neural networks. 5.2 - Building and training deep learning models. 5.3 - Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
→	Unit 6: Natural Language Processing (NLP) 6.1 - Text preprocessing and tokenization. 6.2 - Building NLP models for tasks like sentiment analysis, text generation, and machine translation..
→	Unit 7: Reinforcement Learning 7.1 - Introduction to reinforcement learning. 7.2 - Markov Decision Processes and Q-learning. 7.3 - Applications in game playing and robotics.
→	Unit 8: AI Ethics and Future Trends 8.1 - Ethical considerations in AI development. 8.2 - Emerging trends and applications in AI. 8.3 - Preparing for a career in AI.