What Will You Learn?

• Introduction to Artificial Intelligence: This section provides an overview of artificial intelligence, discussing its history and real-world applications. It highlights the significance of AI in various domains and sets the foundation for understanding the subsequent topics covered in the course.
• Machine Learning: In this module, you will learn the basics of machine learning. It covers different types of machine learning algorithms, including supervised learning (such as linear regression and logistic regression) and unsupervised learning (like clustering and dimensionality reduction). You will understand how these algorithms work and how they can be applied to solve real-world problems.
• Deep Learning and Neural Networks: This section focuses on deep learning, which is a subset of machine learning. You will explore the fundamentals of deep learning and delve into artificial neural networks. The course covers topics such as building and training neural networks for tasks like image recognition and natural language processing. Additionally, it introduces convolutional neural networks (CNNs) for computer vision and recurrent neural networks (RNNs) for sequential data analysis.
• Natural Language Processing (NLP): NLP is a crucial aspect of AI, and this module provides an understanding of the techniques used to process and understand human language. It covers the basics of NLP, including text analysis and language understanding. You will learn about applications like sentiment analysis, text classification, and language generation
• Computer Vision: Computer vision is the field of AI focused on understanding and interpreting visual data. This module explores computer vision basics, including object detection, image segmentation, and generative models. You will learn about the algorithms and techniques used in computer vision and their practical applications.
• Ethical Considerations in AI: This important module addresses the ethical considerations in AI development and deployment. It covers topics like fairness, bias, transparency, and privacy. You will explore the ethical implications of AI systems and gain an understanding of responsible AI practices.
• Hands-on Projects and Practical Exercises: Throughout the course, you will engage in hands-on projects and practical exercises to apply the concepts and techniques learned. These activities provide you with real-world experience and help reinforce your understanding of AI principles.