Introduction to Artificial Intelligence

What Will You Learn?

• Understanding AI and ML: Gain insights into the definitions, principles, and applications of Artificial Intelligence and Machine Learning.
• Data Basics: Learn about the role of data in AI and ML, types of data, data preprocessing, and feature engineering.
• Machine Learning Algorithms: Explore various ML algorithms such as linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks.
• Model Evaluation and Validation: Understand how to assess the performance of ML models using metrics like accuracy, precision, recall, and F1-score.
• Unsupervised Learning: Delve into unsupervised learning techniques including clustering algorithms and dimensionality reduction.
• Deep Learning: Introduction to neural networks, deep learning architectures, and popular frameworks like TensorFlow and PyTorch.
• Practical Applications: Discover real-world applications of AI and ML across different industries such as healthcare, finance, autonomous vehicles, and natural language processing.
• Ethics and Bias: Discuss the ethical considerations, biases, and societal impacts associated with AI and ML technologies.