Machine learning is one of the most exciting and rapidly growing fields in computer science. This course provides a comprehensive introduction to the fundamental concepts and techniques of machine learning. You will also learn about deep learning, a subset of machine learning that is driving major breakthroughs in fields like computer vision, natural language processing, and robotics.

Course Topics:

• Introduction to Machine Learning: What is machine learning, its history, and its applications
• Types of Machine Learning: Supervised learning, unsupervised learning, and reinforcement learning
• Data and ML Models: Data preprocessing, model selection, training, and evaluation
• Deep Learning: Neural networks, convolutional neural networks, and recurrent neural networks
• Deployment: Scalability, performance, and security of machine learning models
• Ethics and Biases: Fairness, privacy, and interpretability in machine learning

Learning Outcomes:

• Understand the fundamental concepts and techniques of machine learning
• Differentiate between the three main types of machine learning
• How to select the right machine learning model for a given problem and evaluate its performance
• What are deep learning techniques to solve complex problems
• What is deployment of machine learning models in a production environment
• Understand the ethical considerations and potential biases that arise in machine learning

Prerequisites: This course requires a basic understanding of programming concepts and mathematics, including calculus and linear algebra. Familiarity with Python programming language is also recommended.