Course Description

"Intro to Machine Learning" is an entry-level course that provides an overview of one of the most rapidly evolving and exciting fields of computer science. The course introduces students to the principles, algorithms, and applications of machine learning, a subfield of artificial intelligence that allows machines to learn from data and make predictions or decisions based on patterns and rules. Machine learning has revolutionized many industries, from finance and healthcare to gaming and transportation, and has enabled new technologies such as self-driving cars, recommender systems, and image recognition. The course begins with an introduction to the basic concepts of machine learning, including supervised and unsupervised learning, classification and regression, and overfitting and underfitting. Students learn about the different types of data used in machine learning, such as structured and unstructured data, and explore various techniques for data preprocessing, cleaning, and feature engineering. They also learn how to evaluate the performance of machine learning models using metrics such as accuracy, precision, recall, and F1 score. The course then covers some of the most widely used machine learning algorithms, such as decision trees, k-nearest neighbors, support vector machines, and neural networks. Students learn how these algorithms work, what are their strengths and weaknesses, and how to choose the right algorithm for a given problem. They also learn about the training and testing phases of machine learning, and how to use tools such as scikit-learn, TensorFlow, and Keras to build and evaluate machine learning models. Throughout the course, students work on hands-on projects and assignments that allow them to apply their knowledge to real-world problems. For example, they may build a spam classifier that can filter out unwanted emails, a movie recommender system that suggests movies based on a user's preferences, or a fraud detection system that identifies fraudulent credit card transactions. These projects help students develop their programming skills, their problem-solving abilities, and their creativity. By the end of the course, students have a solid understanding of the fundamentals of machine learning, and are able to build and evaluate simple machine learning models. They are also well-equipped to further their studies in machine learning, or to apply their knowledge to their own projects or work. Whether you're a computer science student, a data analyst, or a curious learner, "Intro to Machine Learning" is a great way to start your journey into the exciting world of machine learning. Author: (Kaggle)