This course provides an introduction to the fundamentals of Machine Learning. Students will learn the basics of supervised and unsupervised learning, as well as the principles of data mining and predictive analytics. Topics covered include linear and logistic regression, decision trees, support vector machines, neural networks, clustering, and ensemble methods. Students will also gain hands-on experience with popular machine learning libraries such as scikit-learn and TensorFlow.
This course provides an introduction to the fundamentals of Machine Learning. It covers the basic concepts, algorithms, and techniques used in Machine Learning, and provides an overview of the various applications of Machine Learning. The course will cover topics such as supervised and unsupervised learning, linear and non-linear models, decision trees, neural networks, and deep learning. It will also discuss the various evaluation metrics used to measure the performance of Machine Learning models. The course will also provide hands-on experience with popular Machine Learning libraries such as Scikit-Learn, TensorFlow, and Keras. By the end of the course, students will have a good understanding of the fundamentals of Machine Learning and be able to apply them to real-world problems.