Machine Learning
Faculty: Department of Computer Science and Engineering
Batch: 2024
Description
This course covers the fundamentals of machine learning, including supervised and unsupervised learning, regression, classification, clustering, and neural networks. Students will learn how to apply machine learning algorithms to various applications such as image and speech recognition, natural language processing, and recommendation systems. The course will also cover techniques for data preparation, feature engineering, and model evaluation. By the end of the course, students should be able to apply machine learning techniques to solve real-world problems.
Projects
References
- Machine Learning course by Andrew Ng
- Pattern Recognition and Machine Learning by Christopher M. Bishop
Books
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
- Python Machine Learning by Sebastian Raschka and Vahid Mirjalili