Machine Learning Overview
The study of machine learning focuses on how to make computers behave without explicit programming. Self-driving cars, usable speech recognition, efficient web search, and a far better grasp of the human genome have all been made possible by machine learning in the last ten years. Today, machine learning is so commonplace that you probably utilise it numerous times every day without even realising it. It is considered by many academics to be the greatest strategy for advancing AI to human-level performance.The most efficient machine learning techniques will be covered in this course, and you’ll experience using them to your advantage. More importantly, you’ll obtain the practical knowledge required to swiftly and effectively apply these strategies to novel issues in addition to learning about the theoretical foundations of learning. Finally, you’ll discover some of Silicon Valley’s most innovative best practises in relation to AI and machine learning.
Machine Learning Curriculum
This course provides a complete introduction to machine learning, datamining, and statistical pattern recognition.
- Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
- (Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
- Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.