About Machine Learning Training
Machine learning is an area of artificial intelligence and computer science that includes the development of software and algorithms that can make predictions based on data. The software can make decisions and follow a path that is not specifically programmed. Machine learning is used within the field of data analytics to make predictions based on trends and insights in the data.
A prime example of the application of machine learning is the autonomous vehicle. Sensors around the vehicle deliver thousands of data points which are analyzed and processed to move the vehicle toward its destination. Collective data from thousands of self-driving cars can be used to improve vehicle safety and prevent accidents.
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) 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.
Machine learning Training Duration
Weekdays Training Classes (Monday to Friday)
Weekend Training Classes (Saturday, Sunday & Holidays)
Fast Track Training Program (5+ hours daily)
Fees,Course Content and Syllabus for Hadoop Training