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Coursera Course: Machine Learning


           

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Coursera Course: Machine Learning

University: Stanford

Machine Learning Classroom Session by Tutorteddy.com:

April 4, 2019 4pm to 5 pm

Machine Learning Classroom Session by Tutorteddy.com

Even though the course from www.coursera.com provides the necessary guidance needed by the students, they will need some extra assistance outside the given course guidelines. In order to understand the lessons better a student will need some actual class sessions. These classroom sessions are provided by tutorteddy.com based on the courses of Coursera. Students can interact with professional instructors can get their queries answered and learn faster.

Coursera Course Description:

To learn about the most effective machine learning techniques and using them as an application to the real world you need to put into practice the various tools of it. This course is intended to help you to know every detail of Machine Learning and its applications.

What is Machine Learning?

It is a science that helps the computer to compute or act without any explicit programming.


How It has Helped Us?

Machine Learning has been a very effective and a popular topic since the past decade, it has provided us with practical speech recognition, self-driving cars, effective web search and great improved understanding of the human's mind. In today's world, Machine Learning has become so necessary, that we may use it a lot many times a day without our knowledge. According to many researchers, it has proved to be the most effective way to progress in a better way towards human level.

How This Course will Help You in Learning?

The course will help you to learn the techniques related to machine learning and through this you will get to know how to use and implement them as an application. This course is designed in such a way that you become knowledgeable about both the detailed theories and practical. Practical study will help you to apply the theoretical knowledge and you will get to know how the techniques are used quickly and powerfully as an application to the new problems. At the end you will come across some of the Silicon Valley's best practices as a further practice set that relates to the machine learning and AI.


This Machine Learning course introduces us to machine learning, data mining and the recognition of statistical pattern.


Topics included are as follows:

  • Supervised learning: It includes the various parametric and non-parametric algorithms, the use of kernels, support vector machines, and the use of neutral networks.

  • Unsupervised learning: This includes the study of clustering, the techniques to reduce dimensions, deep learning and the recommender systems.

  • Best Practices in Machine Learning: Includes the theory of bias and variance, the procedure in machine learning and AI.

A good number of case studies and applications are provided in the course that helps us to learn the way to apply the algorithms to build up smart robots signifying perception and control, understanding text in the form of web search and anti-spam, medical informatics, computer vision, audio and data mining and other areas of study.


 

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