Over 2Mil students have enrolled in Machine Learning from Stanford.

Andrew Ng’ Machine Learning: Master the Fundamentals

 

 

 

Andrew No-Cofounder of coursera 

 

 

CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist, Baidu and founding lead of Google Brain

This course is the most popular course offered on Coursera with a 4,91/5 rating in the Top 20 Highest Rated Courses with at least 35,000 Enrollments and ≥4.6 Star Rating!

Machine learning is the science of getting computers to act without being explicitly programmed. Machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome in the past decade. 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 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, data mining, 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; 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.

 

This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. The course will also draw from numerous case studies and applications to 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.

 

 

 

Topics Include:

 

  • 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)

 

Skills Gained in this Course:

 

  • Logistic Regression

  • Artificial Neural Network

  • Machine Learning (ML) Algorithms

  • Machine Learning

 

 

 

Key Information About This Course:

 

  • 100% online: start instantly and learn at the schedule

  • Flexible schedule and deadlines: Set and maintain flexible deadlines

  • Approx. 55 hours to complete: Suggested: 7 hours/week

  • Subtitles: English, Chinese, Hebrew, Spanish, Hindi, Japanese

Andrew Ng’ Machine Learning: Master the Fundamentals

mahdis-mousavi-data analyst in stream of data flowing like a waterfall -unsplash

Very well structured and delivered course. Progressive introduction of concepts and intuitive description by Andrew really give a sense of understanding even for the more complex area of the training.

Excellent starting course on machine learning. Beats any of the so-called programming books on ML. Highly recommend this as a starting point for anyone wishing to be an ML programmer or data scientist.

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