[Beginner] - Introduction to Machine Learning - Andrew Ng

[rsn]

 

Salut,

 

Un curs introductiv despre Machine Learning, predat de Andrew Ng:

https://www.coursera.org/learn/machine-learning

 

Can I take this course for free?

You can access all videos, readings, and discussions, free of charge. You can also submit assignments and earn a grade for free. If you want to earn a Course Certificate, you can subscribe or apply for financial aid.

 

About Andrew Ng:

Andrew was a professor at Stanford University Department of Computer Science. He taught students and undertook research related to data mining and machine learning. From 2011 to 2012, he worked at Google, where he founded and led the Google Brain Deep Learning Project. In 2012, he co-foundedCoursera to offer free online courses for everyone. In 2014, he joinedBaidu as Chief Scientist, and carried out research related to big data and A.I. In March 2017, he announced his resignation from Baidu. - https://en.wikipedia.org/wiki/Andrew_Ng

 

About this course:

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.

[urs]

Am inceput cursul. Multumesc ca mi-ai amintit de el.

Alles Gute!

[rsn]

5 hours ago, urs said:

Am inceput cursul. Multumesc ca mi-ai amintit de el.

Alles Gute!

Cu placere. Si eu l-am inceput recent. E o introducere buna in conceptele fundamentale. Are review-uri bune si e recomandat de multe persoane care au mai calcat in domeniu.

[u0m3]

Pentru cei interesati, tot Andrew Ng preda si o serie de cursuri mai avansate despre Deep Learning.

https://www.coursera.org/specializations/deep-learning