Machine Learning

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.

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From Coursera
Institution Stanford University
Instructor Andrew Ng
Price Free (with limitations) or $79 for a Verified Certificate
Language English (English)
Subjects Data Science Machine Learning
5.00 based on 1 review

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Reviews for this course


Simoe says:

Amazing. I learned regression (linear and logistic), k-means clustering, and principal components analysis in uni, but seeing it in an ML context reinforced the grasp I have over those topics. Neural networks were entirely new to me and by far one of the most interesting concepts I've learned from anywhere!

What learners are saying BETA

support vector machines

Topics include: Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks)…

The course broadly covers all of the major areas of machine learning -…

For example, for Support Vector Machines and backpropagation for neural networks.

… logistic regression, Support Vector Machines (SVM), anomaly detection, non-supervised learning (clustering, K…

mentor tom mosher

Course mentor Tom Mosher's contributions are particularly valuable.Great material and teaching style…

hours per week

...I imagine I will have to learn other programming languages after this course.As for time…

I would probably budget at least 5 hours per week.

I think I spent about 20 hours per week to try to absorb the material.

I would estimate that it took about 4-5 hours per week to complete…

balance between theory

It has a perfect balance between theory and practice.

!I wish there was a training exercise for weeks 10-11…

Great balance between theory and practical examples.Very high quality lectures and exercises …

There is a good balance between theory and practice, with programming assignments most weeks.

The topics were very tractable.Complex concepts explained with ease and clarity…

knew nothing about

...some can still be quite challenging.I knew nothing about ML before I joined…

I knew nothing about Machine Learning before starting the course.

I knew nothing about ML and now comfortable with ML.

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