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Best Machine Learning Course By Stanford University For Free

Machine Learning Course

It is the science of getting computers to act without being explicitly programmed. within 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. In this post, you will get an extremely beneficial machine learning course by Stanford University for free.

In brief, it finds patterns in existing data, then creates and uses a model that recognizes those patterns in new data.

Machine learning

Find patterns in the data

Use those patterns to predict the future

Example:-

Detecting credit card fraud

Determining whether a consumer is likely to switch to a competitor

Deciding when to do preventive maintenance on a factory robot

Doing machine learning well requires:-

Lots of data

Lots of compute power

Effective machine learning algorithm

Who is interested in machine learning?

Business leader

Software developer

Data scientists

Statistics

Machine learning software

Some problem domain

Best Machine Learning Course By Stanford University For Free

Machine learning is so pervasive today that you simply probably use it dozens of times each day without knowing it. Many researchers also think it’s the simplest thanks to making progress towards human-level AI. during this class, you’ll study the foremost effective machine learning techniques, and gain practice implementing them and getting them to figure for yourself.

More importantly, you’ll study not only the theoretical underpinnings of learning but also gain the sensible know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you’ll study a number of Silicon Valley’s best practices in innovation because 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; innovation process in machine learning and The course also will draw from numerous case studies and applications, in order that you’ll also find out how to use learning algorithms to put together smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

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