Machine Learning

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.

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Interested in learning Machine Learning for free? This top rated MOOC from Stanford University is the best place to start.

Course description: Machine Learning

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.

This course provides a broad introduction to machine learning and statistical pattern recognition.

Topics include

  • supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines);
  • unsupervised learning (clustering, dimensionality reduction, kernel methods);
  • learning theory (bias/variance tradeoffs; VC theory; large margins);
  • reinforcement learning and adaptive control.

The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.

Course content

In this MOOC, you will learn:

  • Linear regression with one variable
  • Linear Algebra review
  • Linear regression with multiple variables
  • Octave/Matlab tutorial
  • Logistic regression
  • Regularization
  • Neural networks: representation
  • Neural networks: learning
  • Advice for applying Machine Learning
  • Machine Learning system design
  • Support vector machines
  • Unsupervised learning
  • Dimensionality reduction
  • Anomaly detection
  • Recommender systems
  • Large scale Machine Learning
  • Application example: Photo OCR

Instructor : Stanford Professor Andrew Ng.

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