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All You Need to Know About Machine Learning Course at Stanford University


Machine learning is the field of science that explores ways to make machines react to a situation or set of inputs without being explicitly programmed for it. Stanford University offers a course on machine learning, which covers:

  • Theories behind enabling machine learning
  • Most effective machine learning techniques
  • Practical experience in applying theoretical lessons
  • Silicon Valley’s best practices with respect to machine learning

Here is a low down on all you need to know about the course.

Scope

In terms of machine learning, a machine is expected to recognize patterns from data stored in it and react to situations even if it is not specifically programmed for that. So, machine learning concepts overlap with those in data mining and artificial intelligence.

The course at Stanford broadly covers basics of machine learning, data mining, and pattern recognition.

Duration

The curriculum is taught over a period of 11 weeks, each week having quizzes and assignments besides the lectures.

Syllabus

The course has numerous case studies and hands-on applications for you. You will not just study the theory but also get to apply this to build small robots, enhance text understanding or even create data mining applications.

Here’s a week-wise breakup:

  • Week 1: Linear Regression with One Variable and Linear Algebra Review
  • Week 2: Linear Regression with Multiple Variables and Octave Tutorial
  • Week 3: Logistic Regression and Regularization
  • Weeks 4&5: Neural Networks
  • Week 6: Advice for Applying Machine Learning and Machine Learning System Design
  • Week 7: Support Vector Machines
  • Week 8: Unsupervised Learning and Dimensionality Reduction
  • Week 9: Anomaly Detection and Recommender Systems
  • Week 10: Large Scale Machine Learning
  • Week 11: Application Example: Photo OCR

To complete this course, all you need to do is pass all graded assignments.

Happy Learning!!