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Course Highlights

  • Broad introduction to machine learning and statistical pattern recognition

  • Key topics covered: Basics concepts of machine learning, Evaluating and debugging learning algorithms and more

  • Learn from expert instructors of Standford University

Duration: 65 Days

Mode of learning: Online self study

Course Overview

Who should do this course ?
  • Prerequisites - Linear algebra, basic probability and statistics.

What are the course deliverables ?
  • Basics concepts of machine learning

  • Generative learning algorithms

  • Evaluating and debugging learning algorithms

  • Bias/variance tradeoff and VC dimension

  • Value and policy iteration

  • Q-learning and value function approximation

More about this course
  • "Artificial Intelligence is the new electricity." -Andrew Ng, Stanford Adjunct Professor

  • Computers are becoming smarter, as artificial intelligence and machine learning, a subset of AI, make tremendous strides in simulating human thinking. Creating computer systems that automatically improve with experience has many applications including robotic control, data mining, autonomous navigation, and bioinformatics.

  • This course provides a broad introduction to machine learning and statistical pattern recognition. Learn about both supervised and unsupervised learning as well as learning theory, reinforcement learning and control. Explore recent applications of machine learning and design and develop algorithms for machines.

I am Interested


Average Rating


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Parul Garg

Machine Learning for beginners

Overall the course is great and the instructor is awesome. Machine learning is fascinating and I now feel like I have a good foundation. A few minor comments: some of the projects had too much helper code where the student only needed to fill in a portion of the algorithm. I would have preferred to have worked through more of the code. Also, there were a few times when the slides didn't contain the complete equations so it was difficult to piece it all together when writing the code. Lastly, I wish that there was more coverage on vectorized solutions for the algorithms.
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