Free Machine Learning Crash Course by Google

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Here is a good news for Computer Science students who are very much interested in learning new technology. Here is a chance to take a free course offered by Google on Machine Learning with Tensor Flow API’s.

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Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. Machine learning can also be unsupervised and be used to learn and establish baseline behavioral profiles for various entities and then used to find meaningful anomalies.

This course is a self-study guide for aspiring machine learning practitioners, Machine Learning Crash Course features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises. This crash course is divided into 25 lessons which includes 40+ exercises. There is total of 15 hours of tutorial available, these lectures are created by Google researchers based on real world case studies.

Some of the questions answered in this course

Learn best practices from Google experts on key machine learning concepts.
  • How does machine learning differ from traditional programming?

  • What is loss, and how do I measure it?

  • How does gradient descent work?

  • How do I determine whether my model is effective?

  • How do I represent my data so that a program can learn from it?

  • How do I build a deep neural network?

Prerequisites

Machine Learning Crash Course does not presume or require any prior knowledge in machine learning. However, to understand the concepts presented and complete the exercises, we recommend that students meet the following prerequisites:

  • Mastery of intro-level algebra. You should be comfortable with variables and coefficients, linear equations, graphs of functions, and histograms. (Familiarity with more advanced math concepts such as logarithms and derivatives is helpful, but not required.)
  • Proficiency in programming basics, and some experience coding in Python. Programming exercises in Machine Learning Crash Course are coded in Python using TensorFlow. No prior experience with TensorFlow is required, but you should feel comfortable reading and writing Python code that contains basic programming constructs, such as function definitions/invocations, lists and dicts, loops, and conditional expressions.

Join the Machine Learning Crash Course Now and Learn New,

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