Probability for Machine Learning – The Easiest Way of Learning

Probability is the measure of the likelihood that an event will occur, to know and understand the chances of various events and plan things accordingly. In this course, we will discuss the various concepts in Probability and their application in AI and ML.

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Probability for Machine Learning – The Easiest Way of Learning
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Includes
  • 2 hands-on practice exercises
  • 282 knowledge based questions
  • Lifetime access
About the Course

Machine Learning is an interdisciplinary field that uses statistics, probability, algorithms to learn from data and provide insights which can be used to build intelligent applications.

In this course, you will learn about probability, different types of distribution and density functions, joint distributions and density functions and the different moments, parameter estimation and minimum risk estimation and the application of probability in the field of Data Science and ML.

Course Objectives

Upon successful completion of the course, the learner will be able to

  • Master the various concepts in Probability.

  • Learn how Probability plays an important role in prediction and decision making in AI and ML algorithms.

Course Content
Probability Distributions and Densities, Choosing and Applying Probability Densities

In this topic, you will learn about different probability distribution and density functions. You will learn about choosing and applying appropriate Probability Densities functions.

  • 4 Hrs
  • 132 Problems
Joint Distributions and Densities, Moments

In this topic, you will learn about joint distributions and Densities.You will also understand the various order of Moments.

  • 1 Hrs
  • 42 Problems
Parameter Estimations, Minimum Risk Estimations

In this topic, you will learn about choosing the correct parameter through parameter estimation and risk estimation for a given process.

  • 15 Mins
  • 27 Problems
Applications in Data Science and ML

In this topic, you will learn about the application of Probability in the field of Data Science and ML.

  • 1 Hrs
  • 12 Problems
Probability of Events, Conditional Probabilities, Random variables and Random Processes

In this topic, you will learn about introduction to probability and the various terms associated with it. You will understand conditional probability by solving various problems in random variables and finding probabilities for random processes.

  • 2 Hrs
  • 71 Problems
You will Learn through our Scientifically Designed & Proven 5 Step Practice Oriented Learning Process
iLearn
In this session, you will find video lectures and other resources to learn the concepts.
iDesign
In this session, you will start designing and creating your own programs.
iExplore
In this session, you will find interesting set of activities which will make you explore more on the specific topic.
iAssess
In this session, you will have activities to self assess your knowledge and skills on the specific topic.
iAnalyse
In this session, you will find varieties of exercises to improve your code analysis, testing and debugging skills.
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I love the way E- Box teaches topics in such detail. The practical examples along with the Practice quizzes makes me skilled in each

Harihara Budra P, XII std

Harihara Budra P, XII std
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