Statistics for Machine Learning

About this course

An understanding of basic statistical concepts provides a strong foundation for further learning in the fields of data analysis, data science and even some areas of machine learning. This course covers the basics of descriptive statistics, and teaches you more advanced concepts such as hypothesis testing and Bayes’ theorem. The course also explains in a simple manner the various kinds of statistical distributions and how to apply them to business problems.

Skills covered

  • Basic Statistics
  • Hypothesis Testing
  • Bayes’ Theorem
  • Binomial Distribution
  • Poisson Distribution
  • Normal Distribution

Course Syllabus

Statistics for Machine Learning

  • Introduction to Statistics
  • Why statistics is so important
  • Big Data
  • The four pillars of Business Analytics in details
  • Data Vs information
  • Frequency distribution and plots
  • Central tendency_Mean_Median and Mode
  • Measures of Dispersion and Range_Standard Deviation
  • The five number summary and boxplots
  • Probability concepts Uncertainty and Volatility
  • Example for Rules Addition Multiplication Marginal
  • Bayes Theorem
  • Probability Distributions
  • Binomial Distribution using Python
  • Poisson Distribution
  • Poisson Distribution using Python
  • Normal Distribution and its exercises in Excel
  • Normal Distribution using Python
  • Hypothesis Testing

Statistics for Machine Learning

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