Statistics for Machine Learning

Statistical Learning is a branch of applied statistics that emerged in response to machine learning, emphasizing statistical models and assessment of uncertainty. Organizations are being deluged with numerical and non-numerical data and information. All of us in day-to-day routine use numbers in our calculations. Problems in business contain a great degree of the quantitative element in the form of facts and figures. It is essential to carry out data analysis and interpretation for effective decisions.

After doing this course you will have an understanding of Statistics, data and its distribution.

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Skills you will gain

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

Course Syllabus

Module 1

Statistics for Machine Learning

4.5 Hrs

1 Quiz
  • 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


Hypothesis Testing on Golf Equipement

Course certificate

Get Statistics for Machine Learning course completion certificate from Great learning which you can share in the Certifications section of your LinkedIn profile, on printed resumes, CVs, or other documents.