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Statistics for Machine Learning 

  • Offered byGreat Learning

Statistics for Machine Learning
 at 
Great Learning 
Overview

Duration

2 hours

Total fee

Free

Mode of learning

Online

Difficulty level

Beginner

Official Website

Explore Free Course External Link Icon

Credential

Certificate

Statistics for Machine Learning
 at 
Great Learning 
Highlights

  • Earn a certificate of completion
Details Icon

Statistics for Machine Learning
 at 
Great Learning 
Course details

What are the course deliverables?
  • Descriptive Statistics
  • Measures of Dispersion Range and IQR
  • Central Tendency and 3 Ms
  • The Empirical Rule and Chebyshev Rule
  • Correlation Analysis
More about this course
  • This free online statistics in machine learning course covers the basics of descriptive statistics and data visualizations
  • You will be learning about the importance of this functional concept called statistics in this vast domain
  • Statistics is a key requirement which acts as a foundation to build up for further concepts down the line, hence it makes it very vital that you understand this
  • It also explains the various kinds of statistical distributions and how to apply them to business problems in a simple manner
  • An understanding of basic statistics for machine learning concepts provides a strong foundation for further learning in the fields of data analysis, data science, and even some areas of machine learning

Statistics for Machine Learning
 at 
Great Learning 
Curriculum

Outline - Descriptive statistics

Data and Histogram

Central Tendency and 3 Ms

Measures of Dispersion Range and IQR

Standard Deviation

Coefficient of Variation

The Empirical Rule and Chebyshev Rule

Five Number Summary Boxplot and other plots

Data Visualizations

Correlation Analysis

Summary - Descriptive statistics

Exercise on Descriptive Statistics using Python

Faculty Icon

Statistics for Machine Learning
 at 
Great Learning 
Faculty details

Dr. Abhinanda Sarkar
Designation : Faculty Director, Great Learning Description : Dr. Abhinanda Sarkar is the Academic Director at Great Learning for Data Science and Machine Learning Programs. Dr. Sarkar received his B.Stat. and M.Stat. degrees from the Indian Statistical Institute (ISI) and a Ph.D. in Statistics from Stanford University. He has taught applied mathematics at the Massachusetts Institute of Technology (MIT); been on the research staff at IBM; led Quality, Engineering Development, and Analytics functions at General Electric (GE); served as Associate Dean at the MYRA School of Business; and co-founded OmiX Labs. Dr. Sarkar’s publications, patents, and technical leadership have been in applying probabilistic models, statistical data analysis, and machine learning to diverse areas such as experimental physics, computer vision, text mining, wireless networks, e-commerce, credit risk, retail finance, engineering reliability, renewable energy, and infectious diseases, His teaching has mostly been on statistical theory, methods, and algorithms; together with application topics such as financial modeling, quality management, and data mining. Dr. Sarkar is a certified Master Black Belt in Lean Six Sigma and Design for Six Sigma. He has been visiting faculty at Stanford and ISI and continues to teach at the Indian Institute of Management (IIM-Bangalore) and the Indian Institute of Science (IISc). Over the years, he has designed and conducted numerous corporate training sessions for
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Statistics for Machine Learning
 at 
Great Learning 
Students Ratings & Reviews

5/5
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S
SARAT KUMAR PRADHAN
Statistics for Machine Learning
Offered by Great Learning
5
Learning Experience: It was super
Faculty: Nice faculty Its lectures & agenda
Reviewed on 17 Mar 2023Read More
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Statistics for Machine Learning
 at 
Great Learning 

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