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Statistics for Machine Learning

Enroll in this statistics for machine learning course and its correlation analysis. Get ready for this interesting sesison by Dr. Abhinanda Sarkar, and give your career a success in the ML domain.

Instructor:

Dr. Abhinanda Sarkar
4.59
average rating

Ratings

Beginner

Level

3.0 Hrs

Learning hours

39.6K+
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Learners

Skills you’ll Learn

About this Course

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. Without statistics, it becomes nearly impossible to work on becoming an expert in this domain when working with real-time or industry-grade products. Hence, to understand the domain and actively implement it, statistics is very much the need of the hour for Machine Learning.

 

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. 

 

The University of Texas at Austin, in collaboration with Great Lakes Executive Learning, offers several Post Graduate courses in the field of Artificial Intelligence. Explore more about our Artificial Intelligence Course and enroll in it to earn a Postgraduate Certificate in the Artificial Intelligence and Machine Learning online course from the University of Texas and Great Lakes Executive Learning. This course is #1 ranked in India, which ensures you become a successful AI/ML professional with a comprehensive curriculum and industry-relevant projects.

 

Check out our PG Course in Machine learning Today.

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Course Outline

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
Exercise on Descriptive Statistics using Python

Our course instructor

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Dr. Abhinanda Sarkar

Faculty Director, Great Learning

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514.3K+ Learners
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17 Courses
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 technology and business professionals. He is a recipient of the ISI Alumni Association Medal, IBM Invention Achievement Awards, and the Radhakrishan Mentor Award from GE India

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4.59
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Statistics for Machine Learning

3.0 Learning Hours . Beginner

Why upskill with us?

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700+ free courses
In-demand skills & tools
access time
Free life time Access