Mathematics & Statistics for Machine Learning by SeaportAi .

Udemy course Mathematics & Statistics for Machine Learning by SeaportAi .

Mathematics & Statistics for Machine Learning is the best Udemy course on the market. With this offer they will be able to greatly improve their knowledge and become more competitive within the Other IT & Software category. Therefore, if you are looking to improve your Other IT & Software skills we recommend that you download Mathematics & Statistics for Machine Learning udemy course.

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Course data:

• Author: SeaportAi .
• Course rating: 4.6
• Category: Other IT & Software
• Modality: Online
• Status: Available
• Idiom: English

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Abouth SeaportAi .

Profile of Trainer Govind Kumar Summary

What the udemy Mathematics & Statistics for Machine Learning course teaches?

What you’ll learn You will understand the fundamentals of mathematics and statistics relevant for machine learning You will gain insights on the application of math and stats on machine learning You will know what problems Machine Learning can solve, and how the Machine Learning Process works You will learn Measures of Central Tendency vs Dispersion You will understand Mean vs Standard Deviation & Percentiles You will have clarity on the Types of Data & Dependent vs independent variables You will be knowledgeable on Probability & Sample Vs population You will gain clarity on Hypothesis testing You will learn the Types of distribution & Outliers You will understand the maths behind algorithms like regression, decision tree and kNN You will gain insights on optimization and gradient descent Show more Show less

Learn these concepts First before learning Machine Learning | With English Subtitles

More information about the course Mathematics & Statistics for Machine Learning

The trainer of this course is an AI expert and he has observed that many students and young professionals make the mistake of learning machine learning without understanding the core concepts in maths and statistics. This course will help to address that gap in a big way. Since Machine Learning is a field at the intersection of multiple disciplines like statistics, probability, computer science, and mathematics, its essential for practitioners and budding enthusiasts to assimilate these core concepts. These concepts will help you to lay a strong foundation to build a thriving career in artificial intelligence. This course teaches you the concepts mathematics and statistics but from an application perspective. It’s one thing to know about the concepts but it is another matter to understand the application of those concepts. Without this understanding, deploying and utilizing machine learning will always remain challenging. You will learn concepts like measures of central tendency vs dispersion, hypothesis testing, population vs sample, outliers and many interesting concepts. You will also gain insights into gradient decent and mathematics behind many algorithms. We cover the below concepts in this course: Measures of Central Tendency vs Dispersion Mean vs Standard Deviation Percentiles Types of Data Dependent vs independent variables Probability Sample Vs population Hypothesis testing Concept of stability Types of distribution Outliers Maths behind machine learning algorithms like regression, decision tree and kNN Gradient descent.

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