Fundamental Mathematics & Statistics for Machine Learning
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Brief Introduction
Learn these concepts First before learning Machine LearningDescription
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 basic course will help to address that gap in a big way.
Since Machine Learning is a field at the intersection of statistics, probability, computer science, and mathematics, its essential for practitioners and budding enthusiasts to assimilate these core concepts.
This course teaches you the basics of mathematics and statistics but from an application perspective. Its 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.
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
Requirements
- Requirements
- No prior experience is required. We will start from the very basics.
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