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
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
Types of Data
Dependent vs independent variables
Sample Vs population
Concept of stability
Types of distribution
Maths behind machine learning algorithms like regression, decision tree and kNN