Statistics & Linear Algebra for Machine Learning

Testimonials about the course

• "Great course. It cleared all my doubts. I learned statistics previously from HK Dass sir's book, but I couldn't understand there relationship in data science and machine learning. Loved this course!" Rubayet A.

• "Simply amazing course where every basics are described clearly and precisely. Go for this course." Dipesh S

• "Es claro, preciso en los datos. Las ilustraciones son muy pedagógicas, sobre todo las analogías." . Héctor Marañón R.

• "Good for beginners like me to learn the concepts of Machine Learning and the math behind of it. Great to review this course again. Thanks." Clark D

• "Excelentes conceptos, enfocados hacia las investigaciín de base científica" Oscar M

Background and Introduction

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.

• Arrays

• Vectors

• Dot product

• Magnitude

• Eigen vector, eigen value

• Cosine similarity ...