De-Mystifying Math & Statistics For Machine Learning

Math Behind Regression & Classification | Linear Algebra | Hypothesis Testing | ANOVA

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

Requirements

No prior experience is required. We will start from the very basics.

Description

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 MBackground and IntroductionThe 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 DispersionMean vs Standard DeviationPercentilesTypes of DataDependent vs independent variablesProbabilitySample Vs populationHypothesis testingConcept of stabilityTypes of distributionOutliersMaths behind machine learning algorithms like regression, decision tree and kNNGradient descent.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Central Tendency Vs Dispersion

Lecture 2 Central Tendency Vs Dispersion

Section 3: Percentiles

Lecture 3 Percentiles

Section 4: Dependent Vs Independent Variable

Lecture 4 Dependent Vs Independent Variable

Section 5: Types of Data

Lecture 5 Types of Data

Section 6: Sampling

Lecture 6 Sampling

Section 7: Hypothesis Testing

Lecture 7 Hypothesis Testing

Section 8: T Test

Lecture 8 T Test

Section 9: ANOVA

Lecture 9 ANOVA

Section 10: Confidence Level Vs Confidence Interval Vs Significance Level

Lecture 10 Confidence Level Vs Confidence Interval Vs Significance Level

Section 11: Oultiers

Lecture 11 Outliers

Section 12: Machine Learning Concepts

Lecture 12 Machine Learning Concepts

Section 13: Measuring accuracy in algorithms

Lecture 13 Measuring accuracy in algorithms

Section 14: Maths behind regression

Lecture 14 Maths behind regression

Section 15: Linear Algebra in Machine Learning

Lecture 15 Linear Algebra in Machine Learning

Section 16: Maths behind decision tree

Lecture 16 Maths behind decision tree

Section 17: Maths behind kNN

Lecture 17 Maths behind kNN

Section 18: Gradient Descent

Lecture 18 Gradient Descent

Section 19: Quiz

Section 20: Bonus Lecture

Lecture 19 Bonus Lecture

Data Scientists, Python Programmers, ML Practitioners, IT Managers managing data science projects