Statistics & Linear Algebra For Machine Learning

Math Behind ML Algorithms | 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.ArraysVectorsDot productMagnitudeEigen vector, eigen valueCosine similarity …

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: Conditional Probability | Naive Bayes

Lecture 11 Conditional Probability | Naive Bayes

Section 12: Oultiers

Lecture 12 Outliers

Section 13: Machine Learning Concepts

Lecture 13 Machine Learning Concepts

Section 14: Measuring accuracy in algorithms

Lecture 14 Measuring accuracy in algorithms

Section 15: Maths behind regression

Lecture 15 Maths behind regression

Section 16: Linear Algebra in Machine Learning

Lecture 16 Linear Algebra in Machine Learning

Lecture 17 Eigen Vector and Eigen Value | Determinant of matrix

Lecture 18 Matrix Multiplication in Deep Learning

Lecture 19 Computing Magnitude, Angle and Projection of Vectors

Lecture 20 Cosine Similarity

Lecture 21 Convex Optimization and Gradient Descent

Lecture 22 Jacobian Matrix

Section 17: Difference between Machine Learning and Deep Learning in Mathematical Terms

Lecture 23 ML Vs DL in math terms

Section 18: Maths behind decision tree

Lecture 24 Maths behind decision tree

Section 19: Maths behind kNN

Lecture 25 Maths behind kNN

Section 20: Quiz

Section 21: Bonus Lecture

Lecture 26 Bonus Lecture

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