De-Mystifying Math & Statistics For Machine Learning

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

Homepage

https://www.udemy.com/course/applied-math-for-ml/

De-Mystifying Math & Statistics For Machine Learning Fast Download
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