Mathematics-Basics to Advanced for Data Science And GenAI

Mathematics-Basics to Advanced for Data Science And GenAI, available at $54.99, has an average rating of 4.67, with 96 lectures, based on 194 reviews, and has 3680 subscribers.

You will learn about Master Calculus: Understand derivatives and integrals, and apply them in optimizing machine learning algorithms and data analysis tasks. Learn Linear Algebra: Grasp vectors, matrices, and eigenvalues, essential for building and understanding advanced data science models. Understand Probability: Dive into probability theory, crucial for making informed predictions and working with uncertainty in data. Apply Statistics: Gain practical skills in statistical analysis, helping you make data-driven decisions and interpret results effectively. This course is ideal for individuals who are Aspiring Data Scientists: Individuals looking to build a strong mathematical foundation essential for mastering data science and machine learning. or Data Science Beginners: Those who are new to data science and want to understand the core mathematical concepts that drive data science algorithms. or Professionals Transitioning into Data Science: Engineers, analysts, or professionals from other fields seeking to acquire the mathematical skills necessary for a career shift into data science. or Students and Academics: Students pursuing studies in data science, computer science, or related fields who need a comprehensive understanding of mathematics for data science applications. or Lifelong Learners: Anyone with a passion for learning and a desire to understand how mathematics powers the world of data science, even without prior experience in the field. or This course is tailored to equip learners with the essential mathematical tools needed to excel in data science, regardless of their current level of expertise. It is particularly useful for Aspiring Data Scientists: Individuals looking to build a strong mathematical foundation essential for mastering data science and machine learning. or Data Science Beginners: Those who are new to data science and want to understand the core mathematical concepts that drive data science algorithms. or Professionals Transitioning into Data Science: Engineers, analysts, or professionals from other fields seeking to acquire the mathematical skills necessary for a career shift into data science. or Students and Academics: Students pursuing studies in data science, computer science, or related fields who need a comprehensive understanding of mathematics for data science applications. or Lifelong Learners: Anyone with a passion for learning and a desire to understand how mathematics powers the world of data science, even without prior experience in the field. or This course is tailored to equip learners with the essential mathematical tools needed to excel in data science, regardless of their current level of expertise.

Enroll now: Mathematics-Basics to Advanced for Data Science And GenAI

Summary

Title: Mathematics-Basics to Advanced for Data Science And GenAI

Price: $54.99

Average Rating: 4.67

Number of Lectures: 96

Number of Published Lectures: 96

Number of Curriculum Items: 96

Number of Published Curriculum Objects: 96

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Master Calculus: Understand derivatives and integrals, and apply them in optimizing machine learning algorithms and data analysis tasks.

Learn Linear Algebra: Grasp vectors, matrices, and eigenvalues, essential for building and understanding advanced data science models.

Understand Probability: Dive into probability theory, crucial for making informed predictions and working with uncertainty in data.

Apply Statistics: Gain practical skills in statistical analysis, helping you make data-driven decisions and interpret results effectively.

Who Should Attend

Aspiring Data Scientists: Individuals looking to build a strong mathematical foundation essential for mastering data science and machine learning.

Data Science Beginners: Those who are new to data science and want to understand the core mathematical concepts that drive data science algorithms.

Professionals Transitioning into Data Science: Engineers, analysts, or professionals from other fields seeking to acquire the mathematical skills necessary for a career shift into data science.

Students and Academics: Students pursuing studies in data science, computer science, or related fields who need a comprehensive understanding of mathematics for data science applications.

Lifelong Learners: Anyone with a passion for learning and a desire to understand how mathematics powers the world of data science, even without prior experience in the field.

This course is tailored to equip learners with the essential mathematical tools needed to excel in data science, regardless of their current level of expertise.

Target Audiences

Aspiring Data Scientists: Individuals looking to build a strong mathematical foundation essential for mastering data science and machine learning.

Data Science Beginners: Those who are new to data science and want to understand the core mathematical concepts that drive data science algorithms.

Professionals Transitioning into Data Science: Engineers, analysts, or professionals from other fields seeking to acquire the mathematical skills necessary for a career shift into data science.

Students and Academics: Students pursuing studies in data science, computer science, or related fields who need a comprehensive understanding of mathematics for data science applications.

Lifelong Learners: Anyone with a passion for learning and a desire to understand how mathematics powers the world of data science, even without prior experience in the field.

This course is tailored to equip learners with the essential mathematical tools needed to excel in data science, regardless of their current level of expertise.

Are you eager to dive into the world of data science but feel overwhelmed by the mathematical concepts involved? Welcome to the “Complete Maths to Learn Data Science” course, your comprehensive guide to mastering the essential mathematical foundations needed to excel in data science and machine learning.

This course is designed to bridge the gap between your current math skills and the level required to understand and implement data science algorithms effectively. Whether you are a beginner or an experienced professional looking to strengthen your mathematical understanding, this course will equip you with the tools you need to succeed.

What You Will Learn:

1. Calculus for Data Science:

2. Understand the fundamentals of calculus, including derivatives, integrals, and limits.

3. Learn how these concepts are applied in optimizing machine learning algorithms, such as gradient descent, and in understanding complex data transformations.

4. Linear Algebra Essentials:

5. Gain a deep understanding of vectors, matrices, eigenvalues, and eigenvectors.

6. Discover how these linear algebra concepts are crucial for data manipulation, dimensionality reduction (like PCA), and building advanced machine learning models.

7. Probability Theory and Its Applications:

8. Dive into the world of probability, including concepts like random variables, distributions, and Bayes’ Theorem.

9. Explore how probability forms the backbone of predictive modeling, classification algorithms, and risk assessment in data science.

10. Statistics for Data Analysis:

11. Master key statistical techniques such as hypothesis testing, regression analysis, and statistical inference.

12. Learn to make data-driven decisions by understanding and applying statistical methods to real-world datasets.

Why This Course?

This course stands out by focusing on the clarity and practical application of mathematical concepts in data science. Each topic is broken down into simple, easy-to-understand modules that build on one another. You will not only learn the theory but also see exactly how these mathematical tools are used in real data science scenarios.

Throughout the course, you’ll engage with interactive quizzes, assignments, and hands-on projects designed to reinforce your understanding. By applying what you learn in real-world projects, you’ll gain practical experience and build a portfolio that showcases your newly acquired skills.

Who Is This Course For?

Aspiring Data Scientists: Individuals looking to build a strong mathematical foundation essential for mastering data science and machine learning.

Data Science Beginners: Those new to the field who want to understand the core mathematical concepts that drive data science algorithms.

Professionals Transitioning into Data Science: Engineers, analysts, or professionals from other fields seeking to acquire the mathematical skills necessary for a career shift into data science.

Students and Academics: Students pursuing studies in data science, computer science, or related fields who need a comprehensive understanding of mathematics for data science applications.

Lifelong Learners: Anyone with a passion for learning and a desire to understand how mathematics powers the world of data science, even without prior experience in the field.

Enroll Today!

Join thousands of learners who have transformed their careers by mastering the mathematics behind data science. Whether you’re aiming to start a new career, enhance your skills, or simply satisfy your curiosity, this course will provide the solid mathematical foundation you need to succeed. Enroll now and take the first step towards becoming a confident and skilled data scientist!

Course Curriculum

Chapter 1: Welcome To This Course

Lecture 1: What We are Going To Learn

Chapter 2: Introduction To Linear Algebra

Lecture 1: Introduction

Lecture 2: Scalars And Vectors

Lecture 3: Addition Of Vectors

Lecture 4: Multiplication Of Vectors

Lecture 5: Vector Databases- Examples Of Cosines similarity

Lecture 6: Vectors Multiplication-Element Wise Multiplication

Lecture 7: Vectors Multiplication-Scaler Multiplication

Lecture 8: Introduction To Matrices And Application

Lecture 9: Matrices Operation

Chapter 3: Introduction To Functions And Transformation

Lecture 1: Introduction To Function And Linear Transformation

Lecture 2: Vector Transformations

Lecture 3: Linear Transformation

Lecture 4: Why Linear Transformation?

Lecture 5: Linear Transformation Visualization

Lecture 6: Vector Length And Vector Unit

Lecture 7: Introduction To Projection

Chapter 4: Inverse Functions Or Transformation

Lecture 1: Inversion Functions

Lecture 2: Applications of Function And Inverse Function

Lecture 3: How to find Inverse Of A Matrix

Chapter 5: Eigen Vectors And Eigen Values

Lecture 1: All You need to know about Eigen Values And Eigen Vectors

Chapter 6: Equation Of a Line,Plane,Hyperplane

Lecture 1: Equation OF a Line,Plane And Hyperplane

Chapter 7: Introduction To Statistics

Lecture 1: Introduction To Statistics

Lecture 2: Types Of Statistics

Lecture 3: Population And Sample Data

Lecture 4: Types Of Sampling

Lecture 5: Types Of Data

Lecture 6: Scales OF Measurement Of Data

Chapter 8: Descriptive Statistics

Lecture 1: Measure Of Central Tendency

Lecture 2: Measure Of Dispersion

Lecture 3: Why Sample Variance is Divided By N-1

Lecture 4: Random Variables

Lecture 5: Percentile And Quartiles

Lecture 6: 5 Number Summary

Lecture 7: Histogram And Skewness

Lecture 8: Correlation And Covariance

Chapter 9: Introduction To Probability

Lecture 1: Addition Rule In Probability

Lecture 2: Multiplication Rule In Probability

Chapter 10: Probability Distribution function And Types Of Distribution

Lecture 1: PDF,PMF,CDF

Lecture 2: Types Of Probability Distribution

Lecture 3: Bernoulli Distribution

Lecture 4: Binomial Distribution

Lecture 5: Poisson Distribution

Lecture 6: Normal Gaussian Distribution

Lecture 7: Standard Normal Distribution and Z score

Lecture 8: Uniform Distribution

Lecture 9: Log Normal Distribution

Lecture 10: Power Law Distribution

Lecture 11: Pareto Distribution

Lecture 12: Central Limit Theorem

Lecture 13: Estimates

Chapter 11: Inferential Stats and Hypothesis Testing

Lecture 1: Hypothesis Testing And Its Mechanism

Lecture 2: P value and Hypothesis Testing

Lecture 3: Z test And Hypothesis Testing

Lecture 4: Student T Distribution

Lecture 5: T stats With t Test Hypothesis Testing

Lecture 6: Z test vs T test

Lecture 7: Type 1 and Type 2 Error

Lecture 8: Bayes Theorem

Lecture 9: Confidence Interval And Margin OF Error

Chapter 12: Chi Square Test With Solved Exmaples

Lecture 1: What is Chi Square Test

Lecture 2: Chi Square Goodness OF Fit

Chapter 13: Annova Test With Solved Examples

Lecture 1: What is Annova

Lecture 2: Assumptions Of Annova

Lecture 3: Types OF Annova

Lecture 4: Partioning OF Variance In Anova

Chapter 14: Differential Calculus

Lecture 1: What are Slopes and How To Calculate

Lecture 2: Introduction To Derivatives

Lecture 3: Mathematical Notation Of Derivatives With Limits

Lecture 4: Finding a Derivative At a Point with Examples

Chapter 15: Power Rules And Derivative Rules

Lecture 1: Power Rules In Derivative

Lecture 2: Derivative Rules- Constant,Sum,Difference And Scaler Multiplication

Lecture 3: Equation Of Tangent Of Polynomials

Lecture 4: Derivatives Of Trignometric,Logarithmic and Exponential Functions

Chapter 16: Product Rules In Derivative

Lecture 1: Product Rules In Derivative with Exmaples

Chapter 17: Chain Rule Of Derivatives

Lecture 1: Chain Rule Of Derivatives

Lecture 2: Composition Of 3 or many functions

Lecture 3: Application Of Chain Rule Of Derivative

Chapter 18: Application Of Linear algebra,Stats And Differential Calculus In Data Science

Lecture 1: Main Aim Of This Section

Lecture 2: Learning First ML Algorithm- Simple Linear Regression

Lecture 3: Understanding Linear Regression Equations

Lecture 4: Cost Functions In Regression

Instructors

Krish Naik
Chief AI Engineer

KRISHAI Technologies Private Limited
Artificial intelligence and machine learning engineer

Rating Distribution

1 stars: 0 votes

2 stars: 4 votes

3 stars: 8 votes

4 stars: 53 votes

5 stars: 129 votes

Frequently Asked Questions

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