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Mathematics-Basics to Advanced for Data Science And GenAI

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  • 25,248 Students
  • Updated 10/2024
  • Certificate Available
4.6
(3,238 Ratings)
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Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
22 Hour(s) 55 Minute(s)
Language
English
Taught by
Krish Naik, KRISHAI Technologies Private Limited
Certificate
  • Available
  • *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
4.6
(3,238 Ratings)
2 views

Course Overview

Mathematics-Basics to Advanced for Data Science And GenAI

Build Strong math foundation with linear algebra,stats,probability,differential calculus for mastering data science

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:

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

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

  2. Linear Algebra Essentials:

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

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

  3. Probability Theory and Its Applications:

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

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

  4. Statistics for Data Analysis:

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

    • 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 Content

  • 20 section(s)
  • 97 lecture(s)
  • Section 1 Welcome To This Course
  • Section 2 Introduction To Linear Algebra
  • Section 3 Introduction To Functions And Transformation
  • Section 4 Inverse Functions Or Transformation
  • Section 5 Eigen Vectors And Eigen Values
  • Section 6 Equation Of a Line,Plane,Hyperplane
  • Section 7 Introduction To Statistics
  • Section 8 Descriptive Statistics
  • Section 9 Introduction To Probability
  • Section 10 Probability Distribution function And Types Of Distribution
  • Section 11 Inferential Stats and Hypothesis Testing
  • Section 12 Chi Square Test With Solved Exmaples
  • Section 13 Annova Test With Solved Examples
  • Section 14 Differential Calculus
  • Section 15 Power Rules And Derivative Rules
  • Section 16 Product Rules In Derivative
  • Section 17 Chain Rule Of Derivatives
  • Section 18 Application Of Linear algebra,Stats And Differential Calculus In Data Science
  • Section 19 Application Of Linear Algebra In Dimensionality Reduction
  • Section 20 Application Of Derivatives In Deep Learning Neural Network

What You’ll 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.

Reviews

  • A
    Anshi Srivastava
    5.0

    Interactive course helped me in understanding about stats more deeply

  • A
    Andrew Josselyn
    3.0

    It scares me that there were at least two instances where very simple derivatives were wrong. If someone didn't have a University level background and identified the errors, these mistakes could cause quite a bit of confusion. How many other mistakes are in this training if the instructor can't derive simple trig and polynomial functions?

  • Y
    YOKESWARAN S
    5.0

    Best course for the beginner to start the mathematics for data science

  • A
    Aravindkumar Devendran
    4.5

    Found intuitive ways of understanding many concepts that we have understood in a not so intuitive way for years.

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