Udemy

Mathematics-Basics to Advanced for Data Science And GenAI

立即報名
  • 25,248 名學生
  • 更新於 10/2024
  • 可獲發證書
4.6
(3,238 個評分)
  • 提示:
  • 透過本站連結購買Udemy課程,本站將獲得推廣佣金,有助未來提供更多實用進修課程資訊給讀者。

課程資料

報名日期*
全年招生
課程級別
學習模式
修業期
22 小時 55 分鐘
教學語言
英語
授課導師
Krish Naik, KRISHAI Technologies Private Limited
證書
  • 可獲發
  • (證書的發放與分配,依課程提供者的政策及安排而定。)
評分
4.6
(3,238 個評分)
*提示: 以上時間表僅供參考,以機構網站為準。

課程簡介

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!

課程章節

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

課程內容

  • 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.

評價

  • 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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