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Mathematics

Mathematical Statistics Basics for Non-Majors (Practical)

This course is a course that practices the basics of machine learning mathematics and statistics using Python. Machine learning and deep learning require programming skills, but they also require a mathematical background to understand the principles. Through this course, you can gradually learn the basic mathematics required for artificial intelligence.

19 learners are taking this course

Level Basic

Course period 12 months

  • SW School
함수
함수
미분
미분
3시간 만에 완강할 수 있는 강의 ⏰
3시간 만에 완강할 수 있는 강의 ⏰
Linear Algebra
Linear Algebra
Probability and Statistics
Probability and Statistics
Python
Python
Machine Learning(ML)
Machine Learning(ML)
Deep Learning(DL)
Deep Learning(DL)
함수
함수
미분
미분
3시간 만에 완강할 수 있는 강의 ⏰
3시간 만에 완강할 수 있는 강의 ⏰
Linear Algebra
Linear Algebra
Probability and Statistics
Probability and Statistics
Python
Python
Machine Learning(ML)
Machine Learning(ML)
Deep Learning(DL)
Deep Learning(DL)

What you will gain after the course

  • Taxi fare prediction using pseudoinverse matrix

  • Data representation using functions

  • Understand the concepts of probability, required trials, sample space, and events.

  • Calculate probability values from data and infer posterior probabilities for specific events.

Fundamentals of Mathematics and Statistics for Non-Majors <Practical Edition>


We will use Python's basic grammar to cover the theory and practice of differentiation, linear algebra, probability, and statistics .

Have you been pursuing a new career in fields like data analysis and artificial intelligence, but felt the need for a solid foundation in mathematics to understand the principles? Build a solid foundation from theory to practice, covering everything from differentiation, linear algebra, probability, and statistics.


*This course is a practical course, so please complete the theory section before taking it.

Learn about these things


function

  • Understand basic function terminology

  • Comparing mathematical and programming functions

  • Practice functional expressions on various data

  • Definition of the concept of linear function


  • Understanding various nonlinear functions

Differential theory

  • Differentiation Concepts and Definitions

  • Understanding derivatives

  • Geometric meaning of differentiation

Linear algebra theory

  • linear algebra

    Understanding operations, inverse matrices, and equations of lines

  • Understand the difference between scalars, vectors, and matrices

  • Understand square matrices, inverse matrices, identity matrices, and transpose matrices.

Statistics Basics

  • Understanding the scope of what is possible through statistical data analysis

  • Data distribution representation

  • Understand the concepts of trials, sample spaces, and events that require probability.

Distributional inference theory

  • Understanding the characteristics of the normal distribution

  • Calculating probability values with data


Things to note before taking the course

Player Knowledge and Precautions

  • Since this course is divided into theory and practice, you must apply for each course separately.

  • You need to have a basic understanding and knowledge of basic Python functions.

Recommended for
these people

Who is this course right for?

  • For people who are just starting out with programming

  • People who want to study the basic elements required for Python programming

Need to know before starting?

  • I recommend this to those who have completed the basics of mathematical statistics (theory) for non-majors.

  • I need to understand basic Python syntax.

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1,401

Learners

98

Reviews

5

Answers

4.7

Rating

15

Courses

Curriculum

All

5 lectures ∙ (2hr 6min)

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