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Machine Learning Basics

Jaekwang KIM

In this course, you will:

a) understand the basic concepts of machine learning.

b) understand a typical memory-based method, the K nearest neighbor method.

c) understand linear regression.

d) understand model analysis.

Please make sure that you’re comfortable programming in Python and have a basic knowledge of mathematics including matrix multiplications, and conditional probability.

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What's inside

Syllabus

The basic concepts of machine learning

The k-Nearest Neighbors

Linear Regression

Logistic Regression

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Suitable for those just beginning to learn the fundamentals of machine learning

Provides a strong foundation for those interested in pursuing a career in machine learning

Taught by experienced instructors in the field, providing students with valuable insights

Requires basic knowledge in mathematics and programming in Python, making it accessible to learners with a technical foundation

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Machine Learning Basics with these activities:

Brush up on Python programming basics

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Ensure a solid foundation in Python programming to support your learning throughout the course.

Browse courses on Python Programming

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Review basic syntax, data types, and control flow
Practice working with lists, dictionaries, and other data structures
Complete coding exercises to test your understanding

Review Mathematics for Machine Learning

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Review this book for an introduction to the mathematical skills and concepts used in this machine learning course.

View Mathematics for Machine Learning on Amazon

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Read the book's first three chapters
Complete the exercise problems at the end of each chapter

Connect with experts in machine learning

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Enhance your learning by connecting with experienced professionals in the field of machine learning.

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Attend online or in-person meetups and conferences
Reach out to professors, researchers, or industry practitioners
Join online communities and forums related to machine learning

Five other activities

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K-Nearest Neighbors practice problems

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Reinforce your understanding of K-Nearest Neighbors by solving various practice problems, focusing on distance metrics and parameter optimization.

Browse courses on K-Nearest Neighbors

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Implement the K-NN algorithm from scratch using a programming language of your choice
Experiment with different distance metrics, such as Euclidean distance and cosine similarity
Tune the value of K to optimize the accuracy of your model

Explain linear regression to a non-technical audience

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Deepen your understanding of linear regression by explaining its concepts and applications to a non-technical audience.

Browse courses on Linear Regression

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Write a clear and concise explanation of the linear regression model
Provide real-world examples of how linear regression is used in practice
Illustrate the process of training and evaluating a linear regression model

Build a resource library for machine learning

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Organize your learning resources and stay up-to-date by compiling a comprehensive library of machine learning materials.

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Collect relevant articles, tutorials, and online courses
Use tools like Mendeley or Zotero to organize your references
Create a website or online repository to share your resource library

Apply your machine learning skills to a social impact project

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Contribute to the community while enhancing your learning by applying your machine learning skills to a meaningful project.

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Identify a social cause or organization that aligns with your interests
Develop a machine learning solution that addresses a specific problem
Collaborate with others to implement and evaluate your solution

Contribute to an open-source machine learning project

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Gain practical experience and contribute to the machine learning community by participating in open-source projects.

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Find a project that aligns with your interests and skills
Review the project's documentation and codebase
Identify an area where you can make a meaningful contribution
Submit a pull request with your proposed changes

Career center

Learners who complete Machine Learning Basics will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy machine learning models. This course can help you learn the basics of machine learning, which is essential for this role. You will learn about different machine learning algorithms, how to train and evaluate models, and how to deploy models to production.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use machine learning and other statistical methods to extract insights from data. This course can help you build a foundation in machine learning, which is a critical skill for Data Scientists. You will learn about different machine learning algorithms, how to train and evaluate models, and how to use machine learning to solve real-world problems.

See salaries and explore the career path for Data Scientist

Data Analyst

A Data Analyst collects, cleans, and analyzes data to help businesses make informed decisions. This course can help you build a foundation in machine learning, which is a valuable skill for Data Analysts. Machine learning can be used to automate data analysis tasks, such as identifying patterns and trends. This can help Data Analysts save time and improve the accuracy of their work.

See salaries and explore the career path for Data Analyst

Quantitative Analyst

Quantitative Analysts use mathematical and statistical methods to analyze financial data and make investment decisions. This course can help you build a foundation in machine learning, which is increasingly being used in quantitative finance. You will learn about different machine learning algorithms, how to train and evaluate models, and how to use machine learning to make investment decisions.

See salaries and explore the career path for Quantitative Analyst

Statistician

Statisticians collect, analyze, and interpret data. This course can help you build a foundation in machine learning, which is increasingly being used in statistics. You will learn about different machine learning algorithms, how to train and evaluate models, and how to use machine learning to solve statistical problems.

See salaries and explore the career path for Statistician

Underwriter

Underwriters assess risk and determine the terms of insurance policies. This course can help you build a foundation in machine learning, which is increasingly being used in underwriting. You will learn about different machine learning algorithms, how to train and evaluate models, and how to use machine learning to improve underwriting decisions.

See salaries and explore the career path for Underwriter

Operations Research Analyst

Operations Research Analysts use mathematical and statistical methods to improve the efficiency of business operations. This course can help you build a foundation in machine learning, which is increasingly being used in operations research. You will learn about different machine learning algorithms, how to train and evaluate models, and how to use machine learning to solve operations research problems.

See salaries and explore the career path for Operations Research Analyst

Software Engineer

Software Engineers design, develop, and maintain software applications. This course can help you build a foundation in machine learning, which is increasingly being used in software development. You will learn about different machine learning algorithms, how to train and evaluate models, and how to deploy models to production.

See salaries and explore the career path for Software Engineer

Financial Analyst

Financial Analysts use data to help businesses make better financial decisions. This course can help you build a foundation in machine learning, which is increasingly being used in finance. You will learn about different machine learning algorithms, how to train and evaluate models, and how to use machine learning to solve financial problems.

See salaries and explore the career path for Financial Analyst

Risk Analyst

Risk Analysts use data to help businesses identify and mitigate risks. This course can help you build a foundation in machine learning, which is increasingly being used in risk management. You will learn about different machine learning algorithms, how to train and evaluate models, and how to use machine learning to solve risk management problems.

See salaries and explore the career path for Risk Analyst

Data Engineer

Data Engineers design, build, and maintain data pipelines. This course can help you build a foundation in machine learning, which is increasingly being used in data engineering. You will learn about different machine learning algorithms, how to train and evaluate models, and how to deploy models to production.

See salaries and explore the career path for Data Engineer

Actuary

Actuaries use mathematical and statistical methods to assess risk and uncertainty. This course can help you build a foundation in machine learning, which is increasingly being used in actuarial science. You will learn about different machine learning algorithms, how to train and evaluate models, and how to use machine learning to solve actuarial problems.

See salaries and explore the career path for Actuary

Marketing Analyst

Marketing Analysts use data to help businesses make better marketing decisions. This course can help you build a foundation in machine learning, which is increasingly being used in marketing. You will learn about different machine learning algorithms, how to train and evaluate models, and how to use machine learning to solve marketing problems.

See salaries and explore the career path for Marketing Analyst

Product Manager

Product Managers are responsible for the development and launch of new products. This course can help you build a foundation in machine learning, which is increasingly being used in product management. You will learn about different machine learning algorithms, how to train and evaluate models, and how to use machine learning to improve product development.

See salaries and explore the career path for Product Manager

Business Analyst

Business Analysts use data to help businesses make better decisions. This course can help you build a foundation in machine learning, which is increasingly being used in business analysis. You will learn about different machine learning algorithms, how to train and evaluate models, and how to use machine learning to solve business problems.

See salaries and explore the career path for Business Analyst

Reading list

We've selected 14 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Machine Learning Basics.

Probabilistic Machine Learning

Save

Provides a comprehensive and mathematically rigorous treatment of machine learning from a probabilistic perspective. It valuable resource for learners who want to gain a deeper understanding of the theoretical foundations of machine learning.

Probabilistic Machine Learning: Advanced Topics...

Hardcover

$$$$

The Elements of Statistical Learning

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Provides a comprehensive overview of statistical learning methods, which are closely related to machine learning methods. It valuable resource for learners who want to gain a deeper understanding of the statistical foundations of machine learning.

The Elements of Statistical Learning: Data Mining,...

Hardcover

$$$

The Elements of Statistical Learning: Data Mining,...

Hardcover

$$$

The Elements of Statistical Learning: Data Mining,...

Kindle Edition

$$$

Deep Learning

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Provides a comprehensive overview of deep learning, a subfield of machine learning that has gained significant popularity in recent years. It valuable resource for learners who want to gain a deep understanding of deep learning algorithms and their applications.

Machine Learning Basics

What's inside

Syllabus

Good to know

Save this course

Activities

Career center

Reading list

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