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Math for Machine Learning with R
Are you ready to unlock the power of machine learning, but concerned about the level of your math skills? This course is tailored to equip you with the knowledge you need to succeed in the machine learning and AI space. Explore complex mathematical concepts with the help of our expert instructors who will guide you through hands-on exercises using R.
Read more
Are you ready to unlock the power of machine learning, but concerned about the level of your math skills? This course is tailored to equip you with the knowledge you need to succeed in the machine learning and AI space. Explore complex mathematical concepts with the help of our expert instructors who will guide you through hands-on exercises using R.
Read more
Are you ready to unlock the power of machine learning, but concerned about the level of your math skills? This course is tailored to equip you with the knowledge you need to succeed in the machine learning and AI space. Explore complex mathematical concepts with the help of our expert instructors who will guide you through hands-on exercises using R.
Mathematical principles such as Calculus, Linear Algebra, Probability, Statistics, and Optimization underpin machine learning and AI. This course is designed to fill the gaps for students who missed these key concepts as part of their formal education, or who need to refresh their memories after a long break from studying math. Demystify complex mathematical concepts with the help of our expert instructors who will guide you through hands-on exercises using R.
This course includes content authored by Microsoft Corporation. Copyright 2020-2023 Microsoft Corporation. All Rights Reserved.
Register for this course and see more details by visiting:
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What's inside
Learning objectives
- Review algebra fundamentals, quadratic equations, and functions.
- Delve into differential calculus foundations by exploring differentiation and derivatives.
- Harness the power of vectors and matrices to explore relationships.
- Gain insight into statistics fundamentals and probability.
- Review algebra fundamentals, quadratic equations, and functions.
- Delve into differential calculus foundations by exploring differentiation and derivatives.
- Harness the power of vectors and matrices to explore relationships.
- Gain insight into statistics fundamentals and probability.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Prior math study is not required, which makes the course suitable for absolute beginners
No prerequisites are required, making the course suitable for self-paced and independent learners
Emphasizes hands-on exercises, providing practical experience and reinforcement of concepts
Covers essential mathematical concepts for machine learning and AI, bridging the gap for students with limited mathematical backgrounds
Reviews algebra fundamentals, ensuring a strong foundation for understanding more complex concepts
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Activities
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Math for Machine Learning with R.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Math for Machine Learning with R will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist uses their deep understanding of mathematics and programming to solve complex problems using data. To be successful, it is crucial to have a solid foundation in Calculus, Linear Algebra, Probability, Statistics, and Optimization. This course covers these topics to provide you with the mathematical skills needed to excel as a Data Scientist.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, building, and deploying machine learning models. With the mathematical foundations provided in this course, you will be able to develop a solid understanding of the underlying principles of machine learning, enabling you to make informed decisions and create effective models.
Data Analyst
Data Analyst
Data Analysts utilize mathematical and statistical techniques to extract insights from data. To excel in this field, you need a strong foundation in probability, statistics, and optimization. This course helps build these foundations by teaching you how to apply mathematical concepts to real-world data analysis problems.
Quantitative Analyst
Quantitative Analyst
As a Quantitative Analyst, you will use math to analyze financial data and make investment decisions. The course covers Calculus, Linear Algebra, Probability, Statistics, and Optimization, which are essential for developing the mathematical skills needed to succeed in this field.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst applies mathematical techniques to improve the efficiency of business processes. This course covers mathematical concepts including Calculus, Linear Algebra, Probability, Statistics, and Optimization, which are foundational for developing the skills you will need in this role.
Business Analyst
Business Analyst
To succeed as a Business Analyst, it is important to understand the mathematical foundations of data analysis and decision-making. This course covers Calculus, Linear Algebra, Probability, Statistics, and Optimization, providing you with the mathematical skills necessary to analyze business problems, identify solutions, and make informed recommendations.
Software Engineer
Software Engineer
As a Software Engineer, you will need a strong foundation in mathematics, including Calculus, Linear Algebra, Probability, Statistics, and Optimization. This course covers these concepts, providing you with the mathematical knowledge to develop efficient and reliable software solutions.
Statistician
Statistician
A Statistician utilizes mathematical and statistical techniques to collect, analyze, and interpret data. This course covers Calculus, Linear Algebra, Probability, Statistics, and Optimization, providing you with a strong foundation in the mathematical concepts necessary to excel in this field.
Actuary
Actuary
Actuaries use mathematical and statistical skills to assess risk and develop financial plans. By covering Calculus, Linear Algebra, Probability, Statistics, and Optimization, this course provides you with the mathematical foundation needed to understand and apply actuarial principles in practice.
Financial Analyst
Financial Analyst
As a Financial Analyst, you will use mathematical and statistical techniques to analyze financial data and make investment recommendations. This course covers Calculus, Linear Algebra, Probability, Statistics, and Optimization, providing you with the mathematical skills needed to understand financial markets and make sound investment decisions.
Market Researcher
Market Researcher
Market Researchers use mathematical and statistical techniques to understand consumer behavior and market trends. This course covers Calculus, Linear Algebra, Probability, Statistics, and Optimization, providing you with the mathematical knowledge needed to design and conduct effective market research studies.
Risk Analyst
Risk Analyst
To be successful as a Risk Analyst, you need a solid foundation in mathematics, including Calculus, Linear Algebra, Probability, Statistics, and Optimization. This course covers these concepts to give you a deep understanding of risk assessment and management techniques.
Data Engineer
Data Engineer
Data Engineers use mathematical and statistical techniques to design, build, and maintain data systems. This course may be helpful as it covers Calculus, Linear Algebra, Probability, Statistics, and Optimization, providing you with foundational knowledge in data management and analysis.
Product Manager
Product Manager
Understanding mathematical concepts can enhance a Product Manager's ability to analyze data, make informed decisions, and communicate technical concepts to stakeholders. This course covers Calculus, Linear Algebra, Probability, Statistics, and Optimization, providing you with a foundation in mathematics that may be helpful in this role.
UX Designer
UX Designer
In UX Design, mathematical concepts can be applied to understand user behavior and improve user experience. This course covers Calculus, Linear Algebra, Probability, Statistics, and Optimization, which may provide foundational knowledge that can be useful in analyzing user data and designing effective user interfaces.
For more career information including salaries, visit:
OpenCourser.com/course/gmk87a/math
Reading list
We've selected nine 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
Math for Machine Learning with R.
Classic in the field of machine learning. It provides a comprehensive overview of the theory and practice of machine learning, including chapters on supervised learning, unsupervised learning, and model selection.
Great supplement to the course as it provides a comprehensive overview of reinforcement learning using Python. It covers a wide range of topics, from the basics of reinforcement learning to more advanced topics such as deep reinforcement learning.
Great supplement to the course as it provides a comprehensive overview of natural language processing using Python. It covers a wide range of topics, from the basics of natural language processing to more advanced topics such as machine translation and text summarization.
Great supplement to the course as it provides a gentle introduction to machine learning using R. It covers the basics of machine learning, including supervised learning, unsupervised learning, and model evaluation.
Great supplement to the course as it provides a comprehensive overview of the R programming language. It covers a wide range of topics, from the basics of R to more advanced topics such as data manipulation and visualization.
Great supplement to the course as it provides a hands-on introduction to machine learning using R. It covers a wide range of topics, from data preprocessing to model evaluation.
Great supplement to the course as it provides a comprehensive overview of deep learning using Python. It covers a wide range of topics, from the basics of deep learning to more advanced topics such as convolutional neural networks and recurrent neural networks.
Great supplement to the course as it provides a clear and concise introduction to probability and statistics. It covers the basics of probability and statistics, including probability distributions, Bayes' theorem, and hypothesis testing.
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Great supplement to the course as it provides a clear and concise introduction to optimization. It covers the basics of optimization, including gradient descent, Newton's method, and conjugate gradient methods.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/gmk87a/math
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Effort
6 to 8 hours per week for 6 weeks
Level
Intermediate
Via
edX
Institution
edX
Session
October 23, 2023
- October 31, 2024
Language
English
Transcript
English
Good to know
Prior math study is not required, which makes the course suitable for absolute beginners
No prerequisites are required, making the course suitable for self-paced and independent learners
Emphasizes hands-on exercises, providing practical experience and reinforcement of concepts
Covers essential mathematical concepts for machine learning and AI, bridging the gap for students with limited mathematical backgrounds
Reviews algebra fundamentals, ensuring a strong foundation for understanding more complex concepts
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