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Want to study machine learning or artificial intelligence, but worried that your math skills may not be up to it? Do words like “algebra”and “calculus” fill you with dread? Has it been so long since you studied math at school that you’ve forgotten much of what you learned in the first place?
Read more
Want to study machine learning or artificial intelligence, but worried that your math skills may not be up to it? Do words like “algebra”and “calculus” fill you with dread? Has it been so long since you studied math at school that you’ve forgotten much of what you learned in the first place?
Read more
Want to study machine learning or artificial intelligence, but worried that your math skills may not be up to it? Do words like “algebra”and “calculus” fill you with dread? Has it been so long since you studied math at school that you’ve forgotten much of what you learned in the first place?
You’re not alone. Machine learning and AI are built on mathematical principles like Calculus, Linear Algebra, Probability, Statistics, and Optimization; and many would-be AI practitioners find this daunting. This course is not designed to make you a mathematician. Rather, it aims to help you learn some essential foundational concepts and the notation used to express them. By leveraging the power of Python, the course provides a hands-on approach to working with data and applying the techniques you’ve learned.
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:
OpenCourser.com/course/mqcebo/math
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What's inside
Learning objectives
- After completing this course, you will be familiar with the following mathematical concepts and techniques:
- Equations, functions, and graphs
- Differentiation and optimization
- Vectors and matrices
- Statistics and probability
- After completing this course, you will be familiar with the following mathematical concepts and techniques:
- Equations, functions, and graphs
- Differentiation and optimization
- Vectors and matrices
- Statistics and probability
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for those who need a refresher in mathematical concepts for machine learning and AI.
Engages Python to make data exploration more interactive and approachable
Covers key foundational concepts for learners who may not have a background in mathematics
Save this course
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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 Math for Machine Learning with Python with these
activities:
Review Basic Probability and Statistics
Show steps
Probability and statistics are fundamental concepts for machine learning and AI, so refreshing these skills will help you better understand the algorithms discussed in the course.
Browse courses on
Probability
Show steps
-
Review your notes or textbooks from previous courses on probability and statistics.
-
Solve practice problems to test your understanding.
-
Watch online videos or tutorials to refresh your memory on key concepts.
Review algebra
Show steps
Familiarize yourself with algebraic concepts used throughout the course.
Browse courses on
Algebra
Show steps
-
Go over basic algebraic operations
-
Practice solving simple equations
Review 'Essential Calculus: Early Transcendentals' by James Stewart
Show steps
This book provides a comprehensive overview of the fundamental concepts of calculus that are essential for understanding machine learning and AI algorithms.
View
Melania
on Amazon
Show steps
-
Read the first chapter to familiarize yourself with the notation and concepts of calculus.
-
Complete the practice problems at the end of each chapter to reinforce your understanding.
-
Review the solved examples throughout the book to see how calculus is applied to real-world problems.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Review calculus
Show steps
Refresh your knowledge of calculus concepts fundamental to this course.
Browse courses on
Calculus
Show steps
-
Review derivatives and integrals
-
Practice solving basic calculus problems
Solve Calculus Problems on Khan Academy
Show steps
Khan Academy provides a wide range of calculus practice problems that can help you improve your skills and solidify your understanding of the concepts covered in the course.
Browse courses on
Calculus
Show steps
-
Create a free account on Khan Academy.
-
Navigate to the Calculus section and select the topics you want to practice.
-
Complete the practice problems and review your answers to identify areas where you need more practice.
Watch Video Tutorials on Linear Algebra
Show steps
Video tutorials provide a visual and engaging way to learn the concepts of linear algebra, which is essential for understanding machine learning algorithms.
Browse courses on
Linear Algebra
Show steps
-
Search for reputable online courses or tutorials on linear algebra.
-
Watch the videos and take notes on the important concepts.
-
Pause the videos and try to solve the practice problems on your own.
Complete Online Tutorials on Python for Data Science
Show steps
Python is the primary programming language used in machine learning, so completing tutorials will help you become proficient in using it for data analysis and modeling.
Browse courses on
Python
Show steps
-
Choose a reputable online platform or course that offers Python tutorials for data science.
-
Follow the tutorials step-by-step and complete the exercises.
-
Apply the skills you learn to real-world data science projects.
Join a study group for machine learning
Show steps
Collaborate with peers to enhance your understanding of machine learning concepts.
Browse courses on
Machine Learning
Show steps
-
Find a study group or create your own
-
Meet regularly to discuss course material
Practice solving linear algebra problems
Show steps
Deepen your understanding of linear algebra through practice.
Browse courses on
Linear Algebra
Show steps
-
Solve systems of linear equations
-
Find eigenvalues and eigenvectors of matrices
Join a Study Group for the Course
Show steps
Engaging with other students in a study group can help you understand the material more deeply, identify areas where you need additional support, and stay motivated.
Show steps
-
Reach out to classmates or post on forums to find other students interested in forming a study group.
-
Establish regular meeting times and a study schedule.
-
Take turns leading discussions, presenting concepts, and solving problems.
Create a Mind Map of Machine Learning Concepts
Show steps
Creating a mind map will help you organize and visualize the key concepts of machine learning, making it easier to retain and understand the material.
Show steps
-
Start with a central concept and draw branches for related concepts.
-
Use different colors or symbols to represent different categories or levels of concepts.
-
Review and refine your mind map regularly as you learn new material.
Create a Python script for data visualization
Show steps
Apply your Python skills to create visual representations of data.
Browse courses on
Python
Show steps
-
Import data into Python
-
Use Python libraries to create visualizations
Follow tutorials on deep learning frameworks
Show steps
Expand your knowledge of deep learning by following guided tutorials.
Browse courses on
Deep Learning
Show steps
-
Find tutorials for your chosen deep learning framework
-
Follow the tutorials and implement the examples
Build a machine learning model for a real-world problem
Show steps
Apply your machine learning skills to solve a real-world problem.
Browse courses on
Machine Learning
Show steps
-
Define the problem and gather data
-
Choose and train a machine learning model
Review Basic Probability and Statistics
Show steps
Probability and statistics are fundamental concepts for machine learning and AI, so refreshing these skills will help you better understand the algorithms discussed in the course.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review your notes or textbooks from previous courses on probability and statistics.
- Solve practice problems to test your understanding.
- Watch online videos or tutorials to refresh your memory on key concepts.
Review algebra
Show steps
Familiarize yourself with algebraic concepts used throughout the course.
Browse courses on
Algebra
Show steps
Show steps
Show steps
- Go over basic algebraic operations
- Practice solving simple equations
Review 'Essential Calculus: Early Transcendentals' by James Stewart
Show steps
This book provides a comprehensive overview of the fundamental concepts of calculus that are essential for understanding machine learning and AI algorithms.
View
Melania
on Amazon
Show steps
Show steps
Show steps
- Read the first chapter to familiarize yourself with the notation and concepts of calculus.
- Complete the practice problems at the end of each chapter to reinforce your understanding.
- Review the solved examples throughout the book to see how calculus is applied to real-world problems.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Review calculus
Show steps
Refresh your knowledge of calculus concepts fundamental to this course.
Browse courses on
Calculus
Show steps
Show steps
Show steps
- Review derivatives and integrals
- Practice solving basic calculus problems
Solve Calculus Problems on Khan Academy
Show steps
Khan Academy provides a wide range of calculus practice problems that can help you improve your skills and solidify your understanding of the concepts covered in the course.
Browse courses on
Calculus
Show steps
Show steps
Show steps
- Create a free account on Khan Academy.
- Navigate to the Calculus section and select the topics you want to practice.
- Complete the practice problems and review your answers to identify areas where you need more practice.
Watch Video Tutorials on Linear Algebra
Show steps
Video tutorials provide a visual and engaging way to learn the concepts of linear algebra, which is essential for understanding machine learning algorithms.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Search for reputable online courses or tutorials on linear algebra.
- Watch the videos and take notes on the important concepts.
- Pause the videos and try to solve the practice problems on your own.
Complete Online Tutorials on Python for Data Science
Show steps
Python is the primary programming language used in machine learning, so completing tutorials will help you become proficient in using it for data analysis and modeling.
Browse courses on
Python
Show steps
Show steps
Show steps
- Choose a reputable online platform or course that offers Python tutorials for data science.
- Follow the tutorials step-by-step and complete the exercises.
- Apply the skills you learn to real-world data science projects.
Join a study group for machine learning
Show steps
Collaborate with peers to enhance your understanding of machine learning concepts.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find a study group or create your own
- Meet regularly to discuss course material
Practice solving linear algebra problems
Show steps
Deepen your understanding of linear algebra through practice.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Solve systems of linear equations
- Find eigenvalues and eigenvectors of matrices
Join a Study Group for the Course
Show steps
Engaging with other students in a study group can help you understand the material more deeply, identify areas where you need additional support, and stay motivated.
Show steps
Show steps
Show steps
- Reach out to classmates or post on forums to find other students interested in forming a study group.
- Establish regular meeting times and a study schedule.
- Take turns leading discussions, presenting concepts, and solving problems.
Create a Mind Map of Machine Learning Concepts
Show steps
Creating a mind map will help you organize and visualize the key concepts of machine learning, making it easier to retain and understand the material.
Show steps
Show steps
Show steps
- Start with a central concept and draw branches for related concepts.
- Use different colors or symbols to represent different categories or levels of concepts.
- Review and refine your mind map regularly as you learn new material.
Create a Python script for data visualization
Show steps
Apply your Python skills to create visual representations of data.
Browse courses on
Python
Show steps
Show steps
Show steps
- Import data into Python
- Use Python libraries to create visualizations
Follow tutorials on deep learning frameworks
Show steps
Expand your knowledge of deep learning by following guided tutorials.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Find tutorials for your chosen deep learning framework
- Follow the tutorials and implement the examples
Build a machine learning model for a real-world problem
Show steps
Apply your machine learning skills to solve a real-world problem.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Define the problem and gather data
- Choose and train a machine learning model
Career center
Learners who complete Math for Machine Learning with Python will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. They work closely with data scientists to understand the business problem and translate it into a technical solution. Math for Machine Learning with Python can help build a foundation in the programming concepts and techniques used in machine learning. By leveraging the power of Python, this course provides a hands-on approach to working with machine learning models and applying the techniques you’ve learned.
Data Scientist
Data Scientist
A Data Scientist spends most of their time collecting, analyzing, and interpreting complex data. Their goal is to build machine learning models that solve real-world business problems. Math for Machine Learning with Python can help build a foundation in the mathematical concepts and techniques that form the basis of machine learning. By leveraging the power of Python, this course provides a hands-on approach to working with data and applying the techniques you’ve learned, and can help advance you on your journey into becoming a data scientist.
Data Analyst
Data Analyst
A Data Analyst gathers, cleans, and analyzes data to identify trends and patterns. They use this information to make recommendations and inform decision-making. It is an entry-level role that can lead to more advanced roles, such as Data Scientist or Machine Learning Engineer. Math for Machine Learning with Python can help build a foundation in the mathematical concepts and techniques used in machine learning. By leveraging the power of Python, this course provides a hands-on approach to working with data and applying the techniques you’ve learned, and can help advance you on your journey into becoming a data analyst.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst, or Quant, uses mathematical and statistical models to analyze financial data. They use this information to make investment recommendations and inform trading decisions. Math for Machine Learning with Python can help build a foundation in the mathematical concepts and techniques used in machine learning. By leveraging the power of Python, this course provides a hands-on approach to working with financial data and applying the techniques you’ve learned.
Software Engineer
Software Engineer
A Software Engineer is responsible for designing, developing, and maintaining software applications. They work with a variety of programming languages and technologies to create software that meets the needs of users. Math for Machine Learning with Python can supplement your skillset by providing a foundation in the mathematical concepts and techniques that form the basis of machine learning. By leveraging the power of Python, this course provides a hands-on approach to working with data and applying the techniques you’ve learned.
Actuary
Actuary
An Actuary uses mathematical and statistical models to assess risk and uncertainty. They work in a variety of industries, including insurance, finance, and healthcare. Math for Machine Learning with Python can help build a foundation in the mathematical concepts and techniques used in machine learning. By leveraging the power of Python, this course provides a hands-on approach to working with data and applying the techniques you’ve learned.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and analytical techniques to solve problems in a variety of industries, including manufacturing, logistics, and healthcare. Math for Machine Learning with Python can help build a foundation in the mathematical concepts and techniques used in machine learning. By leveraging the power of Python, this course provides a hands-on approach to working with data and applying the techniques you’ve learned.
Statistician
Statistician
A Statistician collects, analyzes, and interprets data. They work in a variety of industries, including healthcare, finance, and education. Math for Machine Learning with Python can help build a foundation in the mathematical concepts and techniques used in machine learning. By leveraging the power of Python, this course provides a hands-on approach to working with data and applying the techniques you’ve learned.
Market Researcher
Market Researcher
A Market Researcher conducts research to understand consumer behavior and trends. They use this information to help businesses develop new products and services. Math for Machine Learning with Python can supplement your skillset by providing a foundation in the statistical concepts and techniques that form the basis of market research.
Business Analyst
Business Analyst
A Business Analyst uses data and analysis to identify and solve business problems. They work with a variety of stakeholders to develop and implement solutions. Math for Machine Learning with Python can help build a foundation in the mathematical concepts and techniques used in machine learning. By leveraging the power of Python, this course provides a hands-on approach to working with data and applying the techniques you’ve learned.
Data Engineer
Data Engineer
A Data Engineer designs, builds, and maintains data pipelines. They work with a variety of data sources and technologies to ensure that data is clean, accurate, and accessible. Math for Machine Learning with Python provides a foundation in the mathematical concepts and techniques that are used in data engineering. By leveraging the power of Python, this course provides the essential programming concepts and techniques that are used in data engineering.
Financial Analyst
Financial Analyst
A Financial Analyst analyzes financial data to make investment recommendations and inform business decisions. Math for Machine Learning with Python can help build a foundation in the mathematical concepts and techniques used in machine learning. By leveraging the power of Python, this course provides a hands-on approach to working with financial data and applying the techniques you’ve learned.
Risk Manager
Risk Manager
A Risk Manager identifies, assesses, and manages risk. They work in a variety of industries, including finance, insurance, and healthcare. Math for Machine Learning with Python can help build a foundation in the mathematical concepts and techniques used in machine learning. By leveraging the power of Python, this course provides a hands-on approach to working with data and applying the techniques you’ve learned.
Operations Manager
Operations Manager
An Operations Manager plans, organizes, and directs the operations of an organization. They work with a variety of stakeholders to ensure that the organization runs smoothly and efficiently. Math for Machine Learning with Python may be useful for understanding how to improve operations using data and analytics.
Product Manager
Product Manager
A Product Manager is responsible for the development and launch of new products. They work with a variety of stakeholders to define the product vision, roadmap, and launch strategy. Math for Machine Learning with Python may be useful for understanding how to use data and analytics to improve product development and launch.
For more career information including salaries, visit:
OpenCourser.com/course/mqcebo/math
Reading list
We've selected 12 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 Python.
A textbook that provides a comprehensive overview of the mathematical foundations of machine learning, including topics such as linear algebra, probability, and optimization.
A classic textbook on linear algebra, providing a solid foundation for understanding the mathematical concepts used in machine learning.
A textbook on calculus, covering topics such as derivatives, integrals, and optimization, which are essential for understanding machine learning algorithms.
A textbook on probability and statistics, providing a foundation for understanding the statistical models used in machine learning.
Save
A textbook on deep learning, providing a comprehensive overview of the field and its applications.
A practical guide to building and deploying machine learning models using popular Python libraries.
A textbook on mathematics for computer science, providing a foundation for understanding the mathematical concepts used in machine learning.
A textbook on machine learning from a probabilistic perspective, providing a deeper understanding of the statistical models used in machine learning.
A textbook on pattern recognition and machine learning, providing a comprehensive overview of the field and its applications.
A textbook on statistical learning with sparsity, providing a deeper understanding of the statistical models used in machine learning.
A textbook on convex optimization, providing a deeper understanding of the optimization techniques used in machine learning.
A textbook on machine learning algorithms, providing a comprehensive overview of the field and its applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/mqcebo/math
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Effort
6 to 8 hours per week for 6 weeks
Level
Intermediate
Via
edX
Institution
edX
Language
English
Transcript
English
Good to know
Suitable for those who need a refresher in mathematical concepts for machine learning and AI.
Engages Python to make data exploration more interactive and approachable
Covers key foundational concepts for learners who may not have a background in mathematics
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