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Luis Serrano
After completing this course, learners will be able to:
• Analytically optimize different types of functions commonly used in machine learning using properties of derivatives and gradients
Read more
After completing this course, learners will be able to:
• Analytically optimize different types of functions commonly used in machine learning using properties of derivatives and gradients
Read more
After completing this course, learners will be able to:
• Analytically optimize different types of functions commonly used in machine learning using properties of derivatives and gradients
• Approximately optimize different types of functions commonly used in machine learning using first-order (gradient descent) and second-order (Newton’s method) iterative methods
• Visually interpret differentiation of different types of functions commonly used in machine learning
• Perform gradient descent in neural networks with different activation and cost functions
Mathematics for Machine Learning and Data science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. This beginner-friendly program is where you’ll master the fundamental mathematics toolkit of machine learning.
Many machine learning engineers and data scientists need help with mathematics, and even experienced practitioners can feel held back by a lack of math skills. This Specialization uses innovative pedagogy in mathematics to help you learn quickly and intuitively, with courses that use easy-to-follow plugins and visualizations to help you see how the math behind machine learning actually works.
Upon completion, you’ll understand the mathematics behind all the most common algorithms and data analysis techniques — plus the know-how to incorporate them into your machine learning career.
This is a beginner-friendly program, with a recommended background of at least high school mathematics (functions, basic algebra). We also recommend a basic familiarity with Python (loops, functions, if/else statements, lists/dictionaries, importing libraries) , as labs use Python and Jupyter Notebooks to demonstrate learning objectives in the environment where they’re most applicable to machine learning and data science.
Register for this course and see more details by visiting:
OpenCourser.com/course/25mnt3/calculus
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What's inside
Syllabus
Week 1 - Derivatives and Optimization
After completing this course, you will be able to:
Week 2 - Gradients and Gradient Descent
Read more
Syllabus
Week 1 - Derivatives and Optimization
After completing this course, you will be able to:
Week 2 - Gradients and Gradient Descent
Read more
Week 3 - Optimization in Neural Networks and Newton's Method
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches differentiation, which is core to analyzing machine learning functions
Covers gradient descent, which helps learners optimize machine learning models
Explores Newton's method, which is useful for optimizing machine learning models
Proper for beginners with basic high-school math and some Python familiarity
Taught by Luis Serrano, an expert in mathematics for machine learning
Save this course
Save Calculus for Machine Learning and Data Science to your list so you can find it easily later:
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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 Calculus for Machine Learning and Data Science with these
activities:
Read 'Calculus for Machine Learning'
Show steps
Review the fundamental concepts of calculus, such as derivatives, gradients, and optimization, as they apply to machine learning. This will provide you with a strong theoretical foundation that will support your understanding of optimization algorithms.
View
Melania
on Amazon
Show steps
-
Review the basic principles of calculus.
-
Understand the concept of a derivative and how it is used to find the slope of a function.
-
Apply derivatives to optimize functions commonly used in machine learning.
Practice Gradient Descent Equations
Show steps
Practice applying the equations of gradient descent to optimize functions commonly used in machine learning. This will solidify your understanding of the mathematical underpinnings of gradient descent and improve your ability to implement it effectively.
Browse courses on
Gradient Descent
Show steps
-
Derive the gradient descent update rule for a linear function.
-
Implement gradient descent in Python to minimize a quadratic function.
-
Apply gradient descent to optimize the weights of a neural network.
Show all two activities
Read 'Calculus for Machine Learning'
Show steps
Review the fundamental concepts of calculus, such as derivatives, gradients, and optimization, as they apply to machine learning. This will provide you with a strong theoretical foundation that will support your understanding of optimization algorithms.
View
Melania
on Amazon
Show steps
Show steps
Show steps
- Review the basic principles of calculus.
- Understand the concept of a derivative and how it is used to find the slope of a function.
- Apply derivatives to optimize functions commonly used in machine learning.
Practice Gradient Descent Equations
Show steps
Practice applying the equations of gradient descent to optimize functions commonly used in machine learning. This will solidify your understanding of the mathematical underpinnings of gradient descent and improve your ability to implement it effectively.
Browse courses on
Gradient Descent
Show steps
Show steps
Show steps
- Derive the gradient descent update rule for a linear function.
- Implement gradient descent in Python to minimize a quadratic function.
- Apply gradient descent to optimize the weights of a neural network.
Show all two activities
Career center
Learners who complete Calculus for Machine Learning and Data Science will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop and maintain the machine learning models and algorithms used in AI-powered products. They need a strong foundation in calculus, and this course will provide learners with the key calculus concepts they need to understand and apply machine learning fundamentals. Additionally, the course's modules can help MLEs better understand how to optimize the algorithms and models they work on.
Data Scientist
Data Scientist
Data Scientists use scientific methods and advanced data analysis to extract insights and knowledge from data. They must have strong calculus skills to make sense of large complex datasets and this course can help build those skills. Likewise, this course can help data scientists who want to specialize in machine learning.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical modeling, along with coding, to address business problems. They take on projects and positions that require highly specialized mathematical skills. Given that this role requires high levels of comfort and skill with mathematical concepts like calculus, this course could be highly valuable. The focus on optimization and use of gradient descent should be especially relevant to those interested in quantitative analysis.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts devise solutions and strategies to help businesses make better decisions. They use advanced analytical methods to understand how systems work and then develop ways to improve them. The heavy focus on optimization in this course will help learners build the math skills needed to excel in operations research and gain critical insights for their organization.
Data Analyst
Data Analyst
Data Analysts closely observe data patterns to uncover trends and other useful information. They can work in any industry from finance to marketing. They need to be able to properly analyze data and communicate its meaning to others. Calculus is a foundational skill for this role, and this course provides learners with the critical calculus concepts they need to understand the fundamentals of data analysis. Given the focus on machine learning and data science, this course will help those who want to specialize in data analysis that utilizes machine learning.
Financial Analyst
Financial Analyst
Financial Analysts help individuals and organizations make better financial choices and decisions. Success in this role requires the ability to properly analyze and understand the relationships between financial indicators. Calculus is a necessary skill to have for any financial analyst. It can help build a foundation of understanding the interconnectedness of variables. The focus on machine learning and data science can help those who want to analyze the data driving financial models.
Software Engineer
Software Engineer
Software Engineers design, build, and maintain software systems. Calculus is a critical part of computer science and software engineering and this course can help build a foundation for working with highly computational software like AI and machine learning programs.
Market Researcher
Market Researcher
Market Researchers analyze and interpret market data to understand consumer behavior and trends. Calculus can help market researchers build models to predict consumer behavior. This course may be useful for those that want to have a more thorough understanding of the mathematics underlying these models.
Financial Engineer
Financial Engineer
Financial Engineers design and develop financial products and services. Calculus is a very important part of finance. This course may help build a foundation for this kind of analysis.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. Calculus is an important part of understanding statistical patterns. This course may be useful for those that want to have a more thorough understanding of the mathematics underlying the statistical methods they use.
Operations Manager
Operations Manager
Operations Managers plan and oversee the operations of businesses and organizations. Calculus can help operations managers build models to optimize business processes. This course may be useful for those that want to have a more thorough understanding of the mathematics underlying these models.
Actuary
Actuary
Actuaries use mathematical and statistical methods to assess risk and uncertainty. Calculus can help build a foundation for the kind of mathematical and statistical modeling and analysis that actuaries do.
Risk Manager
Risk Manager
Risk Managers identify, assess, and manage risks. Calculus can help build a foundation for the kind of mathematical and statistical modeling and analysis that risk managers do.
Economist
Economist
Economists research, collect, and analyze data on the production, distribution, and consumption of goods and services. Calculus can help economists build models that can predict economic trends. This course may help build a foundation for this kind of analysis.
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to biological and medical data. Calculus can help build a foundation for the kind of mathematical and statistical modeling and analysis that biostatisticians do.
For more career information including salaries, visit:
OpenCourser.com/course/25mnt3/calculus
Reading list
We've selected 13 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
Calculus for Machine Learning and Data Science.
Save
Comprehensive reference on deep learning. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It good resource for students who want to learn more about the theoretical aspects of deep learning.
Provides a mathematical foundation for machine learning. It covers topics such as linear algebra, calculus, and probability. It good resource for students who want to learn more about the mathematical foundations of machine learning.
Provides a comprehensive overview of convex optimization. It covers topics such as linear programming, quadratic programming, and semidefinite programming. It good resource for students who want to learn more about optimization techniques used in machine learning.
Provides a comprehensive overview of information theory, inference, and learning algorithms. It covers topics such as entropy, mutual information, and Bayesian inference. It good resource for students who want to learn more about the theoretical foundations of machine learning.
Provides a comprehensive overview of pattern recognition and machine learning. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. It good resource for students who want to learn more about the practical aspects of machine learning.
Provides a probabilistic perspective on machine learning. It covers topics such as Bayesian inference, graphical models, and reinforcement learning. It good resource for students who want to learn more about the theoretical foundations of machine learning.
Provides a comprehensive overview of statistical learning. It covers topics such as linear regression, logistic regression, and tree-based methods. It good resource for students who want to learn more about the practical aspects of machine learning.
Provides a gentle introduction to machine learning. It covers topics such as supervised learning, unsupervised learning, and deep learning. It good resource for students who are new to machine learning.
Provides a practical introduction to deep learning using Python. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It good resource for students who want to learn more about the practical aspects of deep learning.
Provides a hands-on introduction to machine learning using Scikit-Learn, Keras, and TensorFlow. It covers topics such as supervised learning, unsupervised learning, and deep learning. It good resource for students who want to learn more about the practical aspects of machine learning.
Provides a comprehensive overview of machine learning algorithms. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. It good resource for students who want to learn more about the theoretical foundations of machine learning.
Save
Provides an algorithmic perspective on machine learning. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. It good resource for students who want to learn more about the theoretical foundations of machine learning.
Save
Provides an overview of machine learning from a practical perspective. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. It good resource for students who want to learn more about the practical aspects of machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/25mnt3/calculus
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Effort
At the rate of 5 hours per week, it will take you around 3 weeks to complete.
Level
Introductory
Via
Coursera
Institution
DeepLearning.AI
Instructor
Luis Serrano
Language
English
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This course belongs to a
collection
called
Mathematics for Machine Learning and Data Science. It's comprised of three courses:
- Probability & Statistics for Machine Learning & Data Science
- Calculus for Machine Learning and Data Science (viewing)
- Linear Algebra for Machine Learning and Data Science
Good to know
Teaches differentiation, which is core to analyzing machine learning functions
Covers gradient descent, which helps learners optimize machine learning models
Explores Newton's method, which is useful for optimizing machine learning models
Proper for beginners with basic high-school math and some Python familiarity
Taught by Luis Serrano, an expert in mathematics for machine learning
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