Calculus for Machine Learning and Data Science from Coursera

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.

What's inside

Syllabus

Week 1 - Derivatives and Optimization

After completing this course, you will be able to:

Week 2 - Gradients and Gradient Descent

Week 3 - Optimization in Neural Networks and Newton's Method

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

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'

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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.

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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

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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

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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.

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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 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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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