Machine Learning Foundations for Product Managers from Coursera

In this first course of the AI Product Management Specialization offered by Duke University's Pratt School of Engineering, you will build a foundational understanding of what machine learning is, how it works and when and why it is applied. To successfully manage an AI team or product and work collaboratively with data scientists, software engineers, and customers you need to understand the basics of machine learning technology. This course provides a non-coding introduction to machine learning, with focus on the process of developing models, ML model evaluation and interpretation, and the intuition behind common ML and deep learning algorithms. The course will conclude with a hands-on project in which you will have a chance to train and optimize a machine learning model on a simple real-world problem.

At the conclusion of this course, you should be able to:

1) Explain how machine learning works and the types of machine learning

2) Describe the challenges of modeling and strategies to overcome them

3) Identify the primary algorithms used for common ML tasks and their use cases

4) Explain deep learning and its strengths and challenges relative to other forms of machine learning

5) Implement best practices in evaluating and interpreting ML models

What's inside

Syllabus

What is Machine Learning

In this module we will be introduced to what machine learning is and does. We will build the necessary vocabulary for working with data and models and develop an understanding of the different types of machine learning. We will conclude with a critical discussion of what machine learning can do well and cannot (or should not) do.

The Modeling Process

In this module we will discuss the key steps in the process of building machine learning models. We will learn about the sources of model complexity and how complexity impacts a model's performance. We will wrap up with a discussion of strategies for comparing different models to select the optimal model for production.

Evaluating & Interpreting Models

In this module we will learn how to define appropriate outcome and output metrics for AI projects. We will then discuss key metrics for evaluating regression and classification models and how to select one for use. We will wrap up with a discussion of common sources of error in machine learning projects and how to troubleshoot poor performance.

Linear Models

In this module we will explore the use of linear models for regression and classification. We will begin with introducing linear regression and continue with a discussion on how to make linear regression work better through regularization. We will then switch to classification and introduce the logistic regression model for both binary and multi-class classification problems.

Trees, Ensemble Models and Clustering

We will begin this model with a discussion of tree models and their value in modeling compex non-linear problems. We will then introduce the method of creating ensemble models and their benefits. We will wrap this module up by switching gears to unsupervised learning and discussing clustering and the popular K-Means clustering approach.

Deep Learning & Course Project

Our final module in this course will focus on a hot area of machine learning called deep learning, or the use of multi-layer neural networks. We will develop an understanding of the intuition and key mathematical principles behind how neural networks work. We will then discuss common applications of deep learning in computer vision and natural language processing. We will wrap up the course with our course project, where you will have an opportunity to apply the modeling process and best practices you have learned to create your own machine learning model.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Taught by Jon Reifschneider, who are recognized for their work in machine learning

Examines machine learning, which is highly relevant to computer science and related fields

Explores machine learning, which is standard in industry

Builds a strong foundation for beginners

Introduces deep learning, which may add color to other topics and subjects

Explicitly requires learners to come in with extensive background knowledge first

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 Foundations for Product Managers with these activities:

Follow a machine learning tutorial

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Following a machine learning tutorial will help you to learn the basics of machine learning and to get started with building your own models.

Browse courses on Machine Learning Basics

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Find a machine learning tutorial that is appropriate for your level of experience.
Follow the steps in the tutorial and complete the exercises.
Ask questions in the tutorial forum if you get stuck.

Join a machine learning study group

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Joining a machine learning study group will give you the opportunity to learn from and collaborate with other students, and will help you to stay motivated.

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Find a machine learning study group that meets your needs.
Attend the study group meetings regularly.
Participate in the discussions and activities.

Solve machine learning practice problems

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Solving machine learning practice problems will help you to develop your problem-solving skills and to improve your understanding of machine learning concepts.

Browse courses on Machine Learning Algorithms

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Find a set of machine learning practice problems.
Solve the problems using the techniques you have learned in class.
Check your answers and identify any areas where you need to improve your understanding.

Four other activities

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Attend a machine learning workshop

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Attending a machine learning workshop will give you the opportunity to learn from experts in the field and to network with other machine learning enthusiasts.

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Find a machine learning workshop that is relevant to your interests.
Register for the workshop.
Attend the workshop and participate in the activities.

Build a simple machine learning model

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Building a simple machine learning model will give you hands-on experience with the modeling process and help you understand the different steps involved.

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Train the model on the dataset.
Evaluate the model's performance.
Choose a simple dataset to work with.
Select a machine learning algorithm that is appropriate for the task.
Use the model to make predictions.

Contribute to an open-source machine learning project

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Contributing to an open-source machine learning project will give you the opportunity to gain real-world experience and to learn from other developers.

Browse courses on Machine Learning Development

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Find an open-source machine learning project that you are interested in.
Fork the project and make your own changes.
Submit a pull request with your changes.

Create a presentation on a machine learning topic

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Creating a presentation on a machine learning topic will help you to develop your understanding of the topic and to communicate your knowledge to others.

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Choose a machine learning topic to present on.
Research the topic and gather information.
Organize your information into a logical flow.
Create visual aids to support your presentation.
Practice your presentation.

Career center

Learners who complete Machine Learning Foundations for Product Managers will develop knowledge and skills that may be useful to these careers:

AI Engineer

AI engineers design, develop, and deploy AI systems. They typically have a strong foundation in computer science, machine learning, and artificial intelligence. This course may be useful in helping you build a foundation in machine learning, which is a key skill for AI engineers.

See salaries and explore the career path for AI Engineer

Machine Learning Engineer

Machine learning engineers design, develop, and deploy machine learning models. They typically have a strong foundation in computer science, machine learning, and software engineering. This course may be useful in helping you build a foundation in machine learning, which is a key skill for machine learning engineers.

See salaries and explore the career path for Machine Learning Engineer

Statistician

Statisticians collect, analyze, and interpret data. They typically have a strong foundation in mathematics and statistics. This course may be useful in helping you build a foundation in machine learning, which is a subfield of statistics.

See salaries and explore the career path for Statistician

Data Architect

Data architects design and manage data systems. They typically have a strong foundation in computer science, data management, and software engineering. This course may be useful in helping you build a foundation in machine learning, which is an increasingly important skill for data architects, especially those working on projects that involve data.

See salaries and explore the career path for Data Architect

Software Engineer

Software engineers design, develop, and maintain software systems. They typically have a strong foundation in computer science and software engineering. This course may be useful in helping you build a foundation in machine learning, which is an increasingly important skill for software engineers, especially those working on projects that involve AI.

See salaries and explore the career path for Software Engineer

Data Analyst

Data analysts analyze data and identify trends and patterns. They typically have a strong foundation in mathematics and statistics, programming, and data analysis. This course may be useful in helping you build a foundation in machine learning, which is an increasingly important skill for data analysts.

See salaries and explore the career path for Data Analyst

DBA

DBAs design, manage, and maintain database systems. They typically have a strong foundation in computer science, database management, and software engineering. This course may be useful in helping you build a foundation in machine learning, which is an increasingly important skill for DBAs, especially those working on projects that involve data.

See salaries and explore the career path for DBA

Operations Research Analyst

Operations research analysts use mathematical and analytical methods to solve problems in business and industry. They typically have a strong foundation in mathematics, operations research, and problem-solving. This course may be useful in helping you build a foundation in machine learning, which is an increasingly important skill for operations research analysts.

See salaries and explore the career path for Operations Research Analyst

Computer Scientist

Computer scientists研究the theoretical foundations of computing. They typically have a strong foundation in mathematics and computer science. This course may be useful in helping you build a foundation in machine learning, which is a subfield of computer science.

See salaries and explore the career path for Computer Scientist

Quant

Quants use mathematical and statistical methods to solve problems in finance. They typically have a strong foundation in mathematics, finance, and computer science. This course may be useful in helping you build a foundation in machine learning, which is an increasingly important skill for quants, especially those working on projects that involve data.

See salaries and explore the career path for Quant

Data Scientist

Data scientists analyze data and build models to make predictions and solve problems. They typically have a strong foundation in mathematics and statistics, programming and machine learning, and business intelligence. This course may be useful in helping you build a foundation in machine learning, which is a key skill for data scientists.

See salaries and explore the career path for Data Scientist

Product Manager

Product managers are responsible for the development and management of products. They typically have a strong foundation in business, product management, and marketing. This course may be useful in helping you build a foundation in machine learning, which is an increasingly important skill for product managers, especially those working on products that incorporate AI.

See salaries and explore the career path for Product Manager

Financial Analyst

Financial analysts analyze financial data and make recommendations on investments. They typically have a strong foundation in finance, economics, and mathematics. This course may be useful in helping you build a foundation in machine learning, which is an increasingly important skill for financial analysts, especially those working on projects that involve data.

See salaries and explore the career path for Financial Analyst

Market Research Analyst

Market research analysts collect, analyze, and interpret data on consumer behavior. They typically have a strong foundation in marketing, statistics, and consumer behavior. This course may be useful in helping you build a foundation in machine learning, which is an increasingly important skill for market research analysts, especially those working on projects that involve data.

See salaries and explore the career path for Market Research Analyst

Business Analyst

Business analysts analyze business processes and identify opportunities for improvement. They typically have a strong foundation in business, data analysis, and problem-solving. This course may be useful in helping you build a foundation in machine learning, which is an increasingly important skill for business analysts, especially those working on projects that involve data.

See salaries and explore the career path for Business Analyst