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Play by Play

Machine Learning Exposed

In this course, you’ll explore machine learning topics, such as supervised learning, unsupervised learning, reinforcement learning, and deep learning.

Play by Play is a series in which top technologists work through a problem in real time, unrehearsed, and unscripted. In this course, Play by Play: Machine Learning Exposed, James Weaver and Katharine Beaumont will start with the basics, and build up in an approachable way to some of the most interesting techniques machine learning has to offer. Explore Linear Regression, Neural Networks, clustering, and survey various machine learning APIs and platforms. By the end of this course, you'll get an overview of what you can achieve, as well as an intuition on the maths behind machine learning.

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What's inside

Syllabus

Course Overview

Machine Learning Introduction

Supervised Learning

Reinforcement Learning

Course Conclusion

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Play by Play is a series in which top technologists work through a problem in real time, unrehearsed, and unscripted

Introduces a variety of AI and machine learning models and methods, with a focus on real-world examples and case studies

Combines theory with practical application, helping learners to develop a strong foundation in machine learning concepts

Taught by Katharine Beaumont and James Weaver, two experienced machine learning practitioners

Covers essential topics, including supervised and unsupervised learning, reinforcement learning, and deep learning

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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 Play by Play: Machine Learning Exposed with these activities:

Gather Machine Learning Resources

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Expand your knowledge by compiling a collection of valuable resources on machine learning.

Browse courses on Online Courses

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Search for and identify relevant books, online courses, and tutorials.
Organize the resources into categories or topics.
Create a reference list or database for future use.

Review Math for Machine Learning

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Prepare for success in this course by reviewing the math necessary for developing a solid understanding of machine learning concepts.

Browse courses on Probability

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Explore online tutorials on probability, statistics, and linear algebra.
Complete practice problems and exercises to reinforce your understanding.
Seek clarification from the course instructor or online forums if needed.

Review Linear Algebra

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Reinforce your understanding of the mathematical concepts underlying machine learning.

Browse courses on Linear Algebra

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Review basic vector and matrix operations.
Practice solving systems of linear equations.
Study the properties of eigenvalues and eigenvectors.

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Review linear algebra

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Brushing up on the concepts of linear algebra will be helpful before diving into the more advanced topics.

Browse courses on Linear Algebra

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Review matrix operations (addition, subtraction, multiplication, inverse)
Review vector operations (addition, subtraction, dot product, cross product)
Review eigenvalues and eigenvectors
Solve practice problems
Take a practice quiz

Review Linear Regression

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Start by reviewing the basics of linear regression to strengthen your understanding of the concepts and techniques used in this course.

Browse courses on Linear Regression

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Read the introduction to linear regression in your course materials.
Review articles or books on the topic.
Complete practice problems.
Create a simple linear regression model using a tool or library.

Participate in Study Groups

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Enhance your learning by collaborating with peers, discussing concepts, and sharing insights.

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Form or join study groups with fellow students.
Meet regularly to discuss course topics, work on assignments, and ask questions.
Share resources, notes, and ideas with the group.

Solve machine learning practice problems

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Deepen your understanding of machine learning concepts by solving practice problems.

Browse courses on Machine Learning

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Find practice problems online or in textbooks
Attempt to solve the problems on your own
Check your solutions against the provided solutions

Follow Tutorials on Supervised Learning

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Gain hands-on experience with supervised learning algorithms through guided tutorials.

Browse courses on Supervised Learning

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Find tutorials on supervised learning in Python or another programming language of your choice.
Follow the tutorials step-by-step, implementing and testing different supervised learning algorithms.
Experiment with different datasets to see how the algorithms perform in various scenarios.

Solve Coding Challenges

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Improve your coding skills and apply machine learning algorithms by solving coding challenges.

Browse courses on Python

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Find online coding challenge platforms or websites.
Select challenges that align with the course topics.
Implement solutions using Python and machine learning algorithms.
Review your solutions and identify areas for improvement.

Follow TensorFlow Tutorials

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Gain hands-on experience with a popular deep learning library.

Browse courses on TensorFlow

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Complete the TensorFlow 'Hello World' tutorial.
Build a simple neural network using TensorFlow.
Explore the TensorFlow documentation and API reference.

Join a Study Group

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Engage with fellow learners to broaden your knowledge, exchange ideas, and enhance your overall understanding of the course material.

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Identify and connect with other students in the course.
Establish regular meeting times and a communication platform.
Assign specific topics or sections for each session.
Discuss course concepts, share insights, and work through problems together.

Explore Machine Learning Tutorials

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Expand your knowledge and refine your skills by following guided tutorials and online courses that delve deeper into specific machine learning topics.

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Identify reputable platforms or instructors offering machine learning tutorials.
Select tutorials that align with your interests or areas where you need improvement.
Follow the tutorials, complete exercises, and implement the concepts in your own projects.
Join online forums or discussion groups to connect with others and ask questions.

Practice using scikit-learn

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Gain hands-on experience with scikit-learn, a popular machine learning library.

Browse courses on scikit-learn

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Complete scikit-learn tutorials
Follow along with the code examples
Experiment with different scikit-learn functions
Build a simple machine learning model

Practice Drills on Neural Networks

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Reinforce your understanding of neural networks through guided practice drills.

Browse courses on Neural Networks

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Find practice drills or exercises on neural networks in Python or another programming language of your choice.
Complete the drills, which may involve implementing and training neural networks on various datasets.
Review the solutions and identify areas where you need to improve your understanding.

Attend Machine Learning Workshops

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Gain practical experience and learn from experts by attending workshops focused on machine learning.

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Identify and register for workshops related to the course topics.
Actively participate in the workshops, ask questions, and take notes.
Apply the knowledge gained to your coursework and projects.

Solve ML Practice Problems

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Reinforce your understanding of machine learning algorithms and techniques by solving a variety of practice problems.

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Find online platforms or resources that offer machine learning practice problems.
Select problems that cover different concepts and levels of difficulty.
Work through the problems, documenting your approach and solutions.
Review your solutions and identify areas for improvement.

Solve Machine Learning Coding Challenges

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Test your understanding of machine learning concepts by solving coding problems.

Browse courses on Machine Learning Algorithms

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Practice implementing linear regression from scratch.
Build a decision tree classifier using Python.
Train a convolutional neural network for image recognition.

Build a Machine Learning Model

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Demonstrate your understanding of machine learning techniques by building a model and applying it to a real-world problem.

Browse courses on Supervised Learning

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Identify a problem that can be addressed using machine learning.
Collect and prepare the necessary data.
Select and train a suitable machine learning model.
Evaluate the model's performance and make adjustments.
Deploy the model and monitor its results.

Develop a Machine Learning Portfolio

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Showcase your skills and understanding by creating a comprehensive portfolio that demonstrates your proficiency in various machine learning techniques.

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Select projects that highlight different aspects of machine learning.
Document your projects, including problem statements, data analysis, model development, and results.
Create a platform or website to host your portfolio.
Seek feedback on your portfolio from mentors or experienced professionals.

Build a machine learning project

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Apply your machine learning skills by building a project that addresses a real-world problem.

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Identify a problem you'd like to solve with machine learning
Gather and prepare your data
Choose and train a machine learning model
Evaluate your model's performance
Deploy your model

Build a Machine Learning Project

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Apply your machine learning knowledge to a real-world problem by building a project.

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Define the problem you want to solve with machine learning.
Gather and prepare the necessary data.
Choose and implement appropriate machine learning algorithms.
Evaluate the performance of your model and iterate to improve results.
Document your project and share your findings.

Develop a Machine Learning Project

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Showcase your skills and apply your knowledge by building a real-world machine learning project.

Browse courses on Machine Learning Projects

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Identify a problem or challenge that can be solved using machine learning.
Collect and prepare data for your project.
Train and evaluate different machine learning models.
Deploy your project and monitor its performance.

Write a blog post about a machine learning topic

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Writing a blog post will help you reinforce your understanding of machine learning concepts and share your knowledge with others.

Browse courses on Content Creation

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Choose a topic that you're interested in and that you have some knowledge of
Research your topic
Write a draft of your blog post
Edit and revise your blog post
Publish your blog post
Promote your blog post on social media

Career center

Learners who complete Play by Play: Machine Learning Exposed will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers build, deploy, and maintain machine learning systems. This course provides a comprehensive overview of machine learning techniques, including supervised learning, unsupervised learning, and reinforcement learning. The course also covers the use of various machine learning APIs and platforms, which would be essential knowledge for Machine Learning Engineers.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use data to solve business problems. This course can help build a foundation in the machine learning techniques that are essential for Data Scientists, such as clustering, regression, and neural networks. The course also covers the use of various machine learning APIs and platforms, which would be beneficial for Data Scientists who need to apply machine learning to real-world problems.

See salaries and explore the career path for Data Scientist

Quantitative Analyst

Quantitative Analysts utilize advanced mathematical and statistical modeling tools to analyze and solve complex financial problems. This course can help build a strong foundation in the mathematical concepts that underpin quantitative analysis, including regression analysis and machine learning techniques. Specifically, the course's exploration of deep learning may be particularly relevant to Quantitative Analysts who wish to apply AI to financial modeling.

See salaries and explore the career path for Quantitative Analyst

Machine Learning Scientist

Machine Learning Scientists research and develop new machine learning algorithms and techniques. This course may be helpful for Machine Learning Scientists who wish to develop their skills in machine learning and data analysis. The course provides a comprehensive overview of machine learning techniques and their applications. Specifically, the course's coverage of unsupervised learning and deep learning may be relevant for Machine Learning Scientists who need to develop and evaluate new machine learning algorithms.

See salaries and explore the career path for Machine Learning Scientist

Artificial Intelligence Engineer

Artificial Intelligence Engineers design and build AI systems. This course may be helpful for Artificial Intelligence Engineers who wish to develop their skills in machine learning and data analysis. The course provides a comprehensive overview of machine learning techniques and their applications. Specifically, the course's coverage of deep learning is relevant for Artificial Intelligence Engineers who need to develop and evaluate AI systems.

See salaries and explore the career path for Artificial Intelligence Engineer

Statistician

Statisticians collect, analyze, and interpret data. This course may be helpful for Statisticians who wish to develop their skills in machine learning and data analysis. The course provides a comprehensive overview of machine learning techniques and their applications. Specifically, the course's coverage of unsupervised learning and deep learning may be relevant for Statisticians who need to develop and evaluate new statistical methods.

See salaries and explore the career path for Statistician

Researcher

Researchers conduct original research in a variety of fields. This course may be helpful for Researchers who wish to develop their skills in machine learning and data analysis. The course provides a comprehensive overview of machine learning techniques and their applications. Specifically, the course's coverage of unsupervised learning and deep learning may be relevant for Researchers who need to develop and evaluate new machine learning algorithms.

See salaries and explore the career path for Researcher

Data Engineer

Data Engineers design and build data pipelines and systems. This course may be helpful for Data Engineers who wish to develop their skills in machine learning and data analysis. The course provides a comprehensive overview of machine learning techniques and their applications. Specifically, the course's coverage of supervised learning and reinforcement learning may be relevant for Data Engineers who need to develop and evaluate data pipelines and systems.

See salaries and explore the career path for Data Engineer

Data Analyst

Data Analysts collect, analyze, and interpret data to provide insights and make predictions. This course may be helpful for Data Analysts who wish to develop their skills in machine learning techniques. The course provides a comprehensive overview of machine learning techniques and their applications. Specifically, the course's coverage of clustering and supervised learning may be particularly relevant for Data Analysts who need to analyze and interpret large datasets.

See salaries and explore the career path for Data Analyst

Financial Analyst

Financial Analysts use data to make investment recommendations. This course may be helpful for Financial Analysts who wish to develop their skills in machine learning and data analysis. The course provides a comprehensive overview of machine learning techniques and their applications. Specifically, the course's coverage of supervised learning and reinforcement learning may be relevant for Financial Analysts who need to develop and evaluate investment models.

See salaries and explore the career path for Financial Analyst

Actuary

Actuaries use mathematical and statistical models to assess risk. This course may be helpful for Actuaries who wish to develop their skills in machine learning and data analysis. The course provides a comprehensive overview of machine learning techniques and their applications. Specifically, the course's coverage of supervised learning and reinforcement learning may be relevant for Actuaries who need to develop and evaluate risk models.

See salaries and explore the career path for Actuary

Software Engineer

Software Engineers design, develop, and maintain software systems. While not all Software Engineers specialize in machine learning, this course may be helpful for those who wish to develop software systems that incorporate machine learning capabilities. The course provides a comprehensive overview of machine learning techniques and their applications. Specifically, the course's coverage of deep learning is relevant for Software Engineers who wish to develop AI-powered software applications.

See salaries and explore the career path for Software Engineer

Business Analyst

Business Analysts use data to solve business problems. While not all Business Analysts specialize in machine learning, this course may be helpful for those who wish to develop their skills in data analysis and modeling. The course provides a comprehensive overview of machine learning techniques and their applications. Specifically, the course's coverage of supervised learning and reinforcement learning may be relevant for Business Analysts who need to develop and evaluate data-driven solutions.

See salaries and explore the career path for Business Analyst

Consultant

Consultants provide advice and expertise to organizations on a variety of topics. While not all Consultants specialize in machine learning, this course may be helpful for those who wish to develop their skills in data analysis and modeling. The course provides a comprehensive overview of machine learning techniques and their applications. Specifically, the course's coverage of supervised learning and reinforcement learning may be relevant for Consultants who need to develop and evaluate data-driven solutions.

See salaries and explore the career path for Consultant

Product Manager

Product Managers are responsible for the development and launch of new products. While not all Product Managers specialize in machine learning, this course may be helpful for those who wish to develop their understanding of machine learning and its potential applications in product development. The course provides a comprehensive overview of machine learning techniques and their applications. Specifically, the course's coverage of deep learning may be relevant for Product Managers who wish to develop AI-powered products.

See salaries and explore the career path for Product Manager

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 Play by Play: Machine Learning Exposed.

Deep Learning

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Comprehensive guide to deep learning, covering the latest advances in the field. It is an essential read for anyone who wants to learn about the state-of-the-art in deep learning.

Play by Play

Machine Learning Exposed

What's inside

Syllabus

Good to know

Save this course

Activities

Career center

Reading list

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