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Simon Allardice
A practical, pragmatic, jargon-free introduction to Machine Learning. Quickly cover the most important ideas and concepts — and learn approaches and techniques to apply Machine Learning in your own career.
Read more
A practical, pragmatic, jargon-free introduction to Machine Learning. Quickly cover the most important ideas and concepts — and learn approaches and techniques to apply Machine Learning in your own career.
Read more
A practical, pragmatic, jargon-free introduction to Machine Learning. Quickly cover the most important ideas and concepts — and learn approaches and techniques to apply Machine Learning in your own career.
Tech leaders need a fundamental understanding of the tools and technologies their teams use to build solutions. This course, Machine Learning: Executive Briefing, takes a fast-paced, practical, and pragmatic approach to Machine Learning. First, we'll explore common cliches around Machine Learning and how they get in the way of learning. Next, you'll get clear on the most important jargon and terminology you need to know. We'll then cover the steps and sequence of developing a Machine Learning application, Finally, you will explore the most common practical applications of Machine Learning in real-world projects. When you’re finished with this course, you will have the skills and knowledge to help implement Machine Learning to support your product, team, or organization.
Register for this course and see more details by visiting:
OpenCourser.com/course/m0qbv7/machine
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What's inside
Syllabus
Introduction: Solving New Kinds of Problems
But What Is Machine Learning, Really?
Training a Machine Learning Model
The Marketplace of Machine Learning
Read more
Syllabus
Introduction: Solving New Kinds of Problems
But What Is Machine Learning, Really?
Training a Machine Learning Model
The Marketplace of Machine Learning
Read more
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides a high-level overview of Machine Learning
Taught by an established instructor in the field
Suitable for tech leaders who need a fundamental understanding of Machine Learning
Curriculum covers a range of topics from introductory concepts to practical applications
Delivers a practical and pragmatic approach to Machine Learning
Course may not provide sufficient depth for those seeking advanced knowledge
Save this course
Save Machine Learning: Executive Briefing 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 Machine Learning: Executive Briefing with these
activities:
Review Course Materials
Show steps
Reinforce your understanding of key concepts and prepare for upcoming course content.
Browse courses on
Machine Learning
Show steps
-
Go over lecture notes, slides, and any assigned readings prior to each class session
-
Complete any pre-class quizzes or assignments to assess your readiness
Review Machine Learning
Show steps
Build foundational understanding of key terms, concepts, and methods used in Machine Learning.
Browse courses on
Machine Learning
Show steps
-
Review linear algebra and calculus basics
-
Study different types of Machine Learning algorithms, like supervised and unsupervised learning
-
Practice implementing simple Machine Learning models using a programming language like Python
Read 'Machine Learning Yearning'
Show steps
Gain a comprehensive understanding of Machine Learning concepts, algorithms, and applications.
Show steps
-
Read through the book and take notes on key concepts
-
Work through the practice exercises provided in the book to reinforce your understanding
-
Discuss the book's content with peers or participate in online forums to enhance your learning
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Participate in peer learning groups
Show steps
Engaging with peers in collaborative learning will enhance your understanding and retention of the material, as well as provide diverse perspectives.
Show steps
-
Join or create a peer learning group with other course participants
-
Discuss course topics, share knowledge, and collaborate on assignments
Follow Machine Learning Tutorials
Show steps
Gain practical insights and hands-on experience with Machine Learning techniques.
Browse courses on
Machine Learning
Show steps
-
Identify reputable online courses or tutorials on Machine Learning
-
Follow along with video tutorials and complete accompanying exercises
-
Seek clarification or ask questions in online forums or discussion groups
Follow online tutorials on specific Machine Learning algorithms
Show steps
Hands-on implementation of Machine Learning algorithms through guided tutorials will deepen your understanding and practical skills.
Browse courses on
Machine Learning Algorithms
Show steps
-
Identify an algorithm you wish to learn more about
-
Search for online tutorials covering that algorithm
-
Follow the tutorial step-by-step and implement the algorithm yourself
Solve Machine Learning Problems
Show steps
Strengthen problem-solving skills and improve understanding of Machine Learning algorithms.
Browse courses on
Machine Learning
Show steps
-
Work through practice problems and exercises related to Machine Learning concepts
-
Participate in online coding challenges or hackathons focused on Machine Learning
Join a Machine Learning Study Group
Show steps
Engage in collaborative learning and knowledge sharing through peer discussions.
Browse courses on
Machine Learning
Show steps
-
Find or create a study group with peers who share your interests in Machine Learning
-
Meet regularly to discuss course material, work on projects together, and exchange ideas
-
Take turns presenting concepts and leading discussions to enhance your understanding
Develop a simple Machine Learning application
Show steps
Building a practical Machine Learning application will provide you with valuable hands-on experience and a deeper understanding of the entire development process.
Show steps
-
Define a specific problem you want to solve using Machine Learning
-
Choose appropriate data and algorithms for your application
-
Implement and train your Machine Learning model
-
Deploy and test your application
Develop a Machine Learning Project
Show steps
Apply Machine Learning knowledge and skills to solve real-world problems or build innovative solutions.
Browse courses on
Machine Learning
Show steps
-
Identify a suitable project idea that aligns with your interests and learning goals
-
Gather and prepare the necessary data for your project
-
Develop and train a Machine Learning model using appropriate algorithms and techniques
-
Evaluate the performance of your model and make necessary adjustments to improve accuracy
-
Present your project and findings to others to receive feedback and share your learnings
Contribute to Open Source Machine Learning Projects
Show steps
Gain hands-on experience and contribute to the broader Machine Learning community.
Browse courses on
Machine Learning
Show steps
-
Identify open source Machine Learning projects that align with your interests and skills
-
Review the project's documentation and codebase to understand its purpose and functionality
-
Make contributions to the project by fixing bugs, adding new features, or improving documentation
-
Collaborate with other contributors and participate in discussions to enhance your understanding
Review Course Materials
Show steps
Reinforce your understanding of key concepts and prepare for upcoming course content.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Go over lecture notes, slides, and any assigned readings prior to each class session
- Complete any pre-class quizzes or assignments to assess your readiness
Review Machine Learning
Show steps
Build foundational understanding of key terms, concepts, and methods used in Machine Learning.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Review linear algebra and calculus basics
- Study different types of Machine Learning algorithms, like supervised and unsupervised learning
- Practice implementing simple Machine Learning models using a programming language like Python
Read 'Machine Learning Yearning'
Show steps
Gain a comprehensive understanding of Machine Learning concepts, algorithms, and applications.
Show steps
Show steps
Show steps
- Read through the book and take notes on key concepts
- Work through the practice exercises provided in the book to reinforce your understanding
- Discuss the book's content with peers or participate in online forums to enhance your learning
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Participate in peer learning groups
Show steps
Engaging with peers in collaborative learning will enhance your understanding and retention of the material, as well as provide diverse perspectives.
Show steps
Show steps
Show steps
- Join or create a peer learning group with other course participants
- Discuss course topics, share knowledge, and collaborate on assignments
Follow Machine Learning Tutorials
Show steps
Gain practical insights and hands-on experience with Machine Learning techniques.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify reputable online courses or tutorials on Machine Learning
- Follow along with video tutorials and complete accompanying exercises
- Seek clarification or ask questions in online forums or discussion groups
Follow online tutorials on specific Machine Learning algorithms
Show steps
Hands-on implementation of Machine Learning algorithms through guided tutorials will deepen your understanding and practical skills.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Identify an algorithm you wish to learn more about
- Search for online tutorials covering that algorithm
- Follow the tutorial step-by-step and implement the algorithm yourself
Solve Machine Learning Problems
Show steps
Strengthen problem-solving skills and improve understanding of Machine Learning algorithms.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Work through practice problems and exercises related to Machine Learning concepts
- Participate in online coding challenges or hackathons focused on Machine Learning
Join a Machine Learning Study Group
Show steps
Engage in collaborative learning and knowledge sharing through peer discussions.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find or create a study group with peers who share your interests in Machine Learning
- Meet regularly to discuss course material, work on projects together, and exchange ideas
- Take turns presenting concepts and leading discussions to enhance your understanding
Develop a simple Machine Learning application
Show steps
Building a practical Machine Learning application will provide you with valuable hands-on experience and a deeper understanding of the entire development process.
Show steps
Show steps
Show steps
- Define a specific problem you want to solve using Machine Learning
- Choose appropriate data and algorithms for your application
- Implement and train your Machine Learning model
- Deploy and test your application
Develop a Machine Learning Project
Show steps
Apply Machine Learning knowledge and skills to solve real-world problems or build innovative solutions.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify a suitable project idea that aligns with your interests and learning goals
- Gather and prepare the necessary data for your project
- Develop and train a Machine Learning model using appropriate algorithms and techniques
- Evaluate the performance of your model and make necessary adjustments to improve accuracy
- Present your project and findings to others to receive feedback and share your learnings
Contribute to Open Source Machine Learning Projects
Show steps
Gain hands-on experience and contribute to the broader Machine Learning community.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify open source Machine Learning projects that align with your interests and skills
- Review the project's documentation and codebase to understand its purpose and functionality
- Make contributions to the project by fixing bugs, adding new features, or improving documentation
- Collaborate with other contributors and participate in discussions to enhance your understanding
Career center
Learners who complete Machine Learning: Executive Briefing will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build, deploy, and maintain machine learning models. They work with data scientists to identify business problems that can be solved using machine learning, and then they develop solutions that can be implemented in a production environment.
Data Scientist
Data Scientist
A Data Scientist is a professional who uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured. The field of Data Science draws on techniques and theories from many fields within the broader context of mathematics, statistics, computer science, and information science.
Software Engineer
Software Engineer
Software Engineers develop, maintain, and improve software systems. They work with a variety of programming languages and technologies to create software that meets the needs of their users.
Product Manager
Product Manager
A Product Manager is responsible for the development and execution of a product. They work with engineers, designers, and other stakeholders to define the product vision, roadmap, and features.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. They use this information to help businesses make better decisions.
Business Analyst
Business Analyst
A Business Analyst is responsible for understanding the business needs of an organization and translating them into technical requirements.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex problems in a variety of industries.
Financial Analyst
Financial Analyst
A Financial Analyst provides financial advice to individuals and organizations.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst is a financial professional who uses mathematical and statistical techniques to analyze financial data.
Market Researcher
Market Researcher
Market Researchers conduct research to understand the needs and wants of consumers.
Statistician
Statistician
A Statistician is a professional who uses statistical methods to collect, analyze, interpret, and present data.
Teacher
Teacher
A Teacher is responsible for educating students at all levels, from preschool through college.
Writer
Writer
A Writer is responsible for creating written content, such as articles, blog posts, and books.
Consultant
Consultant
A Consultant provides advice and guidance to organizations on a variety of topics.
Salesperson
Salesperson
A Salesperson is responsible for selling products and services to customers.
For more career information including salaries, visit:
OpenCourser.com/course/m0qbv7/machine
Reading list
We've selected ten 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
Machine Learning: Executive Briefing.
Save
Comprehensive guide to deep learning, covering the latest advancements in the field. It is essential reading for anyone interested in learning about the theory and practice of deep learning.
Provides a comprehensive overview of machine learning from a probabilistic perspective, covering foundational concepts such as Bayesian inference, graphical models, and reinforcement learning. It valuable resource for anyone seeking a deeper understanding of the mathematical underpinnings of machine learning.
Classic textbook on statistical learning, covering a wide range of topics from linear regression to support vector machines. It valuable resource for anyone interested in learning about the statistical foundations of machine learning.
Save
Provides a practical introduction to data mining, covering a wide range of topics from data preparation to machine learning algorithms. It valuable resource for anyone interested in learning about the theory and practice of data mining.
Save
Provides a rigorous treatment of machine learning, with a focus on the algorithms and techniques used in real-world applications. It valuable resource for anyone interested in the theoretical foundations of machine learning.
Provides a rigorous treatment of pattern recognition and machine learning, with a focus on statistical methods. It valuable resource for anyone interested in the theoretical foundations of machine learning.
Provides a practical introduction to machine learning, with a focus on the algorithms and techniques used in real-world applications. It great resource for beginners who want to gain a solid understanding of the field.
Save
Provides a practical introduction to machine learning, with a focus on the algorithms and techniques used in real-world applications. It great resource for beginners who want to gain a solid understanding of the field.
Provides a concise introduction to machine learning, covering the key concepts and techniques used in real-world applications. It great resource for beginners who want to gain a solid understanding of the field.
Provides a practical introduction to machine learning using Python, covering a wide range of topics from data preparation to machine learning algorithms. It great resource for beginners who want to gain a solid understanding of the field.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/m0qbv7/machine
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Time
39 hours
Level
Introductory
Via
Pluralsight
Instructor
Simon Allardice
Language
English
Good to know
Provides a high-level overview of Machine Learning
Taught by an established instructor in the field
Suitable for tech leaders who need a fundamental understanding of Machine Learning
Curriculum covers a range of topics from introductory concepts to practical applications
Delivers a practical and pragmatic approach to Machine Learning
Course may not provide sufficient depth for those seeking advanced knowledge
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