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In this machine learning course, you will learn about the machine learning lifecycle, and how to use AWS services at every stage. Additionally, you will discover the diverse sources for machine learning models and learn techniques to evaluate their performance. You will also understand the importance of machine learning operations (MLOps) in streamlining the development and deployment of your machine learning projects.

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What's inside

Syllabus

Developing Machine Learning Solutions

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Provides a solid foundation for learners looking to enter the field of machine learning
Offers a comprehensive overview of the machine learning lifecycle, empowering learners to navigate the entire process
Leverages AWS services, providing learners with hands-on experience using industry-standard tools
Emphasizes MLOps best practices, equipping learners to effectively deploy and manage machine learning projects
Teaches diverse model evaluation techniques, fostering a critical understanding of model performance

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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 Developing Machine Learning Solutions with these activities:
Review linear algebra and calculus
Having a strong foundation in linear algebra and calculus will make it easier to understand machine learning concepts.
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  • Review your notes from previous courses or textbooks
  • Work through practice problems to test your understanding
Attend the 'Machine Learning Meetup' in your city
Networking with other machine learning professionals can help you learn about new opportunities and trends.
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  • Find a machine learning meetup in your area
  • Attend the meetup and introduce yourself to other attendees
Form a study group with other students in the course
Studying with peers can help you understand the material better and stay motivated.
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  • Find other students in your course who are interested in forming a study group
  • Meet regularly to discuss the course material and work on assignments together
Five other activities
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Review 'Machine Learning: A Probabilistic Perspective' by Kevin Murphy
Reviewing this foundational book can help provide a deeper understanding of probabilistic machine learning concepts.
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  • Read chapters 1-3 to gain an introduction to probabilistic machine learning
  • Work through the exercises in chapter 4 to practice applying the concepts
Follow the 'Machine Learning Specialization' on Coursera
This specialization will provide a comprehensive overview of machine learning concepts and techniques.
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  • Complete the four courses in the specialization
  • Participate in the discussion forums to ask questions and share your insights
Complete the 'Machine Learning Practice Problems' by Sebastian Raschka
This book provides a comprehensive set of practice problems to help you master machine learning algorithms.
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  • Solve at least 20 problems from each chapter
  • Focus on understanding the underlying concepts rather than just memorizing solutions
Build a machine learning model to predict customer churn
This project will give you hands-on experience in applying machine learning to a real-world problem.
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  • Collect and prepare the data
  • Train and evaluate different machine learning models
  • Deploy the model and track its performance
Create a blog post or presentation on a machine learning project
This assignment will help you synthesize your knowledge and improve your communication skills.
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  • Choose a machine learning project that you have worked on
  • Write a blog post or create a presentation that describes the project, the techniques you used, and the results you achieved

Career center

Learners who complete Developing Machine Learning Solutions will develop knowledge and skills that may be useful to these careers:

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