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Microsoft Future Ready: Principles of Machine Learning with Python Programming

Understand the theory of machine learning before gaining practical experience using Python programming.

Microsoft Future Ready: Principles of Machine Learning with Python Programming
  • Duration

    5 weeks
  • Weekly study

    5 hours
  • 100% online

    Learn at your own paceHow it works
  • Included in an ExpertTrack

    Course 3 of 4
  • Get full ExpertTrack access

    $39/monthFind out more

This course is part of the Ethics Laws and Implementing an AI Solution on Microsoft Azure ExpertTrack, helping you understand AI ethics and laws while developing advanced AI and machine learning skills.

During this course, you’ll learn the theory of machine learning before gaining practical experience in building, validating and deploying machine learning models.

You’ll also delve into Python and Azure Notebooks, two powerful tools to help you learn how to build and derive insights.

Learn the fundamentals of machine learning and AI theory

This course will teach you the concepts and theories which drive AI technology today. You’ll learn the core principles of machine learning categories, including supervised and unsupervised learning, regression techniques and how algorithms behave and learn in machines.

Gain practical experience in how to deploy machine learning models

You’ll learn how to effectively deploy machine learning models, and understand the gap between IT and data science in putting a model into production. This will further your knowledge in the field and help you understand how this practice sits in the workplace.

Get hands-on experience using Python programming and Azure Notebooks

In this course you’ll learn the importance of evaluating your data before developing algorithms. You’ll explore how to prepare and clean data sets as well as feature engineering techniques.

Following the theory, you’ll get hands-on experience of the ways you can evaluate data using Python and Azure Notebooks.

By the end of the course, you’ll understand the basic machine learning concepts, and gain experience using powerful tools such as Python with state-of-the-art machine learning algorithms. This knowledge will further help you understand the relationship in complex data.

What topics will you cover?

  • Introduction to Machine Learning & Knowledge Discovery
  • Exploring & Evaluating Data
  • Cleaning & Preparing Data
  • Getting Started with Supervised Machine Learning
  • Improving Machine Learning Model Performance
  • Machine Learning Algorithms
  • Unsupervised Machine Learning

Prove you're job ready

Highlight the new, job-relevant skills you’ve gained and supplement existing qualifications with a hard-earned, industry-specific digital certificate – plus one for every course within your ExpertTrack.

  • Learn the latest in your chosen industry or subject.
  • Complete each course and pass assessments.
  • Receive certificates validated by the educating organisation.
  • Impress employers with learning outcomes you can add to your CV.
  • Make your career dreams a reality.

Download a PDF

What will you achieve?

By the end of the course, you‘ll be able to...

  • Data exploration, preparation and cleaning
  • Supervised machine learning techniques
  • Unsupervised machine learning techniques
  • Model performance improvement

Who is the course for?

This course is part of the Microsoft Professional Program Certificate in Data Science and the Microsoft Professional Program in Artificial Intelligence. It provides a foundational understanding of machine learning using python, useful for anyone new to learning python, or wishing to use python to build machine learning solutions.

The practical elements of this course involve building end-to-end workflows by combining the concepts and algorithms that will be introduced throughout the course. For the most part, you’ll be given the code you need to complete the exercises, but a basic knowledge of Python syntax will improve your understanding of what’s going on in the labs and demonstrations.

Who developed the course?

CloudSwyft Global Systems, Inc.

CloudSwyft has partnered with the top global technology companies to deliver cutting edge digital skills learning across the modern workplace.

About this ExpertTrack

Build your data science for AI skills with Python and Microsoft Azure, and explore its ethical and legal frameworks.

Learning on FutureLearn

Your learning, your rules

  • Courses are split into weeks, activities, and steps, but you can complete them as quickly or slowly as you like
  • Learn through a mix of bite-sized videos, long- and short-form articles, audio, and practical activities
  • Stay motivated by using the Progress page to keep track of your step completion and assessment scores

Join a global classroom

  • Experience the power of social learning, and get inspired by an international network of learners
  • Share ideas with your peers and course educators on every step of the course
  • Join the conversation by reading, @ing, liking, bookmarking, and replying to comments from others

Map your progress

  • As you work through the course, use notifications and the Progress page to guide your learning
  • Whenever you’re ready, mark each step as complete, you’re in control
  • Complete 90% of course steps and all of the assessments to earn your certificate

Want to know more about learning on FutureLearn? Using FutureLearn

Start learning today - free 7-day trial

After your free trial you can:

  • Pay $39 per month to keep learning online
  • Have complete control over your subscription; you can cancel any time
  • Work at your own pace and set your own deadlines at every stage
  • Only pay while you’re learning; the subscription will cancel automatically when you finish
  • Complete online assessments to test your knowledge and prove your skills
  • Earn digital course certificates and a final award that you can share online, with potential employers, and your professional network
  • Keep access to the content of courses you complete even after your subscription ends

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