Online Course popular free
The Machine Learning Process A-Z

Master the complete machine learning lifecycle: from problem definition to model deployment in production

4.9

808 reviews on
16701 students already have enrolled
  • Institute of Analytics
  • The Association of Data Scientists
  • E-Learning Quality Network
  • European Agency for Higher Education and Accreditation
  • Global Association of Online Trainers and Examiners

Skill level:

Advanced

Duration:

6 hours
  • Lessons (6 hours)

CPE credits:

7.5
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of professionals must earn to maintain their licenses and stay current with regulations and best practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's official website: www.nasbaregistry.org

Accredited:

certificate

What You Learn

  • Acquire real-world skills to deliver machine learning results.
  • Master all stages of the machine learning lifecycle.
  • Gain hands-on experience in data preprocessing for ML.
  • Improve your ML model’s results with advanced feature engineering.
  • Manage and execute complete ML projects independently.

Topics & tools

machine learningmodel evaluationdata preprocessingmachine learning processdata modelingdealing with imbalanced datacross validationfeature engineeringexploratory data analysismachine and deep learningpython

Your instructor

Course OVERVIEW

Description

CPE Credits: 7.5 Field of Study: Information Technology
Delivery Method: QAS Self Study
Data science education focuses too much on the algorithm itself. In reality, we can have only four lines of code and use them for a variety of problems. The heavy lift of an ML model is the end-to-end process. Jeff Li and Ken Jee walk you step-by-step through this process, so you can successfully take your next project from start to finish. You will learn everything you need to know to set your projects up for success. The Machine Learning Process A-Z course gives you a deep understanding of what machine learning really is. It helps you understand when you should and shouldn’t use this powerful tool. Jeff and Ken break down the specifics of the different problems you can encounter and how machine learning is used in specific domains. In the second part of the course, you will learn the entire modeling process. Jeff and Ken show you how to pull real results and make the ML model work for others, not just yourself. You will learn how to perform essential steps like data preprocessing. In this section, they also show you how to deal with null values and outliers. Next, you’ll see how to explore your data to frame your analysis. Additionally, the course deals with some of the visualization techniques that can help you to see the relationships in your data. After that, we go into feature engineering—one of the most important steps for improving your model’s results. That leads to cross-validation and how to handle bias and variance trade-off in your analysis. Finally, the instructors touch briefly on the model tuning process and how to productionize your work and documentation.

Prerequisites

  • Basic understanding of machine learning concepts.

Curriculum

145 lessons 1 exam

Free preview

Introduction

1.1 Introduction

3 min

ML Process Course - GitHub repository

1.2 ML Process Course - GitHub repository

1 min

Meet your instructors

1.3 Meet your instructors

1 min

How to use this course

1.4 How to use this course

1 min

Additional resources

1.5 Additional resources

1 min

Environment setup

1.6 Environment setup

1 min

Start for free

9 in 10

of our graduates landed a new AI & data job

after enrollment

9 in 10

people walk away career-ready

with practical data and AI skills.

94%

of AI and data science graduates

successfully change

or advance their careers.

ACCREDITED certificates

Craft a resume and LinkedIn profile you’re proud of—featuring certificates recognized by leading global institutions.

Earn CPE-accredited credentials that showcase your dedication, growth, and essential skills—the qualities employers value most.

  • Institute of Analytics
  • The Association of Data Scientists
  • E-Learning Quality Network
  • European Agency for Higher Education and Accreditation
  • Global Association of Online Trainers and Examiners
A LinkedIn profile mockup on a mobile screen showing Parker Maxwell, a Certified Data Analyst, with credentials from 365 Data Science listed under Licenses & Certification. A 365 Data Science Certificate of Achievement awarded to Parker Maxwell for completing the Data Analyst career track, featuring accreditation badges and a gold “Verified Certificate” seal.

How it WORKS

  • Lessons
  • Exercises
  • Projects
  • Practice Exams
  • AI Mock Interviews

Lessons

Learn through short, simple lessons—no prior experience in AI or data science needed.

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Exercises

Reinforce your learning with mini recaps, hands-on coding, flashcards, fill-in-the-blank activities, and other engaging exercises.

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Projects

Tackle real-world AI and data science projects—just like those faced by industry professionals every day.

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Practice Exams

Track your progress and solidify your knowledge with regular practice exams.

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AI Mock Interviews

Prep for interviews with real-world tasks, popular questions, and real-time feedback.

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Student REVIEWS

A collage of student testimonials from 365 Data Science learners, featuring profile photos, names, job titles, and quotes or video play icons, showcasing diverse backgrounds and successful career transitions into AI and data science roles.