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Machine Learning Deep Dive: Business Applications and Coding Walkthroughs

Build the bridge between theoretical ML knowledge and its practical application: apply machine learning to solve complex business problems

4.9

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2443 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:

3 hours
  • Lessons (3 hours)

CPE credits:

4
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 machine learning skills that seamlessly connect theoretical concepts with real-world applications
  • Learn business applications of various algorithms—from linear regression to neural networks
  • Improve your understanding of theoretical ML concepts and get ready for advanced studies and applications
  • Boost your coding skills by going through the complete coding walkthroughs prepared by a world-class senior data scientist
  • Acquire insights on selecting the appropriate ML algorithms for specific business scenarios
  • Improve your career prospects with in-demand machine learning skills, essential for your success in an AI-driven world

Topics & tools

machine learningdata preprocessingdata modelingmachine learning processmodel evaluationdealing with imbalanced dataexploratory data analysiscross validationfeature engineeringmachine and deep learningprogrammingpython

Your instructor

Course OVERVIEW

Description

CPE Credits: 4 Field of Study: Information Technology
Delivery Method: QAS Self Study
Do you wish to learn how companies and non-profit organizations use Machine Learning? Are you interested in the practical coding aspects of building a Machine Learning model? If so, this is the perfect course for you. This course wraps up Ken Jee and Jeff Li’s series on Machine Learning. First, they showed you how the ML Process works in practice; then, they explained the fundamentals of the most popular Machine Learning algorithms used in the data science world. Now it’s time to consider the practical application of ML models and learn how to build them independently. In this course, Ken and Jeff will help you understand how big tech firms and small businesses can use different ML algorithms to boost their results. They’ll consider more straightforward methods which don’t require much data and computational power, such as linear regression, logistic regression, and SVMs. Still, they’ll also discuss several advanced use cases of neural networks and collaborative filtering. The coding walkthroughs section is a rare opportunity to gain practical intuition and see how an experienced data scientist builds an algorithm from scratch. This allows you to better grasp the theoretical concepts and logic studied earlier. You’ll learn to tackle real-world challenges when working on ML problems and develop essential debugging and problem-solving techniques.

Prerequisites

  • Basic understanding of machine learning concepts.
  • Python (version 3.8 or later), Pinecone account and API key, and a code editor or IDE (e.g., VS Code or Jupyter Notebook)
  • Intermediate Python skills are required.

Curriculum

34 lessons 9 exercises 1 exam

Free preview

Linear Regression

1.1 Linear Regression

3 min

Logistic Regression

1.2 Logistic Regression

6 min

Random Forest

1.3 Random Forest

3 min

K-Means Clustering

1.4 K-Means Clustering

2 min

K-Nearest Neighbors

1.5 K-Nearest Neighbors

2 min

Hierarchical Clustering

1.6 Hierarchical Clustering

2 min

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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.

96%

of our students recommend

365 Data Science.

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.