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Complete the Machine Learning course and, optionally, get accredited as a Certified Machine Learning Specialist by passing the certification exam. You can purchase the course now and get the exam later, or you can get them together at a discount as part of the Certification Bundle.

Upon completing the course you will receive a digital certificate of completion, as well as a digital training badge from Acclaim/Credly. Upon getting certified you will also receive an official Machine Learning Specialist digital accreditation certificate and certification badge from Acclaim/Credly, along with an account that can be used to verify your certification status.

The Machine Learning course is comprised of the following 3 course modules, each of which has an estimated completion time of 10 hours:

  • Module 7: Fundamental Machine Learning
  • Module 8: Advanced Machine Learning
  • Module 9: Machine Learning Lab

Choose the Certification Bundle to receive the entire course together with the online-proctored certification exam and a set of practice exam questions, all at a bundle discount.

Upon purchasing this course, you will automatically receive access via the Online Interactive eLearning platform. To provide you with the greatest flexibility, you will also have the option to access the course materials via two additional eLearning formats, at no extra cost. All three eLearning formats are briefly described below. A more detailed comparison can be found here.
  1. For everyday learning: An online interactive eLearning platform with individual lessons, as well as interactive and automatically graded exercises and practice questions.
  2. For learning on-the-go: A study kit platform with access to full course documents that support online/offline synching, annotations, comments, custom bookmarks and cross-document searches.
  3. For your reference: A set of printable PDF documents that you can keep (for all course workbooks and posters).
All three forms of access are subject to Arcitura’s *. Upon purchase, access to the online interactive eLearning platform (1) is provided within one business day. Access to the study kits (2) and the PDF documents (3) is provided upon request.

Shown below are the digital contents and the topic outline for each course module:

Module 7: Fundamental Machine Learning

This course module provides an easy-to-understand overview of machine learning for anyone interested in how it works, what it can and cannot do and how it is commonly utilized in support of business goals. The module covers common algorithm types and further explains how machine learning systems work behind the scenes. The base module materials are accompanied with an informational supplement covering a range of common algorithms and practices.

Course Module Contents

  • Workbook Lessons (100+ pages)
  • Video Lessons (for all topics)
  • Mind Map Poster
  • Symbol Legend Poster

  • Machine Learning Algorithms and Practices Reference Supplement
  • Practice Exam Questions
  • PDFs of Workbook and Posters (printable)

Topics Covered

  • Machine Learning Business and Technology Drivers
  • Machine Learning Benefits and Challenges
  • Machine Learning Usage Scenarios
  • Datasets, Structured, Unstructured and Semi Structured Data
  • Models, Algorithms, Model Training and Learning
  • How Machine Learning Works
  • Collecting and Pre-Processing Training Data
  • Algorithm and Model Selection
  • Training Models and Deploy Trained Models
  • Machine Learning Algorithms and Practices

  • Supervised Learning, Classification, Decision Tree
  • Regression, Ensemble Methods, Dimension Reduction
  • Unsupervised Learning and Clustering
  • Semi-Supervised and Reinforcement Learning
  • Machine Learning Best Practices
  • How Machine Learning Systems Work
  • Common Machine Learning Mechanisms
  • How Mechanisms Are Used in Model Training
  • Machine Learning and Deep Learning, Artificial Intelligence (AI)

Module 8: Advanced Machine Learning

This course module delves into the many algorithms, methods and models of contemporary machine learning practices to explore how a range of different business problems can be solved by utilizing and combining proven machine learning techniques.

Course Module Contents

  • Workbook Lessons (100+ pages)
  • Video Lessons (for all topics)
  • Mind Map Poster

  • Patterns and Mechanisms Mapping Poster
  • Practice Exam Questions
  • PDFs of Workbook and Posters (printable)

Topics Covered

  • Data Exploration Patterns
  • Central Tendency Computation, Variability Computation
  • Associativity Computation, Graphical Summary Computation
  • Data Reduction Patterns
  • Feature Selection, Feature Extraction
  • Data Wrangling Patterns
  • Feature Imputation, Feature Encoding
  • Feature Discretization, Feature Standardization
  • Supervised Learning Patterns

  • Numerical Prediction, Category Prediction
  • Unsupervised Learning Patterns
  • Category Discovery, Pattern Discovery
  • Model Evaluation Patterns, Baseline Modeling
  • Training Performance Evaluation, Prediction Performance Evaluation
  • Model Optimization Patterns
  • Ensemble Learning, Frequent Model Retraining
  • Lightweight Model Implementation, Incremental Model Learning

Module 9: Machine Learning Lab

This course module presents participants with a series of exercises and problems that are designed to test their ability to apply their knowledge of topics covered in previous modules. Completing this lab will help highlight areas that require further attention and will help prove proficiency in machine learning systems and techniques, as they are applied and combined to solve real-world problems.

Course Module Contents

  • Lab Exercise Booklet
  • Mind Map Poster

  • Practice Exam Questions
  • PDFs of Exercise Booklet and Poster (printable)

Topics Covered

  • Reading Exercise 9.1: Mini Case Study: RHE R&D Company
  • Lab Exercise 9.2: Retrieving the Training Data
  • Lab Exercise 9.3: Selecting the Correct Algorithm
  • Lab Exercise 9.4: Modeling Features and Representation
  • Lab Exercise 9.5: Measuring and Optimizing the Trained Model

  • Lab Exercise 9.6: Correcting Inconsistent Features
  • Reading Exercise 9.7: Mini Case Study: GTO Financial Institution
  • Lab Exercise 9.8: Identifying Customer Transaction Data
  • Lab Exercise 9.9: Assessing Customer Risk
  • Lab Exercise 9.10: Identifying Alarming Spending Patterns

Learn About Arcitura: Take the Video Tour

Watch these helpful informational videos to learn about Arcitura programs, courses and certifications.

About Arcitura

About Arcitura Courses

About Arcitura Certifications

What’s in an Arcitura Course


Each course provides a comprehensive curriculum with 2-8 modules and 20-80 hours of training.

More Than Just
Video Lessons

In addition to standard video lessons, courses include full-color workbooks and reference posters for all lessons.

Interactive & Graded

Courses also include interactive and graded exercises, interactive and graded self-tests and other supplements.

The Arcitura Difference


  • is authored by a dedicated courseware development team
  • has a self-test, accreditation exam and professional certification
  • is available via two different eLearning platforms


  • undergo a common development process
  • are authored to be consistent in quality, structure and style
  • share a common vocabulary and symbol notation
  • are authored in collaboration with subject matter experts

Take Your Skills Anywhere

Regardless of whether you are an individual looking to boost your career or an organization looking to up-skill a team, Arcitura courses and certifications provide a sound investment.

Because both courses and accreditations are vendor-neutral, they empower you with skills and credentials that you can take to wherever you need to go.

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