Become an IT Specialist
in Artificial
Intelligence.
A self-paced program covering the fundamentals of AI — from identifying problems and preparing data to training models and deploying solutions. Earn the Pearson IT Specialist Artificial Intelligence (INF-307) certification.
- We are confident that you will pass your certification exam after successfully passing the Pearson practice tests - this is our pledge to you!
- If you follow this certification preparation method and fail the corresponding vendor exam within 30 days of your last practice test attempt, we'll give you another opportunity to take the vendor exam via an exam voucher or gift card for the amount of the exam.
AI is one of the fastest growing fields — start at the foundation.
Artificial Intelligence is one of the fastest-growing fields in information technology, transforming the way people live, learn and work. The Pearson IT Specialist Artificial Intelligence (INF-307) course is a self-paced program designed to introduce learners to the fundamentals of AI — identifying AI problems, managing data, building and testing AI models, and deploying AI solutions in real-world applications.
- Pearson IT Specialist Artificial Intelligence certification — code INF-307
- Face-to-face teaching delivered by a Pearson Approved Training Centre
- Flexible learning supported by Pearson resources
- Certification booking can be arranged with MCCA
Designed for beginners, upskillers & the AI-curious.
No prior experience in coding or data science is required — only curiosity and the willingness to practise.
Beginners & students
Anyone interested in AI and machine learning fundamentals from a clean start.
School leavers & graduates
University students and aspiring IT professionals building future-ready skills.
Professionals upskilling
Career-changers and operators exploring AI-related job opportunities.
Business curious
People who want to understand how AI is used in business, research and the wild.
Cert-bound learners
Anyone preparing for the Pearson IT Specialist (INF-307) certification.
Flexible learners
Those who want a self-paced course that flexes around work and life.
Ten skills, from problem framing to production.
By the end of the course, you'll be able to take an AI idea from a problem statement to a deployed, monitored solution.
- 01Describe the fundamentals of Artificial Intelligence.
- 02Define the problem you want to resolve with AI.
- 03Extract and transform data to be ready for analysis.
- 04Analyse and visualise prepared data.
- 05Design an ML approach and test your hypothesis.
- 06Train and evaluate a classification model.
- 07Train and evaluate a regression model.
- 08Train and evaluate a clustering model.
- 09Launch an AI/ML project end-to-end.
- 10Deploy and monitor an AI/ML model in production.
Expand the full lesson outline, practice tests & certification pathway.
Each section can be expanded or collapsed to view the detailed sub-lessons included in the INF-307 course structure. The Pearson course outline begins with a program-level introduction before progressing through the full lesson sequence.
Intro IT Specialist: Artificial Intelligence The Pearson course outline begins with a program-level introduction before progressing through the full lesson sequence.
- Introduction to the INF-307 course pathway
L 01 Reviewing AI Fundamentals Build a foundation in core AI concepts, practical uses, benefits and challenges.
- Introduction
- Lesson 1.1 — AI Concepts
- Lesson 1.2 — Uses for AI
- Lesson 1.3 — Benefits of AI
- Lesson 1.4 — Challenges of AI
- Lesson 1 Summary
L 02 Defining the Problem for AI Learn how to frame machine learning problems and choose appropriate AI or ML tools.
- Introduction
- 2.1 — Machine Learning Workflow
- 2.2 — Formulate the Machine Learning Problem
- 2.3 — Select AI/ML Tools
- Lesson 2 Summary
L 03 Accessing and Managing Data for AI Cover the data collection and preparation workflow needed for effective AI analysis and modelling.
- Introduction
- 3.1 — Collect and Assess Data
- 3.2 — Extract Data
- 3.3 — Transform Data
- 3.4 — Load Data
- Lesson 3 Summary
L 04 Analyzing Data Understand how to examine, visualise and preprocess data before using it in AI and ML workflows.
- Introduction
- 4.1 — Examine Data
- 4.2 — Analyze Data Distribution
- 4.3 — Visualize Data
- 4.4 — Preprocess Data for AI and ML
- Lesson 4 Summary
L 05 Designing a Machine Learning Approach Move from data understanding into practical model design and hypothesis testing.
- Introduction
- 5.1 — Identify ML Algorithms
- 5.2 — Test a Hypothesis
- Lesson 5 Summary
L 06 Developing Classification Models Train, tune and evaluate classification models for category-based predictions.
- Introduction
- 6.1 — Select, Train and Tune Classification Models
- 6.2 — Evaluate Classification Models
- Lesson 6 Summary
L 07 Developing Regression Models Build regression workflows focused on numeric prediction, regularisation and model evaluation.
- Introduction
- 7.1 — Train Regression Models
- 7.2 — Regularize Regression Models
- 7.3 — Evaluate Regression Models
- Lesson 7 Summary
L 08 Developing Cluster Models Learn how clustering models are trained, tuned and evaluated for unsupervised learning scenarios.
- Introduction
- 8.1 — Train and Tune Cluster Models
- 8.2 — Evaluate Cluster Models
- Lesson 8 Summary
L 09 Launching an AI/ML Project Explore project launch considerations including security, privacy, ethics and communication of results.
- Introduction
- 9.1 — Security and Privacy in AI/ML Projects
- 9.2 — Considerations for Ethical Use of AI/ML
- 9.3 — Communicate Results
- Lesson 9 Summary
L 10 Deploying and Monitoring an AI/ML Model in Production Focus on deployment, testing and production monitoring for real-world AI and ML solutions.
- Introduction
- 10.1 — Communicate Model Capabilities and Limitations
- 10.2 — Deploy and Test Models in Apps
- 10.3 — Support and Monitor AI/ML Solutions
- Lesson 10 Summary
Prac. Practice Tests Practice tests help learners revise the full course pathway and prepare for certification with greater confidence.
- IT Specialist: Artificial Intelligence Official Practice Tests
Cert. Get Certified! Certification scheduling and support information can be arranged with MCCA as part of your enrolment pathway.
- Scheduling and Information 1 question
Four weeks. Forty hours. Self-paced practice.
Evening live sessions plus self-paced quizzes, video tutorials and lab time. Move faster if you can — finish sooner.
Self-paced, deeply resourced.
All trainings are conducted face-to-face in the classroom and supported by the full Pearson Skilling Program resource kit.
Roles you can step into straight after.
Gain foundational AI, ML and data-analysis skills — and a clear runway to further study in AI, Machine Learning, Data Science and Cyber Security.
AI Support Specialist
Day-to-day support for AI tooling, users and pipelines.
Junior AI / ML Assistant
Support data scientists with training, evaluation and reporting.
Data & AI Analyst (Entry)
Translate business questions into data and AI-supported answers.
AI App Support Officer
Operate, monitor and support AI applications in production.
Automation Assistant
Build and maintain automated, AI-assisted digital workflows.
IT Support + AI Skills
Bring AI fluency to an existing IT support or service desk role.
This course also builds a pathway toward further studies and more advanced directions in Artificial Intelligence, Machine Learning, Data Science and Cyber Security.
Enrol online, three simple steps.
Share your details, pick a batch, and we'll be in touch within one business day to confirm your enrolment.
