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CertPREP Courseware: IT Specialist Artificial Intelligence - Self-Paced

CertPREP IT Specialist Artificial Intelligence is a self-paced, beginner-friendly course designed to introduce learners to the foundational concepts and practical workflows of AI and machine learning. The course begins with a deep dive into AI applications, benefits, and ethical challenges, then guides learners through defining machine learning problems and selecting the right tools and frameworks.

Students will build core skills in accessing, transforming, and analyzing data, as well as training and evaluating models across classification, regression, and clustering use cases. The course also covers launching AI/ML projects, ensuring ethical use, managing privacy concerns, and deploying models in real-world environments. Ideal for future AI practitioners, data science enthusiasts, and IT professionals exploring machine learning.

Description

Artificial Intelligence (AI) is one of the fastest growing areas of information technology today, transforming the way we think, learn, and work. A certification in AI can help open a world of high-paying opportunities in industries everywhere.

This self-paced CertPREP IT Specialist Artificial Intelligence (INF-307) course covers the entire field of AI. From defining the AI problem that needs to be solved, managing the data, and building an AI model to solve it, to producing, deploying, and monitoring the model in an application. The course is specifically designed to train you for the IT Specialist Artificial Intelligence Certification.

An IT Specialist Artificial Intelligence Certification is proof or your ability to understand AI concepts and use the tools necessary to create AI applications. 

Duration: Approximately 40 hours of training. Every learner will progress at their own pace.

Course components:

  • Lessons
  • Video learning
  • MeasureUp Practice Test for IT Specialist Artificial Intelligence (INF-307) Practice Mode with remediation and Certification mode to simulate the test day experience.

Audience:

  • This course is designed for learners with the motivation to become an AI-enabled learner; as well as those who are curious about the professional applications of AI, ML, and associated technologies, and their use in research and career fields.

Prerequisites:

  • None

Course objectives:

Upon successful completion of this course, students should be able to:

  • Describe the fundamentals of AI.
  • Define the problem you want to resolve with AI.
  • Extract and transform data to be ready to be analyzed.
  • Analyze and visualize prepared data.
  • Design an ML approach and test your hypothesis.
  • Train and evaluate a classification model.
  • Train and evaluate a regression model.
  • Train and evaluate a cluster model.
  • Launch an AI/ML project.
  • Deploy and monitor an AI/ML model in production.

Required course materials: Self-paced CertPREP IT Specialist Artificial Intelligence (INF-307) Courseware.

Course Code: E031

Fee

$200.00 (GST-exclusive)
$218.00 (GST-inclusive)

Post-Course Support

  • We provide free consultation related to the subject matter after the course.
  • Please email your queries to enquiry@tertiaryinfotech.com and we will forward your queries to the subject matter experts.

Course Cancellation/Reschedule Policy

  • You can register your interest without upfront payment. There is no penalty for withdrawal of the course before the class commerce.
  • We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% for any paid amount.
  • Note the venue of the training is subject to changes due to availability of the classroom

Course Details

Training outline:

Lesson 1: Reviewing AI Fundamentals

  • Topic A: AI Concepts
  • Topic B: Uses for AI
  • Topic C: Benefits of AI
  • Topic D: Challenges of AI

Lesson 2: Defining the Problem for AI

  • Topic A: Machine Learning Workflow
  • Topic B: Formulate the Machine Learning Problem
  • Topic C: Select AI/ML Tools

Lesson 3: Accessing and Managing Data for AI

  • Topic A: Collect and Assess Data
  • Topic B: Extract Data
  • Topic C: Transform Data
  • Topic D: Load Data

Lesson 4: Analyzing Data

  • Topic A: Examine Data
  • Topic B: Analyze Data Distribution
  • Topic C: Visualize Data
  • Topic D: Preprocess Data for AI and ML

Lesson 5: Designing a Machine Learning Approach

  • Topic A: Identify ML Algorithms
  • Topic B: Test a Hypothesis

Lesson 6: Developing Classification Models

  • Topic A: Select, Train, and Tune Classification Models
  • Topic B: Evaluate Classification Models

Lesson 7: Developing Regression Models

  • Topic A: Train Regression Models
  • Topic B: Regularize Regression Models
  • Topic C: Evaluate Regression Models

Lesson 8: Developing Cluster Models

  • Topic A: Train and Tune Cluster Models
  • Topic B: Evaluate Cluster Models

Lesson 9: Launching an AI/ML Project

  • Topic A: Security and Privacy in AI/ML Projects
  • Topic B: Considerations for Ethical Use of AI/ML
  • Topic C: Communicate Results

Lesson 10: Deploying and Monitoring an AI/ML Model in Production

  • Topic A: Communicate Model Capabilities and Limitations
  • Topic B: Deploy and Test Models in Apps
  • Topic C: Support and Monitor AI/ML Solutions

Job Roles

  • AI/ML Support Technician
  • Data Analyst (Entry-Level)
  • Junior Machine Learning Engineer
  • AI Research Assistant
  • Data Science Intern
  • Machine Learning Model Tester
  • AI Project Assistant
  • Junior Data Preprocessing Specialist
  • Applied AI Intern
  • AI Ethics and Compliance Assistant
  • Business Intelligence Assistant
  • Predictive Analytics Support
  • AI Operations Support Technician
  • Model Deployment Assistant
  • AI/ML Documentation Specialist
  • Data Visualization Trainee
  • Junior Statistical Analyst
  • AI Quality Assurance Intern
  • Cloud AI Platform Support
  • Junior AI Consultant

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