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Game Developers

Artificial Intelligence

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.

IT Specialist Certification Exam Voucher - Artificial Intelligence.jfif

Self-Study Course Includes:

Books: a $75 value

Videos: a $250 value

Practice Test by MeasureUp: a $100 value

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With CertPREP, you get all this and
more for just $199.00. Get started today!

Course 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

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