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INF-307 Artificial Intelligence (AI) Specialist

Artificial Intelligence (AI) is one of the fastest growing areas of information technology today, transforming the way we think, learn,…

admin August 19, 2025

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About Course

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 instructor-led Skill Plus 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.

Audience:

  • This course is designed for students 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.
 
 
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Course Content

Lesson 1: Reviewing AI Fundamentals

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

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
3 Topics

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
4 Topics

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
4 Topics

Lesson 5: Designing a Machine Learning Approach

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

Lesson 6: Developing Classification Models

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

Lesson 7: Developing Regression Models

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

Lesson 8: Developing Cluster Models

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

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
3 Topics

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
3 Topics
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