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IT Specialist Artificial Intelligence (INF-307)

Prep and sit for the world’s most sought-after vendor certifications. We get you exam-ready.

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 instructor-led Pearson 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: Five (5) days.

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.

Duration: Approximately 40 hours of primary content. Each learner will progress at their own pace.

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.

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

Register & Pay

Click Enrol Now to open the registration form, fill in your details and programme of interest, then complete your payment securely. You’ll be enrolled and added to the course group as soon as payment is confirmed.

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