IT Specialist Artificial Intelligence (INF-307) Pearson Course
Details
Our eLearning solution brings together the best training and study materials in one place — for a lower price. When purchased separately, you’ll spend $500.*
Included in this Pearson Course:*
Books: a $75 value
Videos: a $250 value
Practice Test by MeasureUp: a $100 value
Training Lab: a $75 value
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USD
$199.00
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 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: 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.
- None
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.
