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Certified Artificial Intelligence (AI) Practitioner (Exam AIP-110) (Intermediate)

  • by Handson
  • Course level: Intermediate

Description

Certified Artificial Intelligence (AI) Practitioner(Intermediate)

Overview:
Artificial intelligence (AI) and machine learning (ML) have become an essential part of the toolset for
many organizations. When used effectively, these tools provide actionable insights that drive critical
decisions and enable organizations to create exciting, new, and innovative products and services. This
course shows you how to apply various approaches and algorithms to solve business problems through AI
and ML, follow a methodical workflow to develop sound solutions, use open source, off-the-shelf tools to
develop, test, and deploy those solutions, and ensure that they protect the privacy of users. This course
includes hands on activities for each topic area. For a detailed outline including activities, hardware
requirements and datasets please contact [email protected]

Benefits of the course

In this course, you will implement AI techniques in order to solve business problems.
You will:
• Specify a general approach to solve a given business problem that uses applied AI and ML.
• Collect and refine a dataset to prepare it for training and testing.
• Train and tune a machine learning model.
• Finalize a machine learning model and present the results to the appropriate audience.
• Build linear regression models.
• Build classification models.
• Build clustering models.
• Build decision trees and random forests.
• Build support-vector machines (SVMs).
• Build artificial neural networks (ANNs).
• Promote data privacy and ethical practices within AI and ML projects

Requirements/Instructions

To ensure your success in this course, you should have at least a high-level understanding of
fundamental AI concepts, including, but not limited to: machine learning, supervised learning,
unsupervised learning, artificial neural networks, computer vision, and natural language processing.
You can obtain this level of knowledge by taking the CertNexus AIBIZ™ (Exam AIZ-110) course.
You should also have experience working with databases and a high-level programming language such
as Python, Java, or C/C++. You can obtain this level of skills and knowledge by taking the following
Logical Operations or comparable course:
• Database Design: A Modern Approach
• Python® Programming: Introduction
• Python® Programming: Advanced

Targeted Audience

The skills covered in this course converge on three areas—software development, applied math and
statistics, and business analysis. Target students for this course may be strong in one or two or these of
these areas and looking to round out their skills in the other areas so they can apply artificial intelligence
(AI) systems, particularly machine learning models, to business problems.
So the target student may be a programmer looking to develop additional skills to apply machine learning
algorithms to business problems, or a data analyst who already has strong skills in applying math and
statistics to business problems, but is looking to develop technology skills related to machine learning.
A typical student in this course should have several years of experience with computing technology,
including some aptitude in computer programming.
This course is also designed to assist students in preparing for the CertNexus® Certified Artificial
Intelligence (AI) Practitioner (Exam AIP-110) certification.

Topics for this course

30 Lessons40h

Lesson 1: Solving Business Problems Using AI and ML

Topic A: Identify AI and ML Solutions for Business Problems00:00:00
Topic B: Formulate a Machine Learning Problem00:00:00
Topic C: Select Appropriate Tools00:00:00

Lesson 2: Collecting and Refining the Dataset

Lesson 3: Setting Up and Training a Model

Lesson 4: Finalizing a Model

Lesson 5: Building Linear Regression Models

Lesson 6: Building Classification Models

Lesson 7: Building Clustering Models

Lesson 8: Building Advanced Models

Lesson 9: Building Support-Vector Machines

Lesson 10: Building Artificial Neural Networks

Lesson 11: Promoting Data Privacy and Ethical Practices

12000

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