The Best in Certified Artificial Intelligence Practitioner Training (CAIP)

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.

Systems Operations on AWS

Certified Artificial Intelligence Practitioner (CAIP)

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.

Systems Operations on AWS

Course Objectives

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.

Systems Operations on AWS

Course Outline

  • Lesson 1: Solving Business Problems Using AI and ML
    Topic A: Identify AI and ML Solutions for Business Problems
    Topic C: Formulate a Machine Learning Problem
    Topic D: Select Appropriate Tools 

  • Lesson 2: Collecting and Refining the Dataset
    Topic A: Collect the Dataset
    Topic B: Analyze the Dataset to Gain Insights
    Topic C: Use Visualizations to Analyze Data
    Topic D: Prepare Data 

  • Lesson 3: Setting Up and Training a Model
    Topic A: Set Up a Machine Learning Model
    Topic B: Train the Model 

  • Lesson 4: Finalizing a Model
    Topic A: Translate Results into Business Actions
    Topic B: Incorporate a Model into a Long-Term Business Solution 

  • Lesson 5: Building Linear Regression Models
    Topic A: Build a Regression Model Using Linear Algebra
    Topic B: Build a Regularized Regression Model Using Linear Algebra
    Topic C: Build an Iterative Linear Regression Model 

  • Lesson 6: Building Classification Models
    Topic A: Train Binary Classification Models
    Topic B: Train Multi-Class Classification Models
    Topic C: Evaluate Classification Models
    Topic D: Tune Classification Models 

  • Lesson 7: Building Clustering Models
    Topic A: Build k-Means Clustering Models
    Topic B: Build Hierarchical Clustering Models

  • Lesson 8: Building Advanced Models
    Topic A: Build Decision Tree Models
    Topic B: Build Random Forest Models

  • Lesson 9: Building Support-Vector Machines
    Topic A: Build SVM Models for Classification
    Topic B: Build SVM Models for Regression

  • Lesson 10: Building Artificial Neural Networks
    Topic A: Build Multi-Layer Perceptrons (MLP)
    Topic B: Build Convolutional Neural Networks (CNN)

  • Lesson 11: Promoting Data Privacy and Ethical Practices
    Topic A: Protect Data Privacy
    Topic B: Promote Ethical Practices
    Topic C: Establish Data Privacy and Ethics Policies



Systems Operations on AWS

Target Student

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. 


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

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Our award winning superior Certified Artificial Intelligence Practioner training is designed to help you set effective business goals and attain measurable business outcomes. With return clients and multiple testimonials, we have established ourselves as a premier training solution provider for corporate teams across the globe, providing nothing less than the best corporate training in the marketplace.

Systems Operations on AWS

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