Premium Features

Previous Buy now to get complete version Next
  • Home
uCertify Logo
  • login
  • Signup
    • Help & Support
    • Accessibility
    • Testimonials
  • Powered by uCertify
  • Request Demo
  • Hello GuestLogin or Signup
  • Feedback & Support
    • Support
    • Keyboard Shortcuts
    • Send Feedback
Scroll to top button

Certified Artificial Intelligence Practitioner (CAIP)

(AIP-110.AK1) / ISBN: 978-1-64459-224-3
This course includes
Lessons
TestPrep
LiveLab
Mentoring (Add-on)
AIP-110.AK1 : Certified Artificial Intelligence Practitioner (CAIP)
Try this course Pre-Assessment and first two Lessons free No credit card required
Are you an instructor? Teach using uCertify products
Request a free evaluation copy

Certified Artificial Intelligence Practitioner (CAIP)

Gain hands-on experience to pass the CertNexus AIP-110 exam with the Certified Artificial Intelligence Practitioner (CAIP) course and lab. The lab is cloud-based, device-enabled, and can easily be integrated with an LMS. Interactive chapters comprehensively cover the AIP-110 exam objectives and provide understanding on the topics such as problem formulation, applied artificial intelligence, and machine learning in business; data collection, comprehension, cleaning, and engineering; analyze a data set to gain insights, algorithm selection, and model training, model handoff, ethics and oversight; and more.
Here's what you will get

The Certified Artificial Intelligence Practitioner certification exam is designed for professionals seeking to demonstrate a vendor-neutral, cross-industry skillset within AI and with a focus on machine learning to design, implement, and handoff an AI solution or environment. The certification exam will prove a candidate's knowledge of AI concepts, technologies, and tools that will enable them to become a capable AI practitioner in a wide variety of AI-related job functions.

Lessons
  • 13+ Lessons
  • 136+ Quizzes
  • 218+ Flashcards
  • 221+ Glossary of terms
TestPrep
  • 50+ Pre Assessment Questions
  • 2+ Full Length Tests
  • 50+ Post Assessment Questions
  • 100+ Practice Test Questions
LiveLab
  • 27+ LiveLab
Here's what you will learn
Download Course Outline
Lesson 1: Introduction
  • Course Description
  • How to use this Course
  • Course-Specific Technical Requirements
Lesson 2: Solving Business Problems Using AI and ML
  • Topic A: Identify AI and ML Solutions for Business Problems
  • Follow a Machine Learning Workflow
  • Topic C: Formulate a Machine Learning Problem
  • Topic D: Select Appropriate Tools
  • Summary
Lesson 3: 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
  • Summary
Lesson 4: Setting Up and Training a Model
  • Topic A: Set Up a Machine Learning Model
  • Topic B: Train the Model
  • Summary
Lesson 5: Finalizing a Model
  • Topic A: Translate Results into Business Actions
  • Topic B: Incorporate a Model into a Long-Term Business Solution
  • Summary
Lesson 6: Building Linear Regression Models
  • Topic A: Build Regression Models Using Linear Algebra
  • Topic B: Build Regularized Regression Models Using Linear Algebra
  • Topic C: Build Iterative Linear Regression Models
  • Summary
Lesson 7: 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
  • Summary
Lesson 8: Building Clustering Models
  • Topic A: Build k-Means Clustering Models
  • Topic B: Build Hierarchical Clustering Models
  • Summary
Lesson 9: Building Decision Trees and Random Forests
  • Topic A: Build Decision Tree Models
  • Topic B: Build Random Forest Models
  • Summary
Lesson 10: Building Support-Vector Machines
  • Topic A: Build SVM Models for Classification
  • Topic B: Build SVM Models for Regression
  • Summary
Lesson 11: Building Artificial Neural Networks
  • Topic A: Build Multi-Layer Perceptrons (MLP)
  • Topic B: Build Convolutional Neural Networks (CNN)
  • Topic C: Build Recurrent Neural Networks
  • Summary
Lesson 12: Promoting Data Privacy and Ethical Practices
  • Topic A: Protect Data Privacy
  • Topic B: Promote Ethical Practices
  • Topic C: Establish Data Privacy and Ethics Policies
  • Summary
Appendix A
  • Mapping Certified Artificial Intelligence (AI) P...oner (Exam AIP-110) Objectives to Course Content

Hands on Activities (Live Labs)

Collecting and Refining the Dataset

  • Examining the Structure of a Machine Learning Dataset
  • Loading the Dataset
  • Exploring the General Structure of the Dataset
  • Analyzing a Dataset Using Statistical Measures
  • Analyzing a Dataset Using Visualizations
  • Splitting the Training and Testing Datasets and Labels

Setting Up and Training a Model

  • Setting Up a Machine Learning Model
  • Dealing with Outliers
  • Scaling and Normalizing Features
  • Refitting and Testing the Model

Building Linear Regression Models

  • Building a Regression Model using Linear Algebra
  • Building a Linear Regression Model to Predict Diabetes Progression
  • Building a Regularized Linear Regression Model
  • Building an Iterative Linear Regression Model

Building Classification Models

  • Creating a Logistic Regression Model to Predict Breast Cancer Recurrence
  • Training Binary Classification Models
  • Training a Multi-Class Classification Model
  • Evaluating a Classification Model
  • Tuning a Classification Model

Building Clustering Models

  • Building a k-Means Clustering Model
  • Building a Clustering Model for Customer Segmentation
  • Building a Hierarchical Clustering Model

Building Decision Trees and Random Forests

  • Building a Decision Tree Model
  • Building a Random Forest Model

Building Support-Vector Machines

  • Building an SVM Model for Classification
  • Building an SVM Model for Regression

Building Artificial Neural Networks

  • Building an MLP
Exam FAQs
What are the prerequisites for this exam? There are no formal prerequisites for the certification exam.
What is the exam registration fee? No application fee
Where do I take the exam? Pearson VUE
What is the format of the exam? Multiple Choice/Multiple Response
How many questions are asked in the exam? The exam contains 80 questions.
What is the duration of the exam? 120 minutes
What is the passing score? 60%
What is the exam's retake policy? Any candidates who do not pass a CertNexus certification exam on the first attempt are eligible for one free retake after 30 calendar days from the time they took the initial exam. All CertNexus certification exam vouchers include one free retake. Candidates must purchase another voucher for any subsequent attempts beyond the first free retake.
What is the validity of the certification? To be declared
Where can I find more information about this exam? Know more about the AIP-110
What are the career opportunities after passing this exam?
  • AI Developer
  • Data Scientist
  • Avatar Animator
  • Applied Scientist
  • Research Scientist
  • Machine Learning Scientist
  • Conversation/Content interface writer
×
uc logo for app downloadDownload our uCertify App [lms_setting_placeholder: This filed is used to set the LMS settings.

Share with your friends and colleagues

We use cookies to enhance your experience. By continuing to visit this site you agree to our use of cookies. More information
Accept