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AIP-210.AK1
Certified Artificial Intelligence Practitioner (CAIP)
Gain in-depth knowledge of AI algorithms, data science, and neural networks to prepare for the CAIP exam.
- Practice in 21 Hands-On Labs — nothing to install
- 13 Interactive Lessons and 48 topics mapped to the official exam objectives
- 381 Practice Test Questions and 2 Full Length Tests
Intermediate Self-paced · 1 year access
21 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
13Interactive Lessons
48Topics
21LiveLab
381Practice Test Questions
12Videos
247Flashcards
247Glossary of terms
01 / Skills you'll get
What you will be able to do
Try Free →
No credit card required
Learn how to apply artificial intelligence (AI) and machine learning (ML) to solve real-world business problems. Our Certified Artificial Intelligence Practitioner (CAIP) training course covers various topics, including identifying AI and ML solutions, preparing and transforming data, and training and evaluating machine learning models. Above all, we provide you with hands-on experience in building models such as linear regression, forecasting, classification, clustering, decision trees, random forests, support vector machines, and neural networks. You’ll also learn about operationalizing ML models for production deployment.
- Formulate AI and ML solutions for business problems
- Collect, transform, and engineer data for ML models
- Train, evaluate, and turn ML models effectively
- Build and implement various ML models such as linear regression, forecasting, classification, clustering, decision trees, and more
- Operationalize and deploy ML models in production environments
- Maintain and secure ML pipelines
Course Highlights
-
13 Structured Lessons Comprehensive coverage of core course objectives
-
21 Hands-On LiveLabs Interactive guided scenarios with instant evaluation
-
381 Practice Questions Assessment tests with detailed answer rationales
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1 Year Full Access Self-paced learning accessible anytime on all devices
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
13 Interactive Lessons · 48 topics01 Introduction 3 topics +
- Course Description
- How To Use This Course
- Course-Specific Technical Requirements
02 Solving Business Problems Using AI and ML 4 topics +
- TOPIC A: Identify AI and ML Solutions for Business Problems
- TOPIC B: Formulate a Machine Learning Problem
- TOPIC C: Select Approaches to Machine Learning
- Summary
03 Preparing Data 5 topics · 4 LiveLab +
- TOPIC A: Collect Data
- TOPIC B: Transform Data
- TOPIC C: Engineer Features
- TOPIC D: Work with Unstructured Data
- Summary
4 LiveLab in this lesson — see the labs panel →
04 Training, Evaluating, and Tuning a Machine Learning Model 3 topics · 2 LiveLab +
- TOPIC A: Train a Machine Learning Model
- TOPIC B: Evaluate and Tune a Machine Learning Model
- Summary
2 LiveLab in this lesson — see the labs panel →
05 Building Linear Regression Models 4 topics · 2 LiveLab +
- Topic A: Build Regression Models Using Linear Algebra
- Topic B: Build Regularized Linear Regression Models
- Topic C: Build Iterative Linear Regression Models
- Summary
2 LiveLab in this lesson — see the labs panel →
06 Building Forecasting Models 3 topics · 2 LiveLab +
- TOPIC A: Build Univariate Time Series Models
- TOPIC B: Build Multivariate Time Series Models
- Summary
2 LiveLab in this lesson — see the labs panel →
07 Building Classification Models Using Logistic Regression and k-Nearest Neighbor 6 topics · 3 LiveLab +
- TOPIC A: Train Binary Classification Models Using Logistic Regression
- TOPIC B: Train Binary Classification Models Using k- Nearest Neighbor
- TOPIC C: Train Multi-Class Classification Models
- TOPIC D: Evaluate Classification Models
- TOPIC E: Tune Classification Models
- Summary
3 LiveLab in this lesson — see the labs panel →
08 Building Clustering Models 3 topics · 2 LiveLab +
- TOPIC A: Build k-Means Clustering Models
- TOPIC B: Build Hierarchical Clustering Models
- Summary
2 LiveLab in this lesson — see the labs panel →
09 Building Decision Trees and Random Forests 3 topics · 1 LiveLab +
- TOPIC A: Build Decision Tree Models
- TOPIC B: Build Random Forest Models
- Summary
1 LiveLab in this lesson — see the labs panel →
10 Building Support-Vector Machines 3 topics · 2 LiveLab +
- TOPIC A: Build SVM Models for Classification
- TOPIC B: Build SVM Models for Regression
- Summary
2 LiveLab in this lesson — see the labs panel →
11 Building Artificial Neural Networks 4 topics · 3 LiveLab +
- TOPIC A: Build Multi-Layer Perceptrons (MLP)
- TOPIC B: Build Convolutional Neural Networks (CNN)
- TOPIC C: Build Recurrent Neural Networks (RNN)
- Summary
3 LiveLab in this lesson — see the labs panel →
12 Operationalizing Machine Learning Models 4 topics +
- TOPIC A: Deploy Machine Learning Models
- TOPIC B: Automate the Machine Learning Process with MLOps
- TOPIC C: Integrate Models into Machine Learning Systems
- Summary
13 Maintaining Machine Learning Operations 3 topics +
- TOPIC A: Secure Machine Learning Pipelines
- TOPIC B: Maintain Models in Production
- Summary
Hands-On Labs Our edge
21 LiveLabs- Loading and Exploring the Dataset
- Transforming the Data and Using Engineering Features
- Working with Text Data
- Working with Image Data
- Training a Machine Learning Model
- Evaluating and Tuning a Machine Learning Model
- Building a Regression Model Using Linear Algebra
- Building a Regularized and Iterative Linear Regression Model
- Building a Univariate Time Series Model
- Building a Multivariate Time Series Model
- Training a Binary Classification Model Using Logistic Regression
- Training a Binary Classification Model Using k-NN
- Training a Multi-Class Classification Model
- Building a k-Means Clustering Model
- Building a Hierarchical Clustering Model
- Building a Decision Tree Model and a Random Forest
- Building an SVM Model for Classification
- Building an SVM Model for Regression
- Building an MLP
- Building a CNN
- Building an RNN
Labs run in your browser — nothing to install.
03 / Exam details
Certified Artificial Intelligence Practitioner (CAIP) Details
The Certified Artificial Intelligence Practitioner (CAIP) course is designed to equip you with the knowledge, skills, and practical experience needed to thrive in the dynamic field of Artificial Intelligence. From foundational concepts to advanced techniques, the course covers the breadth and depth of AI technologies, including machine learning, neural networks, natural language processing, computer vision, and more. The course helps you prepare for the Certified Artificial Intelligence Practitioner (CAIP) exam with confidence.
Questions
80
(per exam)
Duration
120 minutes
(per exam)
Exam Fee
USD 350
(per exam)
Delivered by
Pearson VUE
(in-person or online)
Question Format
Multiple Choice/Multiple Response
single/multiple choice, performance-based
Practice Tests
381 Practice Questions
(mapped to official exam objectives)
Certification
AIP-210.AK1
uCertify credential
Ready to take the exam?
Add your official AIP-210.AK1 exam voucher to your order.
Official Voucher · Fast delivery · Retake bundle available
Exam voucher is sold separately and not included with standard course.
04 / FAQs
Questions before you start
What is a Certified AI Practitioner (CAIP)?+
A Certified Artificial Intelligence Practitioner (CAIP) by CertNexus is a professional AI certification that demonstrates that you have a solid understanding of artificial intelligence (AI) concepts and their applications. As a Certified AI Practitioner, you can leverage AI tools and techniques to solve real-world problems and drive innovation within organizations.
What do artificial intelligence practitioners do?+
AI practitioners work across various industries to develop and implement AI solutions. Their responsibilities may include:
- Identify business opportunities that can be addressed with AI
- Collect and prepare data for AI models
- Build and train AI models using ML algorithms
- Deploy AI models into production environments
- Maintain and optimize AI models over time
What is the cost of the AI practitioner certification?+
The cost of the CAIP training courses can vary. You can expect to invest in the course material, exam feeds, and additional resources like mock exams.
What are the benefits of obtaining CAIP certification?+
A CAIP certification can enhance your credibility as an AI professional, open doors to new career opportunities, and increase your earning potential. It also demonstrates your commitment to staying up-to-date with the latest AI advancements.
What is the exam registration fee?+
USD 350
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
Where can I find more information about this exam?+
Keep going
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- 1 year of full access
- 21 LiveLab included
- Certificate of completion
No credit card required