Certified Artificial Intelligence Practitioner (CAIP)
(AIP-210.AK1)/ISBN:978-1-64459-489-6
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.
Here's what you will get
The Certified Artificial Intelligence Practitioner (CAIP) exam aims to validate that candidates possess the knowledge and skill set encompassing AI concepts, technologies, and tools necessary to excel as proficient AI practitioners across a broad spectrum of AI-related roles and responsibilities. This certification exam validates a vendor-neutral AI skill set with a focus on machine learning, enabling professionals to design and implement AI solutions. Candidates demonstrate proficiency in deploying effective AI environments.
Lessons
13+ Lessons | 245+ Exercises | 125+ Quizzes | 247+ Flashcards | 247+ Glossary of terms
TestPrep
50+ Pre Assessment Questions | 2+ Full Length Tests | 50+ Post Assessment Questions | 100+ Practice Test Questions
Hands-On Labs
21+ LiveLab | 14+ Video tutorials | 43+ Minutes
Need guidance and support? Click here to check our Instructor Led Course.
Here's what you will learn
Download Course OutlineLessons 1: Introduction
- Course Description
- How To Use This Course
- Course-Specific Technical Requirements
Lessons 2: Solving Business Problems Using AI and ML
- 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
Lessons 3: Preparing Data
- TOPIC A: Collect Data
- TOPIC B: Transform Data
- TOPIC C: Engineer Features
- TOPIC D: Work with Unstructured Data
- Summary
Lessons 4: Training, Evaluating, and Tuning a Machine Learning Model
- TOPIC A: Train a Machine Learning Model
- TOPIC B: Evaluate and Tune a Machine Learning Model
- Summary
Lessons 5: Building Linear Regression Models
- Topic A: Build Regression Models Using Linear Algebra
- Topic B: Build Regularized Linear Regression Models
- Topic C: Build Iterative Linear Regression Models
- Summary
Lessons 6: Building Forecasting Models
- TOPIC A: Build Univariate Time Series Models
- TOPIC B: Build Multivariate Time Series Models
- Summary
Lessons 7: Building Classification Models Using Logistic Regression and k-Nearest Neighbor
- 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
Lessons 8: Building Clustering Models
- TOPIC A: Build k-Means Clustering Models
- TOPIC B: Build Hierarchical Clustering Models
- Summary
Lessons 9: Building Decision Trees and Random Forests
- TOPIC A: Build Decision Tree Models
- TOPIC B: Build Random Forest Models
- Summary
Lessons 10: Building Support-Vector Machines
- TOPIC A: Build SVM Models for Classification
- TOPIC B: Build SVM Models for Regression
- Summary
Lessons 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 (RNN)
- Summary
Lessons 12: Operationalizing Machine Learning Models
- TOPIC A: Deploy Machine Learning Models
- TOPIC B: Automate the Machine Learning Process with MLOps
- TOPIC C: Integrate Models into Machine Learning Systems
- Summary
Lessons 13: Maintaining Machine Learning Operations
- TOPIC A: Secure Machine Learning Pipelines
- TOPIC B: Maintain Models in Production
- Summary
Hands-on LAB Activities
Preparing Data
- Loading and Exploring the Dataset
- Transforming the Data and Using Engineering Features
- Working with Text Data
- Working with Image Data
Training, Evaluating, and Tuning a Machine Learning Model
- Training a Machine Learning Model
- Evaluating and Tuning a Machine Learning Model
Building Linear Regression Models
- Building a Regression Model Using Linear Algebra
- Building a Regularized and Iterative Linear Regression Model
Building Forecasting Models
- Building a Univariate Time Series Model
- Building a Multivariate Time Series Model
Building Classification Models Using Logistic Regression and k-Nearest Neighbor
- Training a Binary Classification Model Using Logistic Regression
- Training a Binary Classification Model Using k- NN
- Training a Multi-Class Classification Model
Building Clustering Models
- Building a k-Means Clustering Model
- Building a Hierarchical Clustering Model
Building Decision Trees and Random Forests
- Building a Decision Tree Model and a Random Forest
Building Support-Vector Machines
- Building an SVM Model for Classification
- Building an SVM Model for Regression
Building Artificial Neural Networks
- Building an MLP
- Building a CNN
- Building an RNN
Exam FAQs
USD 350
Pearson VUE
Multiple Choice/Multiple Response
The exam contains 80 questions.
120 minutes