# 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 Outline### Lessons 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