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 Laboratorios prácticos — nothing to install
- 13 Lecciones interactivas y 48 topics mapped to the official exam objectives
- 381 Preguntas del examen de práctica y 2 Pruebas completas
Intermediate A tu propio ritmo · 1 año de acceso
21 LiveLabs prácticos
Practice real IT tasks in guided environments.
- Entornos reales
- Calificación automática
- Sin instalación
13Lecciones interactivas
48Topics
21Laboratorio en vivo
381Preguntas del examen de práctica
12Vídeos
247Tarjetas didácticas
247Glosario de términos
01 / Habilidades que obtendrás
What you will be able to do
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No se requiere tarjeta de crédito
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 Lecciones estructuradas Cobertura completa de los objetivos principales del curso
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21 LiveLabs prácticos Escenarios interactivos guiados con evaluación instantánea
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381 Preguntas de práctica Pruebas de evaluación con justificaciones de respuesta detalladas
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1 año de acceso completo Aprendizaje a tu propio ritmo, accesible en cualquier momento y en todos los dispositivos
02 / Lecciones y laboratorios
See exactly what you will learn and practice
Plan de estudios
13 Lecciones interactivas · 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 Laboratorio en vivo +
- TOPIC A: Collect Data
- TOPIC B: Transform Data
- TOPIC C: Engineer Features
- TOPIC D: Work with Unstructured Data
- Summary
4 Laboratorio en vivo in this lesson — see the labs panel →
04 Training, Evaluating, and Tuning a Machine Learning Model 3 topics · 2 Laboratorio en vivo +
- TOPIC A: Train a Machine Learning Model
- TOPIC B: Evaluate and Tune a Machine Learning Model
- Summary
2 Laboratorio en vivo in this lesson — see the labs panel →
05 Building Linear Regression Models 4 topics · 2 Laboratorio en vivo +
- Topic A: Build Regression Models Using Linear Algebra
- Topic B: Build Regularized Linear Regression Models
- Topic C: Build Iterative Linear Regression Models
- Summary
2 Laboratorio en vivo in this lesson — see the labs panel →
06 Building Forecasting Models 3 topics · 2 Laboratorio en vivo +
- TOPIC A: Build Univariate Time Series Models
- TOPIC B: Build Multivariate Time Series Models
- Summary
2 Laboratorio en vivo in this lesson — see the labs panel →
07 Building Classification Models Using Logistic Regression and k-Nearest Neighbor 6 topics · 3 Laboratorio en vivo +
- 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 Laboratorio en vivo in this lesson — see the labs panel →
08 Building Clustering Models 3 topics · 2 Laboratorio en vivo +
- TOPIC A: Build k-Means Clustering Models
- TOPIC B: Build Hierarchical Clustering Models
- Summary
2 Laboratorio en vivo in this lesson — see the labs panel →
09 Building Decision Trees and Random Forests 3 topics · 1 Laboratorio en vivo +
- TOPIC A: Build Decision Tree Models
- TOPIC B: Build Random Forest Models
- Summary
1 Laboratorio en vivo in this lesson — see the labs panel →
10 Building Support-Vector Machines 3 topics · 2 Laboratorio en vivo +
- TOPIC A: Build SVM Models for Classification
- TOPIC B: Build SVM Models for Regression
- Summary
2 Laboratorio en vivo in this lesson — see the labs panel →
11 Building Artificial Neural Networks 4 topics · 3 Laboratorio en vivo +
- TOPIC A: Build Multi-Layer Perceptrons (MLP)
- TOPIC B: Build Convolutional Neural Networks (CNN)
- TOPIC C: Build Recurrent Neural Networks (RNN)
- Summary
3 Laboratorio en vivo 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
Laboratorios prácticos Our edge
21 Laboratorio en vivos- 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
Los laboratorios se ejecutan en tu navegador; no hay nada que instalar.
03 / Detalles del examen
Certified Artificial Intelligence Practitioner (CAIP) Detalles
El examen de Profesional Certificado en Inteligencia Artificial (CAIP) tiene como objetivo validar que los candidatos poseen el conocimiento y el conjunto de habilidades que abarcan los conceptos, tecnologías y...
Questions
80
(por examen)
Duration
120 minutos
(por examen)
Exam Fee
USD 350
(por examen)
Delivered by
Pearson VUE
(presencial u online)
Question Format
Multiple Choice/Multiple Response
opción única/múltiple, basado en el desempeño
Exámenes de práctica
381 Preguntas de práctica
(alineado con los objetivos oficiales del examen)
Certificación
AIP-210.AK1
uCertify credencial
¿Listo para presentar el examen?
Agrega tu AIP-210.AK1 bono de examen oficial a tu pedido.
Bono oficial · Entrega rápida · Paquete de repetición disponible
El bono de examen se vende por separado y no está incluido en el curso estándar.
04 / Preguntas frecuentes
Preguntas antes de empezar
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.
¿Cuál es la cuota de inscripción al examen?+
350 USD
¿Dónde hago el examen?+
Pearson VUE
¿Cuál es el formato del examen?+
Opción múltiple/Respuesta múltiple
¿Cuántas preguntas se hacen en el examen?+
El examen consta de 80 preguntas.
¿Cuál es la duración del examen?+
120 minutos
¿Dónde puedo encontrar más información sobre este examen?+
Keep going
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- 1 año de acceso completo
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