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 Praktische Übungen — nothing to install
- 13 Interaktive Lektionen Und 48 topics mapped to the official exam objectives
- 381 Testfragen zur Praxis Und 2 Ausführliche Tests
Intermediate Selbstgesteuert · 1 Jahr Zugang
21 Praktische LiveLabs
Practice real IT tasks in guided environments.
- Reale Umgebungen
- Automatisch bewertet
- Keine Installation
01 / Fähigkeiten, die Sie erwerben
What you will be able to do
- 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 Strukturierte Lektionen Umfassende Abdeckung der zentralen Kursziele
-
21 Praktische LiveLabs Interaktive, geführte Szenarien mit sofortiger Auswertung
-
381 Übungsfragen Bewertungstests mit ausführlichen Antwortbegründungen
-
1 Jahr voller Zugang Selbstgesteuertes Lernen, jederzeit auf allen Geräten zugänglich
02 / Lektionen & Labore
See exactly what you will learn and practice
Unterrichtsplan
13 Interaktive Lektionen · 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
Praktische Übungen 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
03 / Prüfungsdetails
Certified Artificial Intelligence Practitioner (CAIP) Details
Der Kurs zum zertifizierten KI-Praktiker (CAIP) vermittelt Ihnen das Wissen, die Fähigkeiten und die praktische Erfahrung, die Sie benötigen, um im dynamischen Bereich der Künstlichen Intelligenz erfolgreich zu sein. Von grundlegenden Konzepten bis hin zu fortgeschrittenen Techniken deckt der Kurs die gesamte Bandbreite der KI-Technologien ab, darunter Maschinelles Lernen, Neuronale Netze, Verarbeitung natürlicher Sprache, Computer Vision und vieles mehr. Dieser Kurs bereitet Sie optimal auf die Prüfung zum Certified Artificial Intelligence Practitioner (CAIP) vor. Die CAIP-Prüfung bestätigt, dass Kandidaten über die notwendigen Kenntnisse und Fähigkeiten in Bezug auf KI-Konzepte, -Technologien und -Tools verfügen, um in einem breiten Spektrum KI-bezogener Rollen und Verantwortlichkeiten erfolgreich zu sein. Diese Zertifizierungsprüfung validiert herstellerneutrale KI-Kompetenzen mit Schwerpunkt auf maschinellem Lernen und befähigt Fachkräfte zur Entwicklung und Implementierung von KI-Lösungen. Die Kandidaten demonstrieren ihre Kompetenz im Einsatz effektiver KI-Umgebungen.
Bereit für die Prüfung?
Fügen Sie Ihren offiziellen AIP-210.AK1 Prüfungsgutschein zu Ihrer Bestellung hinzu.
Offizieller Gutschein · Schnelle Lieferung · Wiederholungspaket verfügbar04 / FAQS
Fragen, bevor Sie beginnen
What is a Certified AI Practitioner (CAIP)?+
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?+
What are the benefits of obtaining CAIP certification?+
Wie hoch ist die Prüfungsgebühr?+
Wo kann ich die Prüfung ablegen?+
Wie ist die Prüfung aufgebaut?+
Wie viele Fragen umfasst die Prüfung?+
Wie lange dauert die Prüfung?+
Wo finde ich weitere Informationen zu dieser Prüfung?+
Keep going
You might also like
Become A Certified AI Professional
Advance your career as an AI cert professional and become a sought-after expert in the AI industry.
- 1 Jahr voller Zugang
- 21 LiveLab enthalten
- Abschlusszertifikat
Keine Kreditkarte benötigt