Certified Artificial Intelligence Practitioner (CAIP)
Gain hands-on experience to pass the CertNexus AIP-110 exam with the Certified Artificial Intelligence Practitioner (CAIP) course and lab. The lab is cloud-based, device-enabled, and can easily be integrated with an LMS. Interactive chapters comprehensively cover the AIP-110 exam objectives and provide understanding on the topics such as problem formulation, applied artificial intelligence, and machine learning in business; data collection, comprehension, cleaning, and engineering; analyze a data set to gain insights, algorithm selection, and model training, model handoff, ethics and oversight; and more.
- Price: $279.99
- Delivery method: eLearning
- DIR Discount: 20%
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Test Prep
50+ Pre Assessment Questions |
2+ Full Length Tests |
50+ Post Assessment Questions |
100+ Practice Test Questions
Features
27+ LiveLab |
00+ Minutes
Multiple Choice/Multiple Response
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- Get assistance every step of the way from our Texas-based team, ensuring your training experience is hassle-free and aligned with your goals.
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- Have confidence in an exceptional 95% approval rating from our students, reflecting outstanding satisfaction with our course content, program support, and overall customer service.
- Benefit from being taught by Professionally Certified Instructors with expertise in their fields and a strong commitment to making sure you learn and succeed.
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
- Follow a Machine Learning Workflow
- Topic C: Formulate a Machine Learning Problem
- Topic D: Select Appropriate Tools
- Summary
Lessons 3:
Collecting and Refining the Dataset
- Topic A: Collect the Dataset
- Topic B: Analyze the Dataset to Gain Insights
- Topic C: Use Visualizations to Analyze Data
- Topic D: Prepare Data
- Summary
Lessons 4:
Setting Up and Training a Model
- Topic A: Set Up a Machine Learning Model
- Topic B: Train the Model
- Summary
Lessons 5:
Finalizing a Model
- Topic A: Translate Results into Business Actions
- Topic B: Incorporate a Model into a Long-Term Business Solution
- Summary
Lessons 6:
Building Linear Regression Models
- Topic A: Build Regression Models Using Linear Algebra
- Topic B: Build Regularized Regression Models Using Linear Algebra
- Topic C: Build Iterative Linear Regression Models
- Summary
Lessons 7:
Building Classification Models
- Topic A: Train Binary Classification Models
- Topic B: Train Multi-Class Classification Models
- Topic C: Evaluate Classification Models
- Topic D: 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
- Summary
Lessons 12:
Promoting Data Privacy and Ethical Practices
- Topic A: Protect Data Privacy
- Topic B: Promote Ethical Practices
- Topic C: Establish Data Privacy and Ethics Policies
- Summary
Appendix A
- Mapping Certified Artificial Intelligence (AI) P…oner (Exam AIP-110) Objectives to Course Content
