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.

Here’s what you will get

The Certified Artificial Intelligence Practitioner certification exam is designed for professionals seeking to demonstrate a vendor-neutral, cross-industry skillset within AI and with a focus on machine learning to design, implement, and handoff an AI solution or environment. The certification exam will prove a candidate’s knowledge of AI concepts, technologies, and tools that will enable them to become a capable AI practitioner in a wide variety of AI-related job functions.

Lessons

13+ Lessons | 136+ Quizzes | 218+ Flashcards | 221+ Glossary of terms

TestPrep

50+ Pre Assessment Questions | 2+ Full Length Tests | 50+ Post Assessment Questions | 100+ Practice Test Questions

Hand on lab

27+ LiveLab | 00+ Minutes

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

Hands-on LAB Activities

Collecting and Refining the Dataset

• Examining the Structure of a Machine Learning Dataset
• Loading the Dataset
• Exploring the General Structure of the Dataset
• Analyzing a Dataset Using Statistical Measures
• Module 1 Lab
• Splitting the Training and Testing Datasets and Labels

Setting Up and Training a Model

• Setting Up a Machine Learning Model
• Dealing with Outliers
• Scaling and Normalizing Features
• Module 2 Lab

Building Linear Regression Models

• Building a Regression Model using Linear Algebra
• Building a Linear Regression Model to Predict Diabetes Progression
• Building a Regularized Linear Regression Model
• Building an Iterative Linear Regression Model

Building Classification Models

• Creating a Logistic Regression Model to Predict Breast Cancer Recurrence
• Training Binary Classification Models
• Training a Multi-Class Classification Model
• Evaluating a Classification Model
• Tuning a Classification Model

Building Clustering Models

• Building a k-Means Clustering Model
• Building a Clustering Model for Customer Segmentation
• Building a Hierarchical Clustering Model

Building Decision Trees and Random Forests

• Building a Decision Tree Model
• Building a Random Forest Model

Building Support-Vector Machines

• Building an SVM Model for Classification
• Building an SVM Model for Regression

Building Artificial Neural Networks

• Building an MLP