Certified Artificial Intelligence Practitioner (CAIP)

(AIP-110.AK1) / ISBN : 978-1-64459-224-3

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This course includes:

Free pre-assessment and first 2 lessons

13+ Interactive Lessons

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Certificate of completion

About This Course

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.

Skills You’ll 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.

Interactive Lessons

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

Gamified TestPrep

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

Hands-On Labs

27+ LiveLab | 00+ Minutes

Lesson Plan

Introduction

Course Description
How to use this Course
Course-Specific Technical Requirements

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

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

Setting Up and Training a Model

Topic A: Set Up a Machine Learning Model
Topic B: Train the Model
Summary

Finalizing a Model

Topic A: Translate Results into Business Actions
Topic B: Incorporate a Model into a Long-Term Business Solution
Summary

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

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

Building Clustering Models

Topic A: Build k-Means Clustering Models
Topic B: Build Hierarchical Clustering Models
Summary

Building Decision Trees and Random Forests

Topic A: Build Decision Tree Models
Topic B: Build Random Forest Models
Summary

Building Support-Vector Machines

Topic A: Build SVM Models for Classification
Topic B: Build SVM Models for Regression
Summary

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

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

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Certified Artificial Intelligence Practitioner (CAIP)

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