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Certified Artificial Intelligence Practitioner (CAIP)

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

Introduction

  • Course Description
  • How to use this Course
  • Course-Specific Technical Requirements
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
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
4

Setting Up and Training a Model

  • Topic A: Set Up a Machine Learning Model
  • Topic B: Train the Model
  • Summary
5

Finalizing a Model

  • Topic A: Translate Results into Business Actions
  • Topic B: Incorporate a Model into a Long-Term Business Solution
  • Summary
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
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
8

Building Clustering Models

  • Topic A: Build k-Means Clustering Models
  • Topic B: Build Hierarchical Clustering Models
  • Summary
9

Building Decision Trees and Random Forests

  • Topic A: Build Decision Tree Models
  • Topic B: Build Random Forest Models
  • Summary
10

Building Support-Vector Machines

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

Appendix A

  • Mapping Certified Artificial Intelligence (AI) P...oner (Exam AIP-110) Objectives to Course Content

1

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
2

Setting Up and Training a Model

  • Setting Up a Machine Learning Model
  • Dealing with Outliers
  • Scaling and Normalizing Features
  • Module 2 Lab
3

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
4

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
5

Building Clustering Models

  • Building a k-Means Clustering Model
  • Building a Clustering Model for Customer Segmentation
  • Building a Hierarchical Clustering Model
6

Building Decision Trees and Random Forests

  • Building a Decision Tree Model
  • Building a Random Forest Model
7

Building Support-Vector Machines

  • Building an SVM Model for Classification
  • Building an SVM Model for Regression
8

Building Artificial Neural Networks

  • Building an MLP

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