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.

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Learning Objectives

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.

1

  • Introduction

  • Course Description
    How to use this Course
    Course-Specific Technical Requirements
2

  • Solving Business Problems Using AI and ML

  • #NAME?
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
13

  • Appendix A

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

Audience

The target student may be a programmer looking to develop additional skills to apply machine learning algorithms to business problems, or a data analyst who already has strong skills in applying math and statistics to business problems, but is looking to develop technology skills related to machine learning.

Language

English

Prerequisites

While there are no prerequisites for this course, please ensure you have the right level of experience to be successful in this training.

Length: 365.0 days ( hours)

Level:

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