Certified Artificial Intelligence Practitioner (CAIP)

This Course Includes:

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; analyse a data set to gain insights, algorithm selection, and model training, model handoff, ethics and oversight.

What will you 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 1: Introduction

  • 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
  • Topic B: Follow a Machine Learning Workflow
  • Topic C: Formulate a Machine Learning Problem
  • Topic D: Select Appropriate Tools

Lessons 3: Collecting and Refining the Dataset

  • Topic A: Collect the Dataset
  • Topic B: Analyse the Dataset to Gain Insights
  • Topic C: Use Visualisations to Analyse Data
  • Topic D: Prepare Data

Lessons 4: Setting Up and Training a Model

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

Lessons 5: Finalising a Model

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

Lessons 6: Building Linear Regression Models

  • Topic A: Build Regression Models Using Linear Algebra
  • Topic B: Build Regularised Regression Models Using Linear Algebra
  • Topic C: Build Iterative Linear Regression Models

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

Lessons 8: Building Clustering Models

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

Lessons 9: Building Decision Trees and Random Forests

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

Lessons 10: Building Support-Vector Machines

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

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

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

Appendix A

  • Mapping Certified Artificial Intelligence (AI) Practitioner (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
  • Analysing a Dataset Using Statistical Measures
  • Analysing a Dataset Using Visualisations
  • 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 Normalising Features
  • Refitting and Testing the Model

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

Exam FAQs

There are no formal prerequisites for the certification exam.

No application fee

Pearson VUE

Multiple Choice/Multiple Response





13+ Lessons

Delivery Method:




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