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Certified AI Practitioner (CAIP) Training

Gain in-depth knowledge of AI algorithms, data science, and neural networks to prepare for the CAIP exam.

(AIP-210.AK1) / ISBN : 978-1-64459-489-6
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About This Course

Learn how to apply artificial intelligence (AI) and machine learning (ML) to solve real-world business problems. Our Certified Artificial Intelligence Practitioner (CAIP) training course covers various topics, including identifying AI and ML solutions, preparing and transforming data, and training and evaluating machine learning models. Above all, we provide you with hands-on experience in building models such as linear regression, forecasting, classification, clustering, decision trees, random forests, support vector machines, and neural networks. You’ll also learn about operationalizing ML models for production deployment.

Skills You’ll Get

  • Formulate AI and ML solutions for business problems
  • Collect, transform, and engineer data for ML models
  • Train, evaluate, and turn ML models effectively
  • Build and implement various ML models such as linear regression, forecasting, classification, clustering, decision trees, and more
  • Operationalize and deploy ML models in production environments 
  • Maintain and secure ML pipelines

 

Download Course Outline

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
  • TOPIC B: Formulate a Machine Learning Problem
  • TOPIC C: Select Approaches to Machine Learning
  • Summary
3

Preparing Data

  • TOPIC A: Collect Data
  • TOPIC B: Transform Data
  • TOPIC C: Engineer Features
  • TOPIC D: Work with Unstructured Data
  • Summary
4

Training, Evaluating, and Tuning a Machine Learning Model

  • TOPIC A: Train a Machine Learning Model
  • TOPIC B: Evaluate and Tune a Machine Learning Model
  • Summary
5

Building Linear Regression Models

  • Topic A: Build Regression Models Using Linear Algebra
  • Topic B: Build Regularized Linear Regression Models
  • Topic C: Build Iterative Linear Regression Models
  • Summary
6

Building Forecasting Models

  • TOPIC A: Build Univariate Time Series Models
  • TOPIC B: Build Multivariate Time Series Models
  • Summary
7

Building Classification Models Using Logistic Regression and k-Nearest Neighbor

  • TOPIC A: Train Binary Classification Models Using Logistic Regression
  • TOPIC B: Train Binary Classification Models Using k- Nearest Neighbor
  • TOPIC C: Train Multi-Class Classification Models
  • TOPIC D: Evaluate Classification Models
  • TOPIC E: 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 (RNN)
  • Summary
12

Operationalizing Machine Learning Models

  • TOPIC A: Deploy Machine Learning Models
  • TOPIC B: Automate the Machine Learning Process with MLOps
  • TOPIC C: Integrate Models into Machine Learning Systems
  • Summary
13

Maintaining Machine Learning Operations

  • TOPIC A: Secure Machine Learning Pipelines
  • TOPIC B: Maintain Models in Production
  • Summary

1

Preparing Data

  • Loading and Exploring the Dataset
  • Transforming the Data and Using Engineering Features
  • Working with Text Data
  • Working with Image Data
2

Training, Evaluating, and Tuning a Machine Learning Model

  • Training a Machine Learning Model
  • Evaluating and Tuning a Machine Learning Model
3

Building Linear Regression Models

  • Building a Regression Model Using Linear Algebra
  • Building a Regularized and Iterative Linear Regression Model
4

Building Forecasting Models

  • Building a Univariate Time Series Model
  • Building a Multivariate Time Series Model
5

Building Classification Models Using Logistic Regression and k-Nearest Neighbor

  • Training a Binary Classification Model Using Logistic Regression
  • Training a Binary Classification Model Using k- NN
  • Training a Multi-Class Classification Model
6

Building Clustering Models

  • Building a Hierarchical Clustering Model
7

Building Decision Trees and Random Forests

  • Building a Decision Tree Model and a Random Forest
8

Building Support-Vector Machines

  • Building an SVM Model for Classification
  • Building an SVM Model for Regression
9

Building Artificial Neural Networks

  • Building an MLP
  • Building a CNN
  • Building an RNN

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A Certified Artificial Intelligence Practitioner (CAIP) by CertNexus is a professional AI certification that demonstrates that you have a solid understanding of artificial intelligence (AI) concepts and their applications. As a Certified AI Practitioner, you can leverage AI tools and techniques to solve real-world problems and drive innovation within organizations.

AI practitioners work across various industries to develop and implement AI solutions. Their responsibilities may include: 

  • Identify business opportunities that can be addressed with AI 
  • Collect and prepare data for AI models
  • Build and train AI models using ML algorithms 
  • Deploy AI models into production environments 
  • Maintain and optimize AI models over time

The cost of the CAIP training courses can vary. You can expect to invest in the course material, exam feeds, and additional resources like mock exams.

A CAIP certification can enhance your credibility as an AI professional, open doors to new career opportunities, and increase your earning potential. It also demonstrates your commitment to staying up-to-date with the latest AI advancements.

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