Certified Artificial Intelligence Practitioner (CAIP) - Virtual Learning
The Certified Artificial Intelligence Practitioner Course (CAIP) offered by Gulf Rowad is a comprehensive training course designed to equip participants with the necessary skills and knowledge to implement artificial intelligence (AI) and machine learning (ML) solutions in a business context. Throughout this course, participants will engage in developing actionable insights and innovative solutions by applying AI and ML to real-world business problems. The training is structured to follow a methodical workflow for developing data-driven solutions, ensuring that attendees can not only grasp but also apply these approaches to optimize and enhance business operations.
Ideal for software developers, IT professionals, and business analysts aiming to expand their expertise in artificial intelligence, the Certified Artificial Intelligence Practitioner Course focuses on the practical application of building, evaluating, and maintaining AI models. Attendees will delve into various topics such as data preparation, linear regression, forecasting models, classification models, and more sophisticated techniques like support-vector machines and neural networks. By the end of this course, individuals will be well-prepared to sit for the CertNexus Certified AI Practitioner Exam, solidifying their credentials as experts in the AI field and significantly enhancing their professional capability to contribute to their organizations' success in the evolving technological landscape.
Artificial intelligence (AI) and machine learning (ML) have become essential parts of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This Gulf Rowad Certified Artificial Intelligence Practitioner (CAIP) training course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, all while following a methodical workflow for developing data-driven solutions.
5 Days
1620 Sessions
Starting from USD 2,000
Available Face to Face
Course Objectives
At the end of this Certified Artificial Intelligence Practitioner (CAIP) training course, you will develop AI solutions for business problems. You will:
- Solve a given business problem using AI and ML.
- Prepare data for use in machine learning.
- Train, evaluate, and tune a machine learning model.
- Build linear regression models.
- Build forecasting models.
- Build classification models using logistic regression and k -nearest neighbor.
- Build clustering models.
- Build classification and regression models using decision trees and random forests.
- Build classification and regression models using support-vector machines (SVMs).
- Build artificial neural networks for deep learning.
- Put machine learning models into operation using automated processes.
- Maintain machine learning pipelines and models while they are in production.
Target Audience
The skills covered in this training course converge on four areas—software development, IT operations, applied math and statistics, and business analysis. Target participants for this course should be looking to build upon their knowledge of the data science process so that they can apply AI systems, particularly machine learning models, to business problems.
So, the target participant is likely a data science practitioner, software developer, or business analyst looking to expand their knowledge of machine learning algorithms and how they can help create intelligent decision-making products that bring value to the business.
A typical participant in this course should have several years of experience with computing technology, including some aptitude in computer programming.
This Certified Artificial Intelligence Practitioner (CAIP) training course is also designed to assist participants in preparing for the CertNexus® Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification.
Target Competencies
Personal Impact
By attending this Certified Artificial Intelligence Practitioner Training Course, participants will be able to:
- Boosts professional value through validated AI proficiency, enhancing career prospects and advancement opportunities.
- Expand their technical abilities in AI and machine learning, opening up new job opportunities and enabling the undertaking of complex projects.
- Enhance problem-solving and critical thinking skills by teaching individuals to address complex challenges with AI-driven solutions effectively.
- Make valuable networking opportunities with AI professionals, facilitating collaboration, mentorship, and potential job offers.
Course Outline
Day 1
Solving Business Problems Using AI and ML
- Identify AI and ML solutions for business problems
- Formulate a machine learning problem
- Select approaches to machine learning
Preparing Data
- Collect data
- Transform data
- Engineer features
- Work with unstructured data
Day 2
Training, Evaluating, and Tuning a Machine Learning Model
- Train a machine learning model
- Evaluate and tune a machine learning model
Building Linear Regression Models
- Build regression models using linear algebra
- Build regularized linear regression models
- Build iterative linear regression models
Building Forecasting Models
- Build univariate time series models
- Build multivariate time series models
Day 3
Building Classification Models Using Logistic Regression and k-Nearest Neighbor
- Train binary classification models using logistic regression
- Train binary classification models using k-nearest neighbor
- Train multi-class classification models
- Evaluate classification models
- Tune classification models
Building Clustering Models
- Build k-means clustering models
- Build hierarchical clustering models
Building Decision Trees and Random Forests
- Build decision tree models
- Build random forest models
Day 4
Building Support-Vector Machines
- Build SVM models for classification
- Build SVM models for regression
Building Artificial Neural Networks
- Build Multi-Layer Perceptrons (MLP)
- Build Convolutional Neural Networks (CNN)
- Build Recurrent Neural Networks (RNN)
Day 5
Operationalizing Machine Learning Models
- Deploy machine learning models
- Automate the machine learning process with MLOps
- Integrate models into machine learning systems
Maintaining Machine Learning Operations
- Secure machine learning pipelines
- Maintain models in production
2026 Schedule & Fees
Course Fees Starting From
USD
2,000
| Date | City | Language | Price | Action |
|---|---|---|---|---|
| 28 Jun - 02 Jul, 2026 | Online | English | USD 2,000 | Book |
| 28 Jun - 02 Jul, 2026 | Online | Arabic | USD 2,000 | Book |
| 05 Jul - 09 Jul, 2026 | Online | English | USD 2,000 | Book |
| 05 Jul - 09 Jul, 2026 | Online | Arabic | USD 2,000 | Book |
| 12 Jul - 16 Jul, 2026 | Online | Arabic | USD 2,000 | Book |
| 12 Jul - 16 Jul, 2026 | Online | English | USD 2,000 | Book |
| 19 Jul - 23 Jul, 2026 | Online | Arabic | USD 2,000 | Book |
| 19 Jul - 23 Jul, 2026 | Online | English | USD 2,000 | Book |
| 26 Jul - 30 Jul, 2026 | Online | English | USD 2,000 | Book |
| 26 Jul - 30 Jul, 2026 | Online | Arabic | USD 2,000 | Book |
| 02 Aug - 06 Aug, 2026 | Online | English | USD 2,000 | Book |
| 02 Aug - 06 Aug, 2026 | Online | Arabic | USD 2,000 | Book |
| 09 Aug - 13 Aug, 2026 | Online | Arabic | USD 2,000 | Book |
| 09 Aug - 13 Aug, 2026 | Online | English | USD 2,000 | Book |
| 16 Aug - 20 Aug, 2026 | Online | Arabic | USD 2,000 | Book |
| 16 Aug - 20 Aug, 2026 | Online | English | USD 2,000 | Book |
| 23 Aug - 27 Aug, 2026 | Online | Arabic | USD 2,000 | Book |
| 23 Aug - 27 Aug, 2026 | Online | English | USD 2,000 | Book |
| 30 Aug - 03 Sep, 2026 | Online | Arabic | USD 2,000 | Book |
| 30 Aug - 03 Sep, 2026 | Online | English | USD 2,000 | Book |
| 06 Sep - 10 Sep, 2026 | Online | Arabic | USD 2,000 | Book |
| 06 Sep - 10 Sep, 2026 | Online | English | USD 2,000 | Book |
| 13 Sep - 17 Sep, 2026 | Online | Arabic | USD 2,000 | Book |
| 13 Sep - 17 Sep, 2026 | Online | English | USD 2,000 | Book |
| 20 Sep - 24 Sep, 2026 | Online | English | USD 2,000 | Book |
| 20 Sep - 24 Sep, 2026 | Online | Arabic | USD 2,000 | Book |
| 27 Sep - 01 Oct, 2026 | Online | English | USD 2,000 | Book |
| 27 Sep - 01 Oct, 2026 | Online | Arabic | USD 2,000 | Book |
| 04 Oct - 08 Oct, 2026 | Online | English | USD 2,000 | Book |
| 04 Oct - 08 Oct, 2026 | Online | Arabic | USD 2,000 | Book |
| 11 Oct - 15 Oct, 2026 | Online | English | USD 2,000 | Book |
| 11 Oct - 15 Oct, 2026 | Online | Arabic | USD 2,000 | Book |
| 18 Oct - 22 Oct, 2026 | Online | Arabic | USD 2,000 | Book |
| 18 Oct - 22 Oct, 2026 | Online | English | USD 2,000 | Book |
| 25 Oct - 29 Oct, 2026 | Online | Arabic | USD 2,000 | Book |
| 25 Oct - 29 Oct, 2026 | Online | English | USD 2,000 | Book |
| 01 Nov - 05 Nov, 2026 | Online | Arabic | USD 2,000 | Book |
| 01 Nov - 05 Nov, 2026 | Online | English | USD 2,000 | Book |
| 08 Nov - 12 Nov, 2026 | Online | Arabic | USD 2,000 | Book |
| 08 Nov - 12 Nov, 2026 | Online | English | USD 2,000 | Book |
| 15 Nov - 19 Nov, 2026 | Online | Arabic | USD 2,000 | Book |
| 15 Nov - 19 Nov, 2026 | Online | English | USD 2,000 | Book |
| 22 Nov - 26 Nov, 2026 | Online | Arabic | USD 2,000 | Book |
| 22 Nov - 26 Nov, 2026 | Online | English | USD 2,000 | Book |
| 29 Nov - 03 Dec, 2026 | Online | English | USD 2,000 | Book |
| 29 Nov - 03 Dec, 2026 | Online | Arabic | USD 2,000 | Book |
| 06 Dec - 10 Dec, 2026 | Online | English | USD 2,000 | Book |
| 06 Dec - 10 Dec, 2026 | Online | Arabic | USD 2,000 | Book |
| 13 Dec - 17 Dec, 2026 | Online | Arabic | USD 2,000 | Book |
| 13 Dec - 17 Dec, 2026 | Online | English | USD 2,000 | Book |
| 20 Dec - 24 Dec, 2026 | Online | Arabic | USD 2,000 | Book |
| 20 Dec - 24 Dec, 2026 | Online | English | USD 2,000 | Book |
| 27 Dec - 31 Dec, 2026 | Online | English | USD 2,000 | Book |
| 27 Dec - 31 Dec, 2026 | Online | Arabic | USD 2,000 | Book |
Face to Face Courses
Face to Face
Certified Artificial Intelligence Practitioner (CAIP)
5 Days
1566 Sessions
Artificial Intelligence Ai
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