Go from theory to hands-on execution — build AI models that solve real business problems.
Learn machine learning workflows, modeling, and deployment — not just concepts.
Earn your CertNexus CAIP certification while mastering practical AI/ML techniques.
Course Overview
Artificial intelligence is a competitive differentiator, and AI/ML professionals are in high demand. This 5-day hands-on course, built for real-world practitioners, takes you through the full machine learning development lifecycle — from data preparation to model building, evaluation, and ethical deployment.
Designed around the CertNexus AIP‑110 certification, this course emphasizes practical coding and modeling. You'll work on regression, classification, clustering, decision trees, SVMs, and deep learning models, all while learning how to refine datasets, tune algorithms, and promote ethical AI practices.
This course is perfect for data analysts, developers, and applied AI professionals looking to bridge skills across programming, statistics, and business needs.
Learning Objectives
Solving business problems with AI and ML workflows
Data collection, preparation, and visualization
Building and tuning models: regression, classification, clustering
Implementing decision trees, SVMs, neural networks, CNNs
Model evaluation using statistical metrics
Ethics, privacy, and governance in AI projects
Finalizing models for long-term deployment
Who Should Attend
Data analysts and engineers expanding into machine learning
Developers applying AI/ML in software products
BI professionals and business analysts working with predictive models
Anyone pursuing CertNexus AIP‑110 certification
Prerequisites
High-level understanding of AI concepts (e.g., from AIBIZ or equivalent)
Programming experience in Python, Java, or C/C++
Familiarity with databases and statistical concepts
Course Modules
Module 1: Solving Business Problems Using AI and ML
Identify AI/ML business cases, follow workflows, formulate ML problems, select appropriate tools.
Module 2: Collecting and Refining the Dataset
Work with open datasets, assess data quality, use visualizations, and prepare data for modeling.
Module 3: Setting Up and Training a Model
Design experiments, choose algorithms, train and tune ML models using iterative refinement.
Module 4: Finalizing a Model
Translate results into business action and integrate models into long-term solutions and pipelines.
Module 5–6: Regression and Classification Models
Build and evaluate linear, logistic, k-NN, and multi-class models using both algebraic and iterative approaches.
Module 7: Clustering Models
Implement k-means and hierarchical clustering, perform analysis and interpret groupings.
Module 8: Decision Trees and Random Forests
Create interpretable tree-based models and ensemble methods for robust predictions.
Module 9: Support-Vector Machines (SVMs)
Apply SVMs for classification and regression, explore kernel tricks, margin trade-offs, and tuning.
Module 10: Artificial Neural Networks (ANN & CNN)
Build MLPs, CNNs, and understand architecture, training, backpropagation, and GANs.
Module 11: Data Privacy and Ethical Practices
Ensure model transparency, mitigate bias, comply with regulations, and establish AI policy frameworks.
Professional Outcomes
This course prepares you for roles such as AI/ML Developer, Data Scientist, or Applied Machine Learning Engineer — professionals ready to build, deploy, and ethically manage AI systems.
Certification Details
Overview
As a candidate for this certification, you:
Specify solutions using applied AI and machine learning to solve business problems
Collect, refine, and analyze datasets using statistical and visualization techniques
Train, evaluate, and tune a wide range of models including regression, classifiers, clusters, and neural networks
Finalize and communicate results to stakeholders, and operationalize models in production
Promote ethical standards and protect privacy in AI projects
You should be proficient in:
Python and machine learning libraries
Exploratory data analysis and model tuning
Cross-disciplinary communication with business and technical stakeholders
Managing ethical, privacy, and performance trade-offs in AI development
Skills Measured
Solve Business Problems Using AI and ML
Collect and Refine the Dataset
Set Up and Train a Model
Finalize a Model for Production
Build and Evaluate:
- Linear & Regularized Regression
- Classification Models (Binary, Multi-Class)
- Clustering Models (k-Means, Hierarchical)
- Decision Trees & Random Forests
- SVMs
- Neural Networks (MLP, CNN)Promote Data Privacy and Ethical Practices in AI
Certification Logistics
Exam Code: AIP‑110
Format: 80 multiple-choice questions
Duration: 120 minutes (including 5 minutes for Candidate Agreement and 5 minutes for Pearson VUE tutorial)
Passing Score: 60% or 59% depending on exam form
Delivery: Pearson VUE proctored (online or in test center)
Credential: Certified Artificial Intelligence (AI) Practitioner by CertNexus
Frequently Asked Questions
Is this course hands-on?
Yes. It includes practical labs, coding activities, and real datasets.
Will I build my own models?
Yes. You’ll build models using Python and open-source tools like scikit-learn and Keras.
Is this course beginner-friendly?
No. It is designed for intermediate-level learners with coding and basic AI experience.
Are neural networks and deep learning covered?
Yes. Modules include MLPs, CNNs, and introductory GAN concepts.
Do I need prior experience with AI/ML tools?
Some experience is recommended. Prior completion of AIBIZ or equivalent is suggested.
Is this an official certification course?
Yes. It’s the official CertNexus training for the AIP‑110 exam.
Is this HRDC claimable?
Yes. It is fully claimable under HRD Corp for eligible Malaysian employers.
Can we run this for a corporate group?
Yes. GemRain offers in-house or virtual delivery for teams.
Will I get a certificate after the course?
Yes. You’ll receive a GemRain certificate of completion and be exam-ready.