Turn your programming skills into practical AI engineering capabilities for building smarter, data-driven applications.
This course takes developers from Python and machine learning fundamentals through model development, AI-assisted coding, automation, and application integration. Gain hands-on experience using modern AI coding assistants to accelerate development while learning how to train, evaluate, optimize, and integrate machine learning models into working applications.
- Why get trained: Apply Python, machine learning, AI-assisted coding, model evaluation, APIs, and intelligent automation
- Why it matters: Combining software engineering with AI enables developers to create intelligent applications while improving development speed and efficiency
- Who should attend: Developers, AI and ML engineers, data professionals, DevOps engineers, and technical professionals moving into AI development
Progress from using AI as a coding assistant to developing, evaluating, and integrating machine learning solutions into real applications. HRD Corp Claimable.
This course is part of Trainocate’s AI Training Roadmap 2026: A structured pathway from AI awareness to enterprise-scale AI transformation and governance which consists of the following courses:
- TMY-AIL: AI Literacy: Building Essential AI Skills for the Modern Workforce
- TMY-AIP: AI Productivity: Maximizing Workplace Efficiency with Generative AI
- TMY-AID: AI for Developers: Machine Learning with Python, AI Engineering & Intelligent Automation
- TMY-AIA: AI for Automation: Designing Intelligent Workflows with Generative AI, AI Agents & No-Code Automation
- TMY-AAI: Agentic AI: Building Autonomous AI Workflows for Productivity & Business Automation
- TMY-AISL: AI for Leaders: Strategic Leadership in the Age of Artificial Intelligence
- TMY-AIEG: AI Ethics & Governance: Responsible AI for Organizations
Overview
Artificial Intelligence is transforming modern software development, enabling developers to build intelligent, data-driven, and autonomous applications. Today’s developers are expected to understand not only software engineering principles but also machine learning, AI-assisted development, intelligent automation, and the effective use of AI coding assistants.
This intensive three-day hands-on workshop is designed to equip developers with practical skills in Python programming, Machine Learning, AI development workflows, and AI-powered software engineering. Participants will learn how to build, evaluate, and deploy machine learning models while leveraging the latest AI productivity tools to accelerate software development.
The course combines Python fundamentals, data preprocessing, exploratory data analysis, supervised and unsupervised machine learning, model evaluation, prompt engineering for developers, AI-assisted coding, intelligent debugging, automation, and rapid application development.
Participants will gain hands-on experience with modern AI development tools including GitHub Copilot, ChatGPT, ChatGPT Projects, Claude, Claude Artifacts, Google AI Studio, Gemini, Gemini Gems, Cursor AI, Windsurf, Continue.dev, Perplexity AI, Kimi AI, Manus AI, JupyterLab, VS Code, and selected open-source AI frameworks.
By the end of the program, participants will be able to design, build, evaluate, and deploy practical machine learning solutions while integrating AI into their daily software development workflow.
Skills Covered
Upon completion of this program, participants will be able to:
- Understand AI, Machine Learning, and modern AI engineering concepts.
- Develop Python programs for data processing and machine learning.
- Perform data wrangling and exploratory data analysis (EDA).
- Build supervised and unsupervised machine learning models.
- Evaluate and improve machine learning model performance.
- Apply AI-assisted software development using modern coding assistants.
- Automate software development tasks using AI tools.
- Build an end-to-end machine learning project using Python.
Prerequisites
Participants should have:
- Basic programming knowledge
- Basic Python programming experience
- Basic understanding of programming logic
- Familiarity with Windows or macOS
- Laptop with administrator access
- Stable internet connection
Recommended software installed before training:
- Python 3.12+
- Visual Studio Code
- JupyterLab / Notebook
- Git
- Anaconda (optional)
Accounts
- GitHub
- ChatGPT
- Claude
- Google Account
- GitHub Copilot Trial or License (recommended)
No previous Machine Learning experience is required.
Target Audience
This course is suitable for:
- Software Developers
- Python Developers
- AI Engineers
- Machine Learning Engineers
- Data Analysts
- Data Scientists
- Backend Developers
- Full Stack Developers
- DevOps Engineers
- Automation Engineers
- Research Engineers
- Computer Science Graduates
- IT Professionals interested in AI
Day 1
Python Programming & AI Developer Productivity
Module 1: Modern AI Development Landscape
- Introduction to AI Engineering
- AI vs Machine Learning vs Deep Learning
- Python for AI Development
- AI Development Lifecycle
- Choosing the Right AI Stack
- Overview of Modern AI Coding Assistants
- Setting up an AI Development Environment
Introduction to
- ChatGPT
- ChatGPT Projects
- GitHub Copilot
- Claude
- Claude Artifacts
- Cursor AI
- Windsurf
- Google AI Studio
- Gemini Gems
- Continue.dev
Hands-on Lab
- Configure Python environment
- Install required libraries
- Configure AI coding assistants
Module 2: Python Programming for Machine Learning
- Variables
- Data Types
- Operators
- Conditional Statements
- Loops
- Functions
- Modules
- Exception Handling
- File Handling
- Object-Oriented Programming
- Virtual Environments
Python Best Practices
Hands-on Lab
Build several Python mini applications.
Module 3: Data Analysis with Python
Libraries
- NumPy
- Pandas
Topics
- Importing datasets
- Cleaning data
- Missing values
- Duplicate handling
- Feature selection
- Data transformation
- Grouping
- Aggregation
- Feature engineering
Hands-on Lab
Perform complete data preprocessing on a real-world dataset.
Module 4: Exploratory Data Analysis (EDA)
Libraries
- Matplotlib
- Plotly
- Scikit-learn utilities
Topics
- Statistical summaries
- Correlation analysis
- Outlier detection
- Feature importance
- Data visualization
- Business insights
Hands-on Lab
Create an EDA report and visualization dashboard.
Day 2
Machine Learning Fundamentals
Module 5: Supervised Machine Learning
Topics
Regression
- Linear Regression
- Multiple Regression
Classification
- Logistic Regression
- Decision Tree
- Random Forest
- K-Nearest Neighbors
- Support Vector Machine
- Naïve Bayes
Concepts
- Training and testing
- Cross-validation
- Overfitting
- Underfitting
Hands-on Lab
Train multiple supervised learning models and compare their performance.
Module 6: Unsupervised Machine Learning
- Clustering
- K-Means
- Hierarchical Clustering
- DBSCAN
- Principal Component Analysis (PCA)
- Dimensionality Reduction
Applications
- Customer Segmentation
- Fraud Detection
- Pattern Discovery
- Recommendation Systems
Hands-on Lab
Cluster a business dataset and interpret the results.
Module 7: Model Evaluation & Optimization
Classification Metrics
- Accuracy
- Precision
- Recall
- F1 Score
- ROC Curve
- AUC
Regression Metrics
- MAE
- MSE
- RMSE
- R² Score
Model Improvement
- Hyperparameter tuning
- Grid Search
- Feature selection
- Feature scaling
- Pipeline creation
Hands-on Lab
Optimize machine learning models and compare performance improvements.
Module 8: AI-Assisted Coding & Intelligent Development
Using AI to
- Generate Python code
- Debug applications
- Refactor legacy code
- Write documentation
- Create unit tests
- Explain algorithms
- Generate SQL queries
- Build APIs
- Review pull requests
Using
- GitHub Copilot
- ChatGPT Projects
- Claude Artifacts
- Cursor AI
- Windsurf
- Google AI Studio
Hands-on Lab
Develop an AI-assisted Python application from requirements to implementation.
Day 3
AI Engineering, Automation & Capstone Project
Session 1 — AI-Powered Automation & Developer Productivity
- Prompt engineering for developers
- AI-assisted code generation
- Workflow automation
- Documentation automation
- Test case generation
- API development assistance
- AI-powered debugging
- Code review automation
- Building reusable developer workflows
Productivity Tools
- GitHub Copilot
- ChatGPT Projects
- Claude Artifacts
- Google AI Studio
- Gemini Gems
- Manus AI
- Kimi AI
- Perplexity AI
- Continue.dev
- VS Code AI extensions
Hands-on Lab
Automate repetitive development tasks using AI.
Session 2 — Building Intelligent Applications
- End-to-end machine learning workflow
- Model serialization with Joblib
- Creating prediction scripts
- Building simple REST APIs with FastAPI or Flask
- Integrating machine learning models into applications
- Introduction to MLOps concepts
- Responsible AI for developers
Hands-on Lab
Expose a trained machine learning model through an API and test predictions.
Session 3 — Capstone Project
Participants work in teams to build a complete AI solution, including:
- Data preparation
- Exploratory Data Analysis
- Feature engineering
- Machine learning model selection
- Model evaluation
- API integration
- AI-assisted documentation
- Presentation preparation using AI tools
Suggested project domains include finance, HR, healthcare, retail, or customer analytics.
Session 4 — Project Presentation, Future Trends & Wrap-Up
Topics
- Team project presentations
- Peer review and feedback
- Best practices for AI engineering
- Emerging trends in AI-assisted software development
- AI agents and autonomous coding
- Multimodal AI
- Retrieval-Augmented Generation (RAG)
- Agentic AI frameworks
- Personal learning roadmap and certification pathways
Dates & Locations
October 12, 2026 - October 14, 2026
October 12, 2026 - October 14, 2026
November 11, 2026 - November 13, 2026
November 11, 2026 - November 13, 2026
December 7, 2026 - December 9, 2026
December 7, 2026 - December 9, 2026
Exam & Certification
Note:Â There is no exam directly associated with this course. However, Trainocate offers an extensive portfolio of industry-recognized certifications that can help you stand out as a tech professional in 2026 and beyond. Achieving these credentials are one of the most effective ways to validate your skills and accelerate your career.
With our expert-led training, you’ll be prepared to:
- Master in-demand capabilities across Cloud, Data & AI, and Cybersecurity — areas driving global digital transformation.
- Prove your expertise with a globally respected credential recognized by employers worldwide.
- Advance your career by enhancing your credibility, increasing your earning potential, and opening doors to new opportunities.
Explore our full range of certs and start building the skills that matter today:
- Top Cybersecurity Skills for 2026
- Top Data and AI certifications for 2026
- Top Cloud Certifications for 2026
Trainocate Malaysia, based in KL Eco City, is proud to be an HRD Corp Registered Training Provider and a Yayasan Peneraju ALTI, driving workforce development across Malaysia.
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All courses are HRD Claimable.
Get in touch with our team via the form or WhatsApp us on +6011-5119 6631
