This course provides practical knowledge of Python programming, data analysis, machine learning, and AI-assisted software development using tools such as GitHub Copilot, ChatGPT, Claude, Gemini, Cursor AI, Windsurf, Google AI Studio, and JupyterLab.

Why get trained: Learn how to build and evaluate machine learning models, analyze data, automate development tasks, leverage AI coding assistants, and develop end-to-end AI-powered applications.

Why it matters: AI and Machine Learning are becoming essential skills for modern developers. Professionals who can combine software engineering with AI tools can build smarter applications, improve code quality, and accelerate development.

Who should attend: Software Developers, Python Developers, AI Engineers, Machine Learning Engineers, Data Analysts, Data Scientists, Full Stack Developers, Backend Developers, DevOps Engineers, Automation Engineers, and IT Professionals interested in AI.

Gain the skills to build intelligent applications and integrate AI into modern software development workflows. HRD Corp Claimable.

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:

  1. Understand AI, Machine Learning, and modern AI engineering concepts.
  2. Develop Python programs for data processing and machine learning.
  3. Perform data wrangling and exploratory data analysis (EDA).
  4. Build supervised and unsupervised machine learning models.
  5. Evaluate and improve machine learning model performance.
  6. Apply AI-assisted software development using modern coding assistants.
  7. Automate software development tasks using AI tools.
  8. 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

Course Curriculum

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

Let’s make it work for you

Can’t find a date that fits? Need to train your whole team? Looking for a discount?
Speak to one of our learning experts today.

There’s no intakes scheduled for this course at the moment!

For enquiries, please contact our reps.

Exam & Certification

Note: 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:

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.

Training & Certification Guide

Why train with Trainocate

Programming

  • Python 3
  • JupyterLab
  • Visual Studio Code
  • Git

Python Libraries

  • NumPy
  • Pandas
  • Matplotlib
  • Plotly
  • Scikit-learn
  • Joblib
  • Flask or FastAPI

AI Productivity & Automation Tools

  • ChatGPT
  • ChatGPT Projects
  • GitHub Copilot
  • Claude
  • Claude Artifacts
  • Google AI Studio
  • Gemini
  • Gemini Gems
  • Cursor AI
  • Windsurf
  • Continue.dev
  • Perplexity AI
  • Manus AI
  • Kimi AI
  • NotebookLM
  • GitHub

Participants will complete a short assessment to evaluate their:

  • Python programming knowledge
  • Understanding of AI and machine learning concepts
  • Familiarity with development tools and workflows
  • Experience using AI-assisted coding tools

Participants will:

  • Complete a practical coding and machine learning assessment.
  • Develop and present a complete AI-enabled Python application using machine learning.
  • Demonstrate effective use of AI productivity tools for coding, testing, debugging, documentation, and automation.
  • Receive instructor feedback and recommendations for continued learning and professional development.

Speak to a Training Consultant

All courses are HRD Claimable.
Get in touch with our team via the form or WhatsApp us on +6011-5119 6631

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