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AI Vibe Coding for Machine Learning

Sessions 2 days
Duration 15 hrs
Level Intermediate
Assessment NA

Venue

12 Woodlands Square #07-85/86/87 Woods Square Tower 1, Singapore 737715. 5 mins walk from Woodlands (NS9) MRT station.

The venue is disabled-friendly.

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This course is for beginners. No prerequisite is required.

Certificate of Achievement from Tertiary Infotech Academy Pte Ltd - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Achievement from Tertiary Infotech Academy Pte Ltd.

AI Vibe Coding for Machine Learning

Course Code: C430

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AI Vibe Coding for Machine Learning shows you how to build, train, evaluate and deploy machine learning models by directing AI coding assistants such as Cursor, GitHub Copilot and Claude. Instead of memorising every library function, you describe your goal in plain English, review the Python code the AI writes and guide it until your model performs well. The course is designed for analysts, developers, engineers and data enthusiasts who want to apply machine learning to real problems faster and with confidence.

You will start by getting started with AI vibe coding for machine learning, setting up your environment and learning how to prompt an AI assistant to load, explore and prepare data with pandas and scikit-learn. Next you will build and train models with AI, covering regression, classification and clustering, feature engineering and train-test splits, and introducing neural networks with TensorFlow or PyTorch. You will then evaluate and improve your models, using appropriate metrics, cross-validation, hyperparameter tuning and techniques to reduce overfitting.

The final topic covers deploying machine learning with AI, packaging your model, serving predictions through a simple API or web app and planning for monitoring and retraining. Throughout the course you will learn to check AI-generated code carefully, understand what each step does and avoid common pitfalls such as data leakage. All exercises are hands-on with real datasets. By the end, you will be able to use AI vibe coding to deliver working machine learning solutions from data to deployment.

This course is part of our AI training in Singapore, where you can browse all our AI courses. See more AI vibe coding courses.

Hands-On Learning Learn by doing. Every topic is practised hands-on, so you leave with skills you can use at work straight away.
Live Coaching Experienced instructors coach you live, answer your questions and guide you step by step.
Free Post-Course Consultation Stuck after class? Message your trainer any time for guidance as you apply what you have learned.
Always-Updated Materials Materials are refreshed to stay relevant. Log in to our LMS any time to get the latest version.

No funding is available for this course

For WSQ funding, please checkout the details at WSQ - AI Vibe Coding for Machine Learning

Course Fee

$700.00 (GST-exclusive)

$763.00 (GST-inclusive)

Mode of Training

Physical Classroom Training
Synchronised Teaching using ZOOM
Corporate Training at Company Premise

Course Date * Course Date

Course Time * Course Time

9:30am - 5:30pm

No. of Participants

Sponsorship

Self-Sponsored
Employer-Sponsored

Name(s) on Certificate *

Additional Message or Emails for Course Notification

* Required Fields

Please bring your own laptop for hands-on training. If you don't have laptop, we can provide spare laptop for training use.

We provide free consultation related to the subject matter after the course.
Please email your queries to enquiry@tertiaryinfotech.com and we will forward your queries to the subject matter experts.

You can register your interest without upfront payment. There is no penalty for withdrawal of the course before the class commences.
We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% for any paid amount.
Note the venue of the training is subject to changes due to availability of the classroom.

Course Details

Topic 1: AI Vibe Coding and Machine Learning Fundamentals

Topic 2: Data Classification and Performance Evaluation

Topic 3: Regression Analysis and Predictive Modelling

Topic 4: Clustering and Customer or Data Segmentation

Topic 5: Principal Component Analysis and Dimensionality Reduction

Course Info

Minimum Entry Requirement

Knowledge and Skills

Able to operate using computer functions
Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)

Attitude

Positive Learning Attitude
Enthusiastic Learner

Experience

Minimum of 1 year of working experience.

Target Age Group: 21-65 years old

Minimum Software/Hardware Requirement

Software:

You will need the following for this course:

Python 3 – free
Visual Studio Code – free
Google Colab – free, runs in the browser with a Google account
An AI coding assistant with a free tier, e.g. GitHub Copilot Free in Visual Studio Code

Hardware: Windows PC or Mac laptop

Job Roles

Machine Learning Engineer
Data Scientist
Deep Learning Researcher
AI Developer
Neural Network Designer
Computer Vision Engineer
NLP Engineer (branching into deep learning)
AI Product Manager (technical understanding)
Robotics Engineer (with AI components)
Bioinformatics Scientist (deep learning applications)
Medical Imaging Specialist (AI-focused)
Game Developer (AI-driven features)
Predictive Analytics Specialist
AI/ML Educator or Trainer
Autonomous Systems Developer.

Trainers

Dr. Alfred Ang writes much of his own software today by directing AI coding assistants, and he teaches machine learning the same way. He is a full-stack AI developer, Agentic AI solution architect, courseware developer and corporate trainer, and serves as Managing Director, CIO, CTO and Chief Instructional Designer of Tertiary Infotech Academy Pte Ltd, having founded Tertiary Courses. His machine learning credentials are substantial: he is a Microsoft Certified Data Scientist (DP-100) and AI Engineer Associate (AI-102), an AWS Certified AI Practitioner, and an IBM certified instructor for the AI Practitioners course. He holds a PhD in Electrical Engineering from NUS, an MSc in Electrical and Electronic Engineering from NTU, a B.Sc (Hons) in Physics from NUS and a Graduate Diploma in Intelligent Robotics Systems from NUS-ISS, and he has published more than 20 peer-reviewed papers. On GitHub, where he maintains more than 300 public repositories, his data-mining-ml-labs project offers interactive Python and Streamlit labs for beginner data mining and machine learning. He has also published a collection of Claude Code skills, including an app launcher and a README generator, and built an n8n inventory agent in which a local LLM flags items under the reorder threshold and computes replenishment. His experience spans the defence, electronics and semiconductor industries, which gives him a practical feel for how models behave on real, imperfect data. He believes the future belongs to professionals who can build, deploy and govern intelligent systems responsibly, and he designs his machine learning courses with exactly that outcome in mind.

In AI Vibe Coding for Machine Learning, Dr. Ang shows learners how to build, train, evaluate and deploy models by describing goals in plain English and guiding assistants such as Cursor, GitHub Copilot and Claude. The course opens with vibe coding and machine learning fundamentals, so learners understand the workflow they are directing before the AI writes any Python. They then tackle classification and performance evaluation, learning to read accuracy, precision, recall and confusion matrices rather than accepting whatever the assistant reports. Regression and predictive modelling follow, then clustering for customer and data segmentation, and finally principal component analysis for dimensionality reduction. At every stage he insists that learners review the generated code, ask the AI to explain its choices and test the results, because a confident assistant can still leak data, mis-scale features or pick the wrong metric. His data science background lets him spot these mistakes quickly and turn them into teaching moments. The style is hands-on and incremental: short demonstrations, then guided labs in which learners prompt, inspect, run and refine their own notebooks until the model performs. Professionals with little coding experience leave able to produce working machine learning projects, and experienced coders leave significantly faster. He also explains when a simpler model is the better answer, and how to present results to non-technical stakeholders in a way that builds trust in the analysis. To explore his machine learning labs and AI coding projects, view his GitHub portfolio at https://github.com/alfredang and connect with him on LinkedIn at https://www.linkedin.com/in/angchewhoe/

Solomon Soh is a data scientist and AI trainer specialising in Python programming, data analytics and machine learning. As a Data Science Trainer with IBM Singapore, he has coached teams on natural language processing, computer vision and chatbot projects, earning a 96% learner satisfaction rating. A Certified AI Engineer Associate by AI Singapore, he has also applied Python-driven analytics at Workforce Optimizer, Certis Cisco, Ernst & Young and IQVIA to improve operations and optimise staffing. He holds a double degree in Economics and Psychology from Singapore Management University (Summa Cum Laude, with a triple major in Analytics), an MBA and a Master's in Financial Engineering.

AI Vibe Coding for Machine Learning maps directly onto his day-to-day expertise. Learners direct AI coding assistants such as Cursor, GitHub Copilot and Claude to build and evaluate models, covering machine learning fundamentals, data classification and performance evaluation, regression and predictive modelling, clustering for customer or data segmentation, and principal component analysis for dimensionality reduction. Solomon's industry experience helps learners connect each technique to real business questions and judge model quality with the right metrics. Participants leave able to describe a modelling task in plain English, have an assistant implement it, and evaluate the results with confidence.

Richard Wan is an ACLP-certified lecturer and software consultant specialising in machine learning, artificial intelligence and computer vision, with decades of experience in software and hardware development. He has taught machine learning with Scikit-Learn, deep learning with TensorFlow and PyTorch, and computer vision with OpenCV, and has led AI-driven software development in healthcare, defence and manufacturing. He holds an M.Sc. in Electrical Engineering (Computer Vision) from the University of Wisconsin-Madison, began programming on 8-bit computers in the late 1970s, and has co-founded multiple high-tech companies.

In AI Vibe Coding for Machine Learning, Richard pairs his hands-on machine learning teaching with AI coding assistants such as Cursor, GitHub Copilot and Claude. Learners cover vibe coding and machine learning fundamentals, then build and evaluate classification models, develop regression and predictive models, segment customers or data with clustering, and reduce dimensionality with principal component analysis. Because he understands the algorithms behind the code, he helps participants describe goals in plain English, review the Python the AI writes and check whether a model genuinely performs. His structured, beginner-friendly style equips learners with practical skills to transform data into prediction.

Quah Chee Yong is an ACLP-certified trainer and data science professional with extensive experience in Python, natural language processing (NLP) and machine learning. He has led AI training programmes for SAP, Temasek Polytechnic and IMDA under the SGUnited Mid-Career Pathways initiative. His corporate workshops have covered text analytics, recommender systems and chatbot development, and he applies NLP tools such as NLTK, spaCy and Gensim to sentiment analysis, topic modelling and text classification. He has built his skills across data science, machine learning, deep learning and cloud deployment, and brings a broad view from technical, operations and commercial roles in several industries. He is also a Singapore WSQ-certified Adult Educator.

AI Vibe Coding for Machine Learning draws directly on this background. He guides learners in directing AI coding assistants to write Python machine learning code, then reviewing and refining what the assistant produces until the model performs. Across the course, participants build and evaluate classification models, carry out regression analysis and predictive modelling, segment customers or data with clustering, and reduce dimensionality with principal component analysis. His emphasis on understanding why a model works, not only getting code to run, helps learners interpret performance metrics sensibly and leave with a repeatable workflow for practical machine learning projects.

Dr Alvin Ang is an ACTA-certified trainer whose focus is real-world data science. He holds a PhD, Masters and Bachelors from NTU, Singapore, and has worked as a Principal Consultant (Data Science) and an Assistant Professor, in addition to eight years as an adjunct lecturer at SUSS. An operational researcher by study, he places practical application ahead of theory, and he also owns a startup.

In AI Vibe Coding for Machine Learning, Dr Ang brings his data science consulting and university teaching experience to a modern, assistant-led way of building models. Learners direct AI coding assistants such as Cursor, GitHub Copilot and Claude through the fundamentals of machine learning, then data classification and performance evaluation, regression analysis and predictive modelling, clustering for customer or data segmentation, and principal component analysis for dimensionality reduction. His emphasis is on understanding what each model is doing and how to evaluate it, so that learners can spot weak results instead of trusting generated code at face value. Participants leave able to describe a modelling task in plain English, guide an assistant to build and test it, and interpret the outcome with confidence.