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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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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

105 Review(s) | Add Your Review

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.

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

Promotion Code

Your will get 10% discount voucher for 2nd course onwards if you write us a Google review.

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 can download and install the following software:

Hardware: Windows and Mac Laptops

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

Richard Wan is an ACLP-certified lecturer and software consultant with over 40 years of experience in software and hardware development, spanning AI, computer vision, and machine learning. He began his programming career with 8-bit computing in the late 1970s and went on to earn his M.Sc. in Electrical Engineering (Computer Vision) from the University of Wisconsin–Madison. His professional contributions include co-founding multiple high-tech companies, pioneering digital publishing technologies, and leading AI-driven software development in healthcare, defense, and manufacturing. Richard has taught a wide range of technical courses, including machine learning with Scikit-Learn, deep learning with TensorFlow and PyTorch, and computer vision with OpenCV. In predictive analytics, he emphasizes the use of PyTorch for building deep learning models that can forecast trends, detect anomalies, and classify outcomes. His teaching approach blends decades of hands-on development with structured, beginner-friendly instruction, equipping learners with practical skills to transform data into prediction.

Dr Alvin Ang is a ACTA certified trainer. Alvin Ang did his Ph.D., Masters and Bachelors from NTU, Singapore. Previously he was a Principal Consultant (Data Science) as well as an Assistant Professor. He was also 8 years SUSS adjunct lecturer. His focus and interest is in the area of real world data science. Though an operational researcher by study, his passion for practical applications outweigh his academic background. He owns a startup externally

CY Quah is an ACLP-certified trainer and data science professional with extensive experience in Python, NLP, and machine learning. He has led AI training programs for SAP, Temasek Polytechnic, and IMDA under the SGUnited Mid-Career Pathways initiative, and has delivered corporate workshops on text analytics, recommender systems, and chatbot development. His expertise includes applying NLP tools such as NLTK, spaCy, and Gensim for sentiment analysis, topic modeling, and text classification.

Solomon Soh is an experienced Data Scientist and AI Trainer with a strong record of teaching and mentoring in Python programming, data analytics, and machine learning. Currently a Data Science Trainer with IBM Singapore, he has coached teams on projects involving natural language processing, computer vision, and chatbots, achieving a 96% learner satisfaction rating for his communication and technical expertise. His career spans roles at Workforce Optimizer, Certis Cisco, Ernst & Young, and IQVIA, where he applied Python-driven analytics to improve operations, optimize staffing, and deliver actionable insights. His academic background includes a double degree in Economics and Psychology from Singapore Management University (Summa Cum Laude, triple major in Analytics), an MBA, and a Master’s in Financial Engineering.

Review

Customer Reviews (105)

Might Recommend Review by Course Participant/Trainee

1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment

Nil (Posted on 4/29/2018)

Might Recommend Review by Course Participant/Trainee

1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment

Nil (Posted on 4/19/2018)

Might Recommend Review by Course Participant/Trainee

1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment

Get a larger training venue or limit the number of people in the course! (Posted on 3/29/2018)

Might Recommend Review by Course Participant/Trainee

1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment

More emphasis of why it works than how it works (Posted on 3/21/2018)

Will Recommend Review by Course Participant/Trainee

1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment

Probably to include/cover more on Keras examples and adding exercises on transfer learning (e.g. retrain the top few layers and freeze the rest) just as using image/speech data. Would be good to cover a bit on using the TensorFlow GPU version to run real application problems, giving the students a glimpse of how to start if using the GPU to run. (Posted on 3/21/2018)