Machine Learning using Python Course in Singapore
Machine learning is how software gets better at making predictions by studying patterns in historical data, and Python is the programming language used to build and run those models. Machine Learning with Python Course Singapore is a two day classroom training where participants learn how to write machine learning algorithms in Python, covering data preparation, supervised learning, unsupervised learning, model evaluation, and optimisation using the scikit-learn library. From understanding how ml algorithms work to building and testing your own models, the python machine learning training gives you a hands on and structured way to work with machine learning using Python code. A background in Python programming and basic data analytics knowledge are required before joining this ai course Singapore.
Schedule
- Days: 2 Days
- Duration: 16 Hours
- Timings: 9:30 AM - 5:30 PM
- 10 Anson Road, 26-08A International Plaza, Singapore 079903
Why Choose Us
- Expert Local Trainers
- Hands-on Training by experts
- Certificate of Achievement
Who Should Attend This Machine learning with python Course
Anyone who wants to learn how to build and apply machine learning models using Python will find this machine learning course Singapore a practical and well structured starting point. A background in Python programming is required before joining.
Business professionals
Analyst
Entrepreneur
Training Professionals
Student
Course Completion Requirements
Minimum 75% attendance is required in order to complete the course and receive the course completion certificate
Above 16 years old
High School Level English
Basic internet & computer operation skills
About the Course
Machine Learning with Python Course Singapore is an advanced level course that teaches participants how to write algorithms and code in Python to perform complex data analysis. Across two days of hands on classroom training, participants work through the full machine learning workflow from data preparation and model building to evaluation and optimisation using the scikit-learn library. Every topic is covered practically so participants can apply what they learn directly during the session.
What You Will Learn in This Machine Learning Course Singapore
By completing this data science course Singapore, participants will be able to:
- Understand the core concepts of scikit-learn and how it fits into the machine learning workflow
- Learn how machine learning algorithms work and when to apply them to different data problems
- Identify when unsupervised learning is the right approach and apply it accurately
- Understand how multiple machine learning algorithms function and how they differ from each other
- Apply supervised learning algorithms to data analysis problems using Python
- Write and program supervised learning algorithms in Python from scratch
- Build, test, and validate machine learning models to measure their accuracy and performance
- Work with model evaluation metrics and understand what each metric measures
- Optimise machine learning models using different techniques to improve prediction accuracy
- Work with advanced machine learning algorithms covered across the two day ml certification Singapore training
Begin your Machine Learning career with us
Data Analytics knowledge is compulsory, and recommended to join the basic Python course before joining this course.
What You’ll Learn
Machine Learning with Python is an advanced-level course that teaches you how to write algorithms and code in Python to perform complex analytics. You’ll learn to build, train, and evaluate a variety of machine learning models to make predictions and generate actionable insights.
Course Prerequisites
Before joining this Machine Learning with Python Course Singapore, participants should have a solid background in Python programming. Data analytics knowledge is required before attending this course. Joining a foundational Python course before this python machine learning training will help participants follow the course material at a comfortable pace and get more out of each session across both days.
About the Machine Learning with Python Course
Scikit-learn is one of the most widely used Python libraries for machine learning and is the core library used throughout this course. Participants use scikit-learn across both days to work with supervised and unsupervised learning algorithms, build predictive models, and measure how well those models perform on data.
Supervised learning covers algorithms that learn from labelled data to make predictions, including regression and classification techniques that are applied across different data analysis problems. Unsupervised learning covers algorithms that find patterns in data without labelled inputs, including clustering techniques like k-means and SOM that group data points based on similarity.
Beyond building models, the ai course Singapore also covers the full machine learning workflow including data preparation, feature engineering, model selection, evaluation metrics, and optimisation techniques. Understanding each stage of the workflow gives participants a complete picture of how machine learning projects are structured from start to finish. For anyone looking to work with Python machine learning tools at an advanced level, this ml certification Singapore course covers the right depth across two focused classroom days.
What You Can Do After This Training
After completing the Machine Learning with Python Course Singapore, you will be able to:
- Write and run machine learning algorithms in Python to analyse and work with data
- Build, train, and evaluate machine learning models that make predictions from data
- Apply supervised learning algorithms including regression and classification to data problems
- Work with unsupervised learning algorithms and apply clustering techniques like k-means and SOM
- Use scikit-learn confidently to build and manage machine learning workflows
- Apply feature engineering techniques to prepare and improve data before model training
- Select the right machine learning model for different data scenarios and requirements
- Measure model performance using evaluation metrics and understand what the results mean
- Optimise machine learning models using different techniques to improve prediction accuracy
- Work through the full machine learning workflow from data preparation to model evaluation independently
Machine Learning using Python Course Outline
Module 1
The main objective of this module is to learn the basics of Data Preparation and understand the essentials of machine learning in terms of functional & non-functional processes.
- Restoring the essential techniques in python as an essential to machine learning
Module 2
The main goal of this module is to learn the fundamentals of machine learning with Sckikit-learn. Also, you can learn important libraries of machine learning in this module.
- Understand the machine learning flow and concepts
- Learn functions within scikit-learn
- Prologue to supervised and unsupervised machine learning
Module 3
The main objective of this module is to learn Unsupervised Machine Learning. It uses a very famous python library known as scikit-learn. Unsupervised learning is very important in different business cases today, right from client division to property analysis.
- Learn unsupervised ML algorithms
- Understand clustering (k-means, SOM)
- Executing clustering with real use cases
Module 4
The main objective of this module is to learn Supervised Machine Learning. Unde Supervised Machine Learning Supervised machine learning is one of the most popular techniques in machine learning today. This module will focus on some of the most famous algorithms in regression and classification and equip learners with an understanding of how the algorithms execute.
- Learn various supervised learning algorithms
- Learn feature engineering and feature sets
- Execute numerous Supervised ML algorithms with real use cases
Module 5
The main goal of this module is to learn machine learning models and data science lifecycle. Also, you will learn model selection, evaluation, and optimisation.
- Learn model selection and evaluation methods
- Learn how to optimize machine learning models
Frequently Asked Questions (FAQs)
Who should consider taking this course?
This advanced-level training is perfect for professionals like analytics managers, developers, business analysts, information architects, and graduates looking to build a career in machine learning.
What prerequisites are required?
How long is the course and what does it cover?
This is a two-day intensive course. You’ll begin with data preparation fundamentals, then dive into both supervised and unsupervised learning using Python’s Scikit-Learn library. You’ll also learn how to evaluate and validate your machine learning models
Are there any certification or attendance requirements?
Yes—participants must attend at least 75% of the sessions to receive the course completion certificate