Applied Machine Learning
Improve your data wrangling and visualisation skills, and learn cutting-edge Machine Learning techniques
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Learn to build your own AI applications & machine learning models
AI200 teaches practical skills to apply machine learning algorithms across various industries to solve complex problems, automate processes, and provide personalised recommendations.
This course is an excellent choice for individuals interested in technology, data analysis, and problem-solving. You will gain practical skills, work on real-world projects, contribute to innovation in the field, and open up many career opportunities.
AI200 is the result of a unique blend of beginner friendliness and industry relevance – a signature characteristic of all Heicoders Academy courses. Learn Machine Learning with Heicoders Academy today to future-proof your career.
Other roles that require machine learning:
AI200 is suitable for:
- Individuals comfortable with coding in Python (covered in AI100)
- Individuals looking to apply Machine Learning in their jobs
AI200 is part of Heicoders Academy's:
Machine learning is crucial for an AI engineer because it equips them with the skills to design and implement AI systems that can learn and make predictions, which are essential for developing advanced AI applications
Machine learning enhances a Data Analyst's ability to interpret and understand data, enabling the development of algorithms and models that can make accurate predictions based on historical data
Not looking for a career switch?
Understanding machine learning can help you comprehend how decisions are made by algorithms, be aware of the social, societal, and ethical implications of machine learning, and engage with these tools effectively.
Sign up for AI200 today to learn how machine learning can help you be more productive, or contact us so we can help you decide if this is the right course for you.
The Heicoders Guarantee
Heicoders Academy is founded and run by a team of passionate technology industry practitioners who believed that their collective expertise and insights could fill the increasing technology skill gap in Singapore.
Eager to share their experiences, they set out on a mission to create a learning experience that not only addressed the shortcomings of existing courses but also aimed to inspire the next generation of technology-sector talents in Singapore.
Each Heicoders Academy course is crafted and regularly updated by an esteemed Academic Board, consisting of university professors and industry veterans with extensive experience as practitioners and hiring managers.
When you enrol in a course with Heicoders Academy, we guarantee:
A course filled with content and skills that you will find not only immediately useful but also valuable for your future endeavours
A dedicated instructor personally interviewed by the founders, someone from whom you'll be eager to learn
1-to-1 guidance and mentorship towards in-demand careers as part of our Nanodegree program (provided you put in the hard work as well!)
A course designed to accommodate and support learners who are new to the subject matter, making it easy to understand and engage with the material
Heicoders Academy’s AI200 Applied Machine Learning course in Singapore aims to equip you with a strong foundation in data wrangling and cutting-edge machine learning techniques. These skills are identified by our Academic Board as a core competency for any AI engineer, data analyst and data scientist.
You will complete the course with an exciting capstone project that addresses a given real-world prediction problem. This project will also serve as a valuable portfolio piece for you to showcase in future job applications.
3 hours each lesson
Held on Zoom
1-2 Teaching Assistants
As a Heicoders student, you will be given access to online learning materials in our e-learning portal.
To get you ready for learning, this essential pre-work will familiarise you with the basics of the key concepts and tools we will be using throughout the course.
Although you will learn these topics remotely before you arrive in class, you won’t be far away from the resources of the Heicoders community. Make use of our Telegram channel to leverage connections with students, alumni, instructors and experts. At the end of your pre-work, you’ll be ready for lessons!
After the course, you can choose to participate in follow-up sessions with your instructor, either in a group and/or individually, included as part of the course fee.
Lesson 1: Data Transformation and EDA with Pandas
- Use pandas to perform data ingestion, data merging & transformation, data cleaning and exploratory data analysis
Lesson 2: Advanced Graphical Visualisation
- Explore the use cases and implementation of advanced python visualisation libraries for feature exploration
- Implement beautiful and detail-rich visualisation dashboards that are reactive to user interactions
Lesson 3: Supervised Learning with Regression
- Understand the overview of key machine learning concepts such as: supervised learning, unsupervised learning, reinforcement learning, regression, classification and clustering
- Dive into the intuition behind regression models and their use cases
- Evaluate performance of regression models with cross-validation
Lesson 4: Supervised Learning with Classification
- Gain the intuition behind and build various machine learning classifiers such as Logistic Regression and Decision Trees
- Apply a range of commonly-used performance evaluation metrics to classification models
- Identify the difference between single- and multi-label classification, and how to tweak classification models for either task
Lesson 5: Ensemble Learning
- Improve the predictive power of machine learning models using lesser-known ensemble learning techniques
- Understand ensemble learning methods from first principles, and implement tree-based models to boost regression and classification performance
Lesson 6: Feature Engineering with Pandas
- Learn why feature engineering is crucial to successful machine learning models, and how to apply various feature engineering with pandas
Lesson 7: Unsupervised Learning with Clustering
- Understand the intuition behind clustering algorithms
- Differentiate the nuances of various clustering methods and when best to employ them for each scenario
- Explore the use cases of clustering techniques through real-life datasets
Lesson 8: Recommender Systems
- Capstone project presentation (by top 3 teams with best model)
- Understand how recommender systems work and learn how companies like Netflix and Amazon utilise these systems to improve business outcomes
- Implement a recommender system from scratch via an in-class project
Kaggle Machine Learning Prediction Competition
All AI200 graduates have the option of continuing with subsequent courses in our nanodegrees, where we prepare and equip you with the skills needed to secure prestigious jobs at top companies.
Course Fees & Subsidies
|Course Fees after Subsidy (incl. GST)
|Singaporeans aged 40 and above
|Singaporeans below 40 and all PRs
|Non-Singapore Citizens and Non-PRs
Further reduce your out-of-pocket costs with:
Upcoming Course Schedules
Select a suitable course schedule below to apply. Limited slots are available and are allotted on a first-come-first-served basis.
All instructors at Heicoders Academy have been carefully screened and selected for their excellence and passion for teaching. With their extensive industry experience, we are confident that you will gain a wealth of knowledge from them.
Data Scientist, Google
Data Scientist, ByteDance
Data Scientist, OCBC Bank
Wong Kay Jan
Senior Software Developer, GIC
Yip Jia Qi
PhD Candidate in Artificial Intelligence, NTU
Beh Min Yan
Data Scientist, Gojek
Senior Data Scientist, Grab
Senior Data Scientist, StarHub
Data Scientist, OCBC Bank
Head of Data, Gojek
Course Reviews and Testimonials
Heicoders Academy is proud to be recognised by our graduates for our ability to deliver comprehensive and practical content in a beginner-friendly manner. Embark on your Heicoders experience today and discover our dedication to delivering high-quality education.
Frequently Asked Questions
Here are the questions we receive most often:
Is machine learning very difficult?
Machine learning can be a complex and challenging field to learn, but it is not impossible. It requires a strong foundation in programming and mathematics, as well as a deep understanding of machine learning algorithms and techniques. However, with dedication, persistence, and proper guidance, anyone can learn machine learning.
Is Python enough for machine learning?
Python is one of the most popular programming languages used in machine learning due to its ease of use and versatility. It provides a wide range of libraries and frameworks that make it easier to implement machine learning algorithms. However, to become a proficient machine learning engineer, you also need to have knowledge of linear algebra, calculus, statistics, and other related fields.
How to pick a machine learning course?
How can I start a career in machine learning?
To start a career in machine learning, you need to have a strong foundation in programming and mathematics, as well as knowledge of machine learning algorithms and techniques. You can start by taking online courses, reading books and research papers, and practising on real-world problems. It is also beneficial to build a strong portfolio of machine learning projects to showcase your skills and knowledge to potential employers.
What is applied machine learning?
Applied machine learning involves using machine learning algorithms to solve real-world problems, such as predicting consumer behaviour, detecting fraud, or diagnosing medical conditions. It focuses on the practical application of machine learning techniques to specific use cases and requires a strong understanding of machine learning algorithms and programming languages.
Read our blog to learn more about what applied machine learning is and why it is important.
What are the different types of applied machine learning?
There are several types of applied machine learning, including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. In supervised learning, the model is trained on labelled data to make predictions on new data. In unsupervised learning, the model is trained on unlabeled data to identify patterns and relationships. In semi-supervised learning, the model is trained on a combination of labelled and unlabeled data. In reinforcement learning, the model learns by trial and error through interaction with an environment.
Will students be provided recorded sessions to revise the weekly lectures?
Lesson Recordings Are Provided
You are welcome to use the lesson recordings and materials for reference whenever you need a review of a particular lesson. Our Heicoders curriculum team takes great pride in documenting the class content thoroughly, so feel free to go through the lesson materials at your own pace. You could always reach out to your Teaching Assistants as well if you have questions outside of class.
I just registered for the course and am a NTUC Union member. How do I apply for the UTAP subsidy?
The UTAP subsidy is disbursed by NTUC to eligible students on a reimbursement basis. After completing the course, Heicoders students can then apply on the NTUC website for reimbursement, and upon approval they would usually receive a direct deposit from NTUC to their bank accounts within two to three weeks. You may find more details on the eligibility requirements and claims process on this NTUC UTAP website. On Heicoders’ end, we provide a PDF step-by-step guide on how to submit the claim to all students at the end of the course.
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