Course Overview
Domain
Infocomm Technology
Format
Short Course
Duration
3 days
Fee Subsidy
Up to 90% SF Funding
Learn how to apply specific verticals within Fintech in this introductory machine learning workshop.
Apply these verticals to areas such as the prediction of house prices, detection of credit card fraud, economic growth forecasting or automating loan approvals.
The theory of machine learning, as well as key accompanying concepts required for designing, implementing, evaluating, and incrementally refining machine learning solutions will be taught as well. Other topics include the classification of algorithms and time-series forecasting in a practical manner. Python’s flagship Scikit-learn machine learning library will also be introduced in this workshop.
Who Should Attend
- Best suited for people desiring modern-day skills for extracting insights from data in Fintech
- Fintech Data Analysts who desire to upskill and gain a more technical approach to working with data using industry-standard tools
- Technically-oriented professionals desiring re-skilling and potential new career paths
- Curious software developers wanting to expand their career into the Fintech space
- Professionals who want to pursue a path into machine learning for Fintech
- This course is not appropriate for those who have no prior programming experience
Prerequisites
- Comfortable in programming with any programming language
- Basic exposure to the Python programming language
- Computer literate
What You Will Learn
Topic 1
- Jupyter Notebook development environment
- How to manipulate and clean data using Pandas DataFrames
- How to perform data Input/Output operations and handle missing values with DataFrames
- How to apply functions to DataFrames as well as merge and concatenate datasets
Topic 2
- Introduction to Scikit-learn
- Supervised learning theory
- kNN classification
- Evaluating classification results
- Feature selection
- Machine learning training procedures
Topic 3
- Naïve Bayes classification algorithm
- Decision Trees
Topic 4
- Ensemble-based machine learning
- Random Forest algorithm
Teaching Team
Soh Cheng Lock, Donny
Associate Professor / Prog Leader, Infocomm Technology, Singapore Institute of Technology
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Teo Susnjak
Senior Lecturer, Information Technology, Massey University, New Zealand
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Schedule
Day 1
| Topic | Topic Description |
|---|---|
| Machine Learning Toolkit | A data analyst is only as good as his tools. We introduce you first to Jupyter Notebooks and Python’s Pandas library, which are arguably the best set of tools available for machine learning specialists. |
| Theory of Machine Learning | Machine learning is exciting because it can transform a small amount of input knowledge into a large amount of output knowledge. It isn’t magic, but it can give us a lot for very little. Here we cover what is the actual goal of machine learning and what are some of its pitfalls. |
| First Classification Algorithm | There are machine learning algorithms which mimic intuitive decision-making processes that humans rely on. kNN algorithm is one of those and is a good starting point. We use the challenge of fraud detection as an illustrative example together with Python’s flagship machine learning library: sci-kit learn. |
Day 2
| Topic | Topic Description |
|---|---|
| Assessing Results | Generating machine learning models is typically not difficult. But how good are they? Are they really doing their job? Evaluating the performance of models is an indispensable part of the machine learning and there numerous perspectives to achieve this which will be covered here. |
| Training Classifiers | Robust training strategies are needed to more reliably estimate how well our models will perform once deployed. We cover strategies for creating training, validation and test data sets, as well as designing experiments using cross-fold validation. |
| Refining Classifiers | Machine learning algorithms always find patterns. However, not all patterns are meaningful and some are just hallucinations. We explore methods to reduce this problematic phenomenon called overfitting through techniques like feature selection using loan default risk data as an illustration. |
Day 3
| Topic | Topic Description |
|---|---|
| Expanding the Machine Learning Toolkit | No single machine learning algorithm will always outperform the others. We introduce you to Naïve Bayes, which is often used as a benchmark when selecting and refining solutions. Most algorithms produce models which are not interpretable and being able to make sense of models is becoming increasingly important. Decision Trees possess this quality and serve as a valuable tool on many domains. |
| Wisdom of Crowds | It has been said that all models are wrong, but some are useful. Sometimes putting together all the ‘wrong’ models creates a solution that is more ‘right’. The family of ensemble-based machine learning methods attempts to achieve exactly this. We cover one of the most popular and successful algorithms in this category called the Random Forest classifiers and apply it to data from the financial sector. |
Certificate and Assessment
A Certificate of Participation will be issued to participants who
- Attend at least 75% of the course
- Undertake and pass non-credit bearing assessment during the course
Fee Structure
The full fee for this course is S$2,616.00.
| Category | After SF Funding |
|---|---|
| Singapore Citizen (Below 40) | S$784.80 |
| Singapore Citizen (40 & Above) | S$304.80 |
| Singapore PR / LTVP+ Holder | S$784.80 |
| Non-Singapore Citizen | S$2,616.00 (No Funding) |
Note: All fees above include GST. GST applies to individuals and Singapore-registered companies.
Course Runs
Frequently Asked Questions
-
What coding skills do I need for this course?
You need to have at least some basic programming skills in any programming language.
-
I’ve never programmed before and I really want to take this course, what should I do?
You can take the Introduction to Python Programming short course which prepares you for this one.
-
I am an experienced software developer but I have not worked with Python before, can I still attend this course?
You should be fine to attend this course but it would be helpful if you looked over some of the Python fundamentals just before the course begins.
-
Will I be able to regard myself as a software engineer after this course?
No. This course does not teach you how to write application software, but will instead develop your skills in scripting.
-
I know how to use Python 2 but not Python 3. Does this matter?
Not at all. There are some minor differences which you will pick up quickly as needed during the course.
-
Which development environment will we use so that I can set this up on my personal machine?
Anaconda3-2018-12 version.
-
Will I have to learn how to write large programs in this course?
No. This course focuses on writing small snippets of code which provide you with immediate feedback.
-
Will this course cover topics like object-oriented programming?
You will be using objects all thought the course, but we will not be covering OO principles, nor will we be learning to write Python classes in this course. The focus is on leveraging parts of Python that are most relevant to data science and not end-product development.
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