IBFIT10: MACHINE LEARNING AND ADVANCED ANALYTICS USING PYTHON-P200511TDG
MACHINE LEARNING AND ADVANCED ANALYTICS USING PYTHON-P200511TDG
Course Duration
Mode of Assessment
Written and Practical
Who Should Attend
This particular course should be attended by those who want to learn data analytics using Python should attend this intermediate course. Applicable to students, working professionals and PMETs.
Course Overview
Programme Code: P200511TDG
Understanding and analysing data is one of the key skills required in the industry today. This course is completely focused on the various aspects of data analytics using Python. Participants will be taught to use and taken through the key libraries for data ingestion and manipulation, exploratory data analysis, model building and data visualization as well as the basic statistics knowledge required to understand the concepts in the latter courses.
Course Schedule
Next available schedule
Pre-requisites
These are the admission requirements:
- This course requires a basic understanding of Python.
- Participants who do not have the basic knowledge are encouraged to take up Basics of Python prior to this course.
Course Outline
Module 1: Introduction to Machine Learning with scikit-learn
The objective is to understand the basics of machine learning and what it means. The module also introduces the basic concepts of supervised and unsupervised machine learning and introduces a very important library used for machine learning on Python scikit-learn.
Agenda:
- Introducing the machine learning flow and concepts
- Functions within scikit-learn
- Introduction to supervised and unsupervised machine learning
Key Takeaways:
- Understand the basic concepts of scikit-learn
- Understand and learn the nuances of machine learning and the various learning types
Module 2: Unsupervised Machine Learning
This module aims to equip participants with the fundamentals of unsupervised machine learning using a very popular python library called scikit-learn. Unsupervised learning is very important across various business cases today, right from customer segmentation to property analysis.
Agenda:
- Understanding unsupervised ML algorithms
- Introduction to clustering (k-means, SOM)
- Implementing clustering with real use cases
Key Takeaways:
- Learn when to apply unsupervised learning algorithms
- Understand the nuances of how unsupervised machine learning algorithms work
Module 3: Supervised Machine Learning
Supervised machine learning is one of the most popular technique in machine learning today. This module will stress on some of the most popular algorithms in regression and classification and equip participants with an understanding of how the algorithms work and where they can be used.
Agenda:
- Introduction to various supervised learning algorithms
- Understanding feature engineering and feature sets
- Understanding and implementing
- Logistic Regression
- Support Vector Machines
- Decision Trees
- Bayesian Networks
- Implementing the above algorithms with real use cases
Key Takeaways:
- Learn how to apply supervised learning algorithms to business cases
- Learn how to code supervised learning algorithms using Python
- Learn how to test and validate machine learning models
Module 4: Evaluating machine learning models
One of the key steps in the data science lifecycle is to evaluate machine learning models to make sure the right one is selected for use in the business. Also, these models need to be trained and optimised over time. This module aims to do just that by covering the techniques aiding model selection and evaluation and optimisation.
Agenda:
- Understanding model selection and evaluation methods
- Optimise machine learning models
Key Takeaways:
- Understand the keys concepts of model evaluation and performance metrics involved to avoid unintended outcomes
- Learn to optimise machine learning models using various techniques
Certificate Obtained and Conferred by
Awarded NTUC LearningHub Certificate of Completion
Additional Details
Medium of Instruction & Trainer
Medium of Instruction: English
Trainer: Trainee ratio is 1: 20
Price
| Course commencing after 1 July 2021 | |||
| (90% funded) | |||
| Course Fee | |||
| Full Course Fee | $1,000.00 | ||
| Full Course Fee with GST | $1070.00 | ||
| Nett amount after Funding | |||
| Singapore Citizens & Permanent Residents** | Individual | $107.00* | |
| Corporate | $170.00* |
* includes 7% GST
Eligibility
Individual:
- Singapore Citizens or Singapore Permanent Residents physically based in Singapore
- Trainee has to complete 100% attendance and pass all relevant assessments and examinations
- NTUC LearningHub reserves the right to claw back the funded amount from trainee if he/she did not meet the eligibility criteria
Corporate:
- Singapore Citizens or Singapore Permanent Residents physically based in Singapore
- Company must be Financial Institutions regulated by MAS (licensed or exempted from licensing). FinTech firms must be certified by Singapore FinTech Association (SFA)
- Trainee has to complete 100% attendance and pass all relevant assessments and examinations
- Eligible company-sponsored trainees will be able to clock CPD hours upon successful course completion
Class Allowance
For all courses commencing after 1st July 2021 and before 31st December 2021 :
- Singapore Citizens and permanent residents of all ages will receive course fee subsidies of 90%
- The course fee for individuals and corporates are shown in the pricing table.
- Training Allowance – $10 per training and assessment hour (Sponsoring companies in Finance and Banking)
Terms and conditions apply. NTUC LearningHub reserve the right to make changes or improvements to any of the products described in this document without prior notice.
Prices are subject to other LHUB miscellaneous fees.
| Batch ID | Course Period | Course Title | Funding Available |
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