[Online NEW] BCS Foundation Award in Machine Learning
Course Information
  • 18-Nov-2021
  • Online
  • BCS Certified Trainer
  • English
  • RM 2500 RM 1900
    Until 01 Nov 2021
  • 9:00am - 5:00pm
  • Contact Hours: 14
  • Self-Learning Hours: 35
  • Duration: 2 Days
  • SFIAplus Level: Level III


The term “Machine Learning” has increased in popularity in the last decade and is a technology which is becoming more commonly used within many organisations. With its ability to help solve business problems and develop new customer experiences, there is now a greater demand for individuals with the knowledge and skills to support organisations to successfully implement the technology to deliver improvements.

This award explores what Machine Learning is and how it is used in practice. It provides an introduction into the different types of Machine Leaning and the tools and techniques required to develop it, including a basic introduction to algorithms. This award will enable candidates to understand these concepts at a foundation level, enabling them to be better informed and equipping them with knowledge which they can build upon through further study and application.



Upon completion of the award, candidates will be able to demonstrate:

1. An understanding of the basic principles of machine learning
2. A basic understanding of the use of coding languages and software used in Machine Learning

3. An understanding of the different types of algorithms used in machine learning
4. An understanding of the key stages within the Machine Learning process



The BCS Foundation Award in Machine Learning is designed for individuals wishing to gain an understanding of the principles of Machine Learning and the process through which it can be developed.

The following roles could be interested:

• Engineers

• Scientists

• Professional research managers

• Chief technical officers

• Chief information officers

• Organizational change practitioners and managers

• Business change practitioners and managers

• Service architects and managers

• Program and planning managers

• Service provider portfolio strategists / leads

• Process architects and managers

• Business strategists and consultants

• Web page developers



Successful completion of the BCS Foundation Award in Machine Learning exam

This award represents 5 credits that can count towards the credits required for a BCS Foundation Certificate or Diploma in a relevant discipline.



- Examination type: Multiple Choice Questions

- Number of questions: 16 Multiple Choice questions, 2 Scenario Based Questions

- Pass mark: 13 / 20 (65%)
- Open book/notes: No

- Electronic equipment/ aides permitted: No

- Exam duration: 30 minutes




Contact Hours

• 14 hours. This includes training lecture, group assignments, exam preparation, sample assessment review and short breaks.


Self-Study Hours

• Recommended 35 hours, depending on existing knowledge.



Date: 18 – 19 November 2021 (Thu - Fri)

Time: 9.00am - 5.00pm

Mode: Online/ Interactive

[HRDF Claimable - SBL KHAS]




- Early Bird Offer (1 Nov 2021) – RM 1,900

- Special Offer (before 14 Nov 2021) - RM 2,200

- Normal Fee - RM 2,500


>> Fee is inclusive of Course Notes, Exercises, BCS Certification Exam, BCS Remote Proctoring, Certificate of Completion & 6% Service Tax


** OFFER ** 

- 10% Cashback for SST2u Learner Card+ (SLC+) members. T&Cs applied.




Day 1


1. What is Machine Learning?

1.1 Define Machine Learning

a. Machine Learning a subset of AI

b. “Learning from experience”

c. Tom Mitchell – definition  

d. Requirement for talent for learning/mathematics (i.e. Data Scientist)

e. Application of algorithms to given data to derive insight


1.2 Explain different applications of Machine Learning

a. Prediction

b. Object recognition

c. Classification

d. Clustering

e. Recommendations (e.g. Netflix, Spotify)


1.3 Describe the role of a Learning Agent

a. Data
b. Single task
c. Learning from experience


1.4 Explain the concept of Deep Learning

a. Universal technique to solve a larger set of problems

b. Neural Networks combined with large data sets


1.5 Describe the purpose of a Neural Network

a. Input > Identify patterns in data > Output

b. Decision Making


1.6 Illustrate how Machine Learning compliments Knowledge-Based Systems

a. Knowledge-Based Systems

b. Complimentary AI technologies


1.7 Explain the process through which Machine Learning works with Data.

a. The Machine Learning process

b. Analyse the problem

c. Data Selection

d. Data Pre-processing

   - Cleaning

   - Integration

   - Transformation

   - Reduction

   - Wrangling

e. Data Visualisation

f Select a Machine Learning model (algorithm)

   - Train the model

   - Test the model

   - Repeat(Learning from experience to improve results)

g. Review

   - Peer review

   - Learning from multiple algorithms

   - Identify best Machine Learning model


2. Coding for Machine Learning


2.1 Explain the use of at least one coding language used in machine learning

a. Object-oriented programming languages

   - Python

   - R
   - C++
   - Java

b. Libraries/ templates


2.2 Identify common open source and proprietary software used in coding for Machine Learning

a. Tensorflow

b. R Studio
c. Cuda
d. Scikit-Learn



Day 2


3. Algorithms Used in Machine Learning


3.1 Explain the use of mathematics in enabling a machine to solve numerical problems

a. Probability (Bayes Theorem)

b. Statistics

   - Descriptive Statistics

   - Inferential Statistics

c. Linear Algebra


3.2 List and describe typical algorithms used in Machine Learning

a. Regression algorithms (e.g.)

   - Linear regression
   - Polynomial Regression

b. Classification algorithms (e.g.)

   - K-Nearest Neighbours

   - Decision Trees

   - Logistic Regression

c. Clustering algorithms (e.g.)

   - K-means

   - Hierarchical


3.3 Describe Supervised, Unsupervised and Semi-Supervised learning

a. Supervised learning

b. Unsupervised learning

c. Semi-Supervised learning


4. Machine Learning in Practice


4.1 Describe a particular problem that can be addressed through the use of Machine Learning

a. Problem identification

b. Requirements for data collection

c. Proposing the Machine Learning solution


4.2 Outline typical tasks required in the preparation of data for developing a particular application of Machine Learning

a. Data Pre-processing
b. Data Transformation
c. Importing/loading data


4.3 Explain the process of training a Machine Learning model

a. Requirements for training

b. Setting up training bins for data
c. Selecting an algorithm Rules
d. Supervised, Unsupervised,

e. Semi-supervised


4.4 Explain the process of testing a Machine Learning model

a. Testing
b. Tuning
c. Ensembles
d. Statistical testing

e. Review


4.5 Discuss how to evaluate the results of testing in order to identify the information to be shared

a. Evaluating findings

b. Identifying relevant

   - information for your stakeholders/context

   - What have we learned?

   - Have we been able to address the problem?

   - What next?

   - Learning from experience

c. Drawing conclusions

d. Communication techniques/ methods



BCS, The Chartered Institute for IT


BCS, The Chartered Institute for IT,  is a professional body that represents those working in information technology (IT) and computer science, both in the United Kingdom and internationally.  It has over 60,000 members in 150 countries and its professional qualifications are recognized globally.



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- Fee inclusive of Exam Voucher, Review of Sample Papers

- Wide selection of training delivery methods



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