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5 Days Machine Learning Specialization

Course Information

  • Sessions 5 days
  • Duration 35 hrs
  • Level Beginner to Intermediate
  • Assessment NA

Venue

Kuala Lumpur: G-3A-02, Suite Pejabat Korporat, KL Gateway, No 2, Jalan kerinchi, Gerbang kernichi Lestari, 59200 Kuala Lumpur, Malaysia
Penang: Jalan Sungai Dua, 11700 Penang, Malaysia.

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Certification

  • Certificate of Completion from Tertiary Courses - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Completion from Tertiary Courses.

5 Days Machine Learning Specialization

Course Code: C1053
  • HRDF

7 Review(s) | Add Your Review

What's This Course About

This Machine Learning Specialization  introduces you to the exciting, high-demand field of Machine Learning. Through a series of hand on practical exercises, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, Computer Vision and Deep Learning. You will learn to analyze data and  build intelligent applications that can make predictions from data.

This five days classroom facilitator Machine Learning Specialisation course will build your fundation in Python first, then follow by classical Machine Learning using Scikit Learn, follow by Deep Learning using Tensorflow 2.x framework.

WSQ Funding

Full Fee 5,500.00 Before GST
GST 495.00 9% of fee
Baseline Nett 3,245.00 SG/PR age 21+ · 50% funded
MCES / SME Nett 2,145.00 SG age 40+ · 70% funded

Course Fee

MYR5,500.00

Course Date

Course Time

Additional Note

Please bring your own laptop for hands-on training. If you don't have laptop, we can provide spare laptop for training use.

Disclaimer: The course dates displayed on our website are tentative and subject to trainer availability. We will confirm the final date after checking with the trainer. You are also welcome to email us your preferred date at sales@tertiarycourses.com.my, and we will do our best to coordinate with the trainer's schedule.

Post-Course Support

  • We may provide consultation related to the subject matter after the course.
  • Please email your queries to sales@tertiarycourses.com.my and we will forward your queries to the subject matter experts and get back to you as soon as possible.

Cancellation & Reschedule Policy

  • We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% to participants.
  • Note: the venue of the training is subject to changes due to class size and availability of the classroom. The minimum class size to start a class is 3 Pax.

Course Details

Course Details

What You'll Learn

Day 1 Topic 1 - Python Fundamental

Topic 1.1 Get Started on Python

  • Overview of Python
  • Set Python
  • Code Your First Python Script

Topic 1.2: Data Types

  • Number
  • String
  • List
  • Tuple
  • Dictionary
  • Set

Topic 1.3 Operators

  • Arithmetic Operators
  • Compound Operators
  • Comparison Operators
  • Membership Operators
  • Logical Operators

Topic 1.4 Control Structure, Loop and Comprehension

  • Conditional
  • Loop
  • Iterating Over Multiple Sequences
  • Comprehension

Topic 1.5 Function

  • Function Syntax
  • Return Values
  • Default Arguments
  • Variable Arguments
  • Lambda, Map, Filter

Topic 1.6 Modules & Packages

  • Import Modules and Packages
  • Python Standard Packages
  • Third Party Packages

Day 2 Topic 2 - Data Analytics and Visualization with Python

Topic 2.1 Data Preparation

  • Data Analytics with Pandas
  • Pandas DataFrame and Series
  • Import and Export Data
  • Filter and Slice Data
  • Clean Data

Topic 2.2 Data Transformation

  • Join Data
  • Transform Data
  • Aggregate Data

Topic 2.3 Data Visualization

  • Data Visualization with Matplotlib and Seaborn
  • Visualize Statistical Relationships with Scatter Plot
  • Visualize Categorical Data with Bar Plot
  • Visualize Correlation with Pair Plot and Heatmap
  • Visualize Linear Relationships with Regression

Topic 2.4 Data Analysis

  • Statistical Data Analysis
  • Time Series Analysis

Topic 2.5 Advanced Data Analytics

  • Data Piping
  • Groupby and Apply Custom Functions
  • Linear Regression

Day 3 Topic 3 Machine Learning with Scikit Learn

Topic 3.1 Overview of Machine Learning and Scikit Learn

  • Introduction to Machine Learning
  • Supervised vs Unsupervised Learnings
  • Machine Learning Applications and Case Studies
  • What is Scikit Learn
  • Installing Scikit-Learn

Topic 3.2 Classification

  • What is Classification
  • Classification Algorithms
  • Classification Workflow
  • Confusion Matrix
  • Binary Classification Metrics
  • ROC and AUC

Topic 3.3 Regression

  • What is Regression?
  • Regression Algorithms
  • Regression Workflow
  • Regression Metrics
  • Overfitting and Regularizations

Topic 3.4 Clustering

  • What is Clustering
  • K-Means Clustering
  • Silhouette Analysis
  • Dendrogram and Hierarchical Clustering

Topic 3.5 Principal Component Analysis

  • Curse of Dimensionality Issue
  • What is Principal Component Analysis (PCA)
  • Feature Reduction with PCA

Day 4 Topic 4 Basic Neural Network with Tensorflow

Topic 4.1 Introduction to Deep Learning

  • Machine Learning vs Deep Learning
  • Deep Learning Methodology
  • Overview of Tensorflow Keras
  • Install and Run Tensorflow Keras
  • Basic Tensorflow Keras Operations

Topic 4.2 Neural Network for Regression

  • What is Neural Network (NN)?
  • Loss Function and Optimizer
  • Build a Neural Network Model for Regression

Topic 4.3 Neural Network for Classification

  • One Hot Encoding and SoftMax
  • Cross Entropy Loss Function
  • Build a Neural Network Model for Classification

Day 5 Topic 5 Advanced Neural Networks with Tensorflow

Topic 5.1 Convolutional Neural Network (CNN)

  • Introduction to Convolutional Neural Network?
  • ImageDataGenerator
  • Image Classification Model with CNN
  • Data Augmentation and Dropout

Topic 5.2 Transfer Learning

  • Introduction to Transfer Learning
  • Applications of Pre-Trained Models
  • Fine Tuning Pre-Trained Models

Topic 5.3 Recurrent Neural Network (RNN)

  • Introduction to Recurrent Neural Network (RNN)
  • LSTM and GRU
  • Build a RNN Model for Time Series Forecasting
  • Build a RNN Model for Sentiment Analysis

Course Info

Prerequisite

The learner must meet the minimum requirement below :

  • Read, write, speak and understand English

Target Audience

  • NSF
  • Full Time Students
  • Data Analysts

Software Requirement

This course will use Google Colab for training. Please ensure you have a Google account.

Job Roles

Job Roles

  • Data Analysts
  • Machine Learning Engineers and Developers

Trainers

Trainers

Saeid is co-founder of Skymics Sdn Bhd. He has 8 years of experience in the field of IoT (Internet of Things) and Information Technology. He is a certified IBM IoT Practitioner and instructor, and a Certified Citizen Data Scientist Train-The-Trainer. He has been co-inventor of 3 inventions during the last 4 years.

Review

Customer Reviews (7)

Good course materials Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
Good hands-on training with plenty of examples. I feel much more confident applying these skills now. (Posted on 12/05/2026)
Great practical training Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
The training was very practical and hands-on. I could apply what I learned to my work immediately. (Posted on 15/02/2026)
Practical and relevant Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
The course content was comprehensive and up to date. The practical exercises were the best part. (Posted on 27/01/2026)
Great learning experience Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
Great course materials and well-paced lessons. The exercises really helped me understand the topic. (Posted on 06/09/2025)
Very informative Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
The trainer was knowledgeable and explained the concepts clearly with real-world examples. (Posted on 02/08/2025)

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