Call +60 3-7490 2093 Email: sales@tertiarycourses.com.my

HRDF Approved Training Provider in Malaysia - Modular Fast Track Skill-Based Trainings

Solving Problems with Machine Learning

Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine Learning algorithms comb through data and identify patterns that are too complex to be discerned by the human mind. These patterns can then be used for decision making and action.

In this course we will use a Graphical User Interface (GUI) based Machine Learning tool (this course does not require any programming). We will work through examples and understand the the top regression, classification and ensemble machine learning algorithms.

We will also understand the process of problem solving through machine learning - including problem definition framework, data preparation, algorithm spot check, fine-tuning results and presenting outcome.

Certificate

All participants will receive a Certificate of Completion from Tertiary Courses after achieved at least 75% attendance.

HRDF SBL Claimable for Employers Registered with HRDF

HRDF claimable

Course Code: M454

Course Booking

MYR880.00

Course Date

Course Time

* Required Fields

Course 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.
Note the minimal class size to start a class is 3 Pax.


Course Details

Topic 1 - Introduction to Machine Learning

  • What is Machine Learning 
  • Machine Learning in Real life 
  • Types of Machine Learning 
  • Key ML Models
  • Installing Weka
  • Load Dataset to Weka
  • Build Your First Classifier

Topic 2 - Classification

  • What is Classification?
  • K-Nearest Neighbours (KNN)
  • Support Vector Machine (SVM)
  • Naive Bayes
  • Decision Tree (DT)


Topic 3 - Regression

  • What is Regression?
  • Linear Regression
  • Support Vector Regression
  • K-Nearest Neighbour Regression

Topic 4 - Ensemble Methods

  • What is Ensemble Methods?
  • Bagging
  • Random Forest
  • Stacking

Topic 5 - Clustering

  • What is Clustering?
  • K-Means Clustering
  • Hierarchical Clustering

Topic 6 - Neural Network

  • What is Neural Network?
  • Multilayer Perceptron Classifier

Topic 7 - Problem Solving through Machine Learning

  • Problem Definition
  • Data Conceptualization
  • Data Gathering
  • Feature Engineering
  • Algorithm Spot Check
  • Fine Tuning Model
  • Pitfalls

Course Admin

Prerequisite

This course is for beginner. No programming or coding knowledge is required. ;

Software Requirement

Please download and install Weka from https://www.cs.waikato.ac.nz/~ml/weka/downloading.html

Who Should Attend

This course is for both for:

Non programmers who wish to understand how Machine Learning should be applied for problem solving (without being bogged down by a particular programming language)
Programmers and Machine Learning practitioners who want to better understand how to apply the algorithms to real life problem solving

Trainers

Basic Tableau Trainer Ghazaleh Babanejad has received her Ph.D from University Putra Malaysia in Faculty of Computer ‎Science and Information Technology. She is working on recommender systems in the ‎ the field of skyline queries over Dynamic and Incomplete databases for her Ph.D. thesis. She is also working ‎on Data Science field as a trainer and Data Scientist. She worked on Machine Learning and Process ‎ Mining projects. She also has several international certificates in Practical Machine Learning (John ‎ Hopkins University) Mining Massive Datasets (Stanford University), Process Mining (Eindhoven ‎ University), Hadoop (University of San Diego), MongoDB for DBAs (MongoDB Inc) and some other ‎ certificates. She has more than 5 years of experience as a lecturer and database administrator.

Cyber SecurityTrainerReza Adinehnia is an experienced Network Security Specialist with a demonstrated history of working in IT industry. He is skilled in Data mining, VMware ESX, Database Security, C, PHP, Computer networks and security. He possesses a strong information technology background with a Doctor of Philosophy (Ph.D.) focused in Computer Security from Universiti Putra Malaysia.

Customer Reviews (5)

will recommend 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
. (Posted on 06/03/2020)
Might Consider 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
. (Posted on 20/10/2019)
Will Recommend 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
. (Posted on 04/10/2019)
Will Recommend 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
Extended to two days course (Posted on 26/12/2018)
Will Recommend 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
Should add more exercises and examples (Posted on 17/09/2018)

Write Your Own Review

You're reviewing: Solving Problems with Machine Learning

How do you rate this product? *

  1 star 2 stars 3 stars 4 stars 5 stars
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
  • Reload captcha
    Attention: Captcha is case sensitive.

Product Subjects

Other people marked this product with these Subjects:

Use spaces to separate Subjects. Use single quotes (') for phrases.