Machine Learning

Course Overview

This 5-day Machine Learning course is meant to give participants a thorough understanding of machine learning ideas, techniques, and practical applications. Participants will engage in practical tasks and real-world scenarios throughout the course to strengthen their theoretical knowledge. This course will give you the knowledge and tools you need to traverse the dynamic field of machine learning, whether you are a new learning how it works or an experienced professional trying to improve your skills.

Course Objectives

By the end of this 5-day training, participants will:

Learn the fundamentals of machine learning, including supervised and unsupervised learning, feature engineering, and model evaluation.
Master Algorithms: Learn how to use a range of machine learning methods, including linear regression, decision trees, support vector machines, and neural networks.
Data Preprocessing: Learn how to clean, transform, and prepare datasets for machine learning models using effective data preprocessing methods.
Model Evaluation: Develop skills in evaluating and selecting the best models for certain tasks, taking into account measures such as accuracy, precision, recall, and F1 score.
Practical Experience: Gain practical skills by working on real-world projects and creating machine learning algorithms using popular frameworks.

Course Outline

Day 1: Introduction to Machine Learning

Understanding the basics of machine learning
Differentiating supervised and unsupervised learning
Exploring real-world applications of machine learning

Day 2: Fundamentals of Data and Feature Engineering

Data exploration and visualization
Preprocessing techniques for cleaning and transforming data
Feature engineering for improved model performance

Day 3: Supervised Learning Algorithms

Linear regression and logistic regression
Decision trees and random forests
Support vector machines

Day 4: Unsupervised Learning Algorithms

Clustering techniques (K-means, hierarchical)
Dimensionality reduction (PCA)
Introduction to neural networks

Day 5: Model Evaluation and Deployment

Model evaluation metrics and techniques
Deployment considerations and best practices
Case studies and real-world applications

Course Outcomes :

By the end of the training, participants will be equipped to:

Build and train machine learning models for various tasks.
Show your ability to implement and assess machine learning models.
Apply data preprocessing techniques to improve model accuracy.
Implement supervised and unsupervised learning algorithms.
Understand the ethical considerations and challenges in machine learning deployment.

Duration:

5 days

Registered Training Partners

DATE, VENUE AND FEES

Date : 15-19 Jan 2024 / 19-23 Feb 2024 / 18-22 Mar 2024 / 22-26 April 2024 / 27-31 May 2024 / 10-14 June 2024 / 15-19 July 2024 / 26-30 Aug 2024 / 9-13 Sept 2024 / 14-18 Oct 2024 / 25-29 Nov 2024 / 2-6 Dec 2024

Duration : 10.30AM – 4.30PM

Venue : 27-1, Jalan Desa, Taman Desa, Kuala Lumpur 58100, Malaysia

Location : https://goo.gl/maps/V1TZ1vkDxdH2

All the programmes can be conducted in house.
For SEO programme (inhouse) they will be given a free six-month SEO package worth MYR 9,000