Pattern Recognition and Machine Learning with R
About This Course
Dive into the intricate world of pattern recognition and machine learning with our WSQ-accredited course. This course is structured to take you from the essentials of R programming to the complexities of machine learning algorithms. You will learn to deploy pattern recognition techniques for data classification, delve into clustering methods, and explore predictive analytics. Real-world examples and projects are integrated into the curriculum for hands-on learning.
Whether you're a seasoned data scientist or a beginner eager to expand your skill set, this course is designed to equip you with the knowledge needed for the evolving field of data analytics. You'll leave with practical experience in applying machine learning algorithms using R, positioning you at the forefront of today’s data-driven landscape. Upgrade your analytics skills and stay ahead of the curve with our specialized training.
What You'll Learn
- model pattern recognition problems suitable for machine learning
- apply supervised regression techniques to predict pattern in the data
- apply supervised classification techniques to classify pattern in the data
- apply unsupervised clustering techniques to cluster patterns and detect anomaly in the data
- apply principal component analysis as alternative method to detect pattern in the data
- apply deep neural network and CNN models for visual recognition
Course Outline:
Topic 1 Overview of Machine Learning
Introduction to Machine Learning
Pattern Recognition Problems Suitable for Machine Learning
Supervised vs Unsupervised Learnings
Types of Machine Learning
Machine Learning Techniques
R Packages for Machine Learning
Topic 2 Regression
What is Regression
Applications of Regression
Least Square Error Minimization
Data Pre-processing
Bias vs Variance Trade-off
Regression Methods with Regularization
Logistic Regression
Topic 3 Classification
What is Classification
Applications of Classification
Classification Algorithms
Confusion Matrix
Classification Performance Evaluation
Topic 4 Clustering
What is Clustering
Applications of Clustering
Distance Measure
Clustering Algorithms
Clustering Performance Evaluation
Anomaly Detection Problem
Topic 5 Principal Component Analysis
Principal Component Analysis (PCA) and Dimension Reduction
Applications of PCA
PCA Workflow
Topic 6 Deep Learning
What is Neural Network
Activation Functions
Loss Function Minimization
Gradient Descent Algorithms and Learning Rate
Deep Neural Network for Visual Recognition
Improve Visual Recognition with Convolutional Neural Network
The Future of AI
AI Ethics
Entry Requirements
Knowledge and Skills
• Able to operate computer functions with minimum Computer Literacy Level 2 based on ICAS Computer Skills Assessment Framework
• Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)
Attitude
• Positive Learning Attitude
• Enthusiastic Learner
Experience
• Minimum of 1 year of working experience.
• Minimum 18 years old