Building Advanced Machine Learning and AI Solutions with Pytorch
About This Course
Embark on a groundbreaking journey with our WSQ-endorsed Pattern Recognition with Pytorch course. This course will introduce you to the complexities of recognizing patterns in data sets, utilizing Pytorch as the main framework. Through real-world examples and hands-on projects, you'll get to grips with neural networks, image recognition, and various machine learning algorithms, sharpening your skills in data analysis.
Upon completion of the course, you'll be fully equipped to interpret complex data structures and predict future patterns using Pytorch. This course is your stepping stone to becoming a specialist in machine learning and data analysis, with a particular focus on pattern recognition techniques.
What You'll Learn
- assess various machine learning techniques across different application domains
- apply machine learning techniques to regression and classification problems.
- solve temporal sequential problem using recurrent neural network
- build pattern recognition systems using convolutional neural network
- apply machine learning techniques to signal processing
Course Outline:
Topic 1 Overview of Deep Learning Application Domains
AI and Deep Landscape
Deep Learning Application Domains
AI Ethnics
Topic 2 Machine Learning for Regression and Classification
General Concepts of Machine Learning and Neural Network (NN)
Introduction to Pytorch Framework
Build a Regression Model Using NN
Build a Classification Model Using NN
Topic 3 Recurrent Neural Network (RNN)
Introduction to Recurrent Neural Network (RNN)
Build a Temporal Sequential Model Using RNN
Build a Sentimental Analysis Model Using RNN
Topic 4 Convolutional Neural Network (CNN)
Introduction to Convolutional Neural Network (CNN)
Build a Image Classification Model Using CNN
Techniques to Resolve Overfitting Issue
Topic 5 Application of Machine Learning to Signal Processing
Digital Signal Processing
Machine Learning Classification for Signal Processing
Evaluation of Signal Processing Performance
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