WSQ Machine Learning Algorithms and methods (SF) (Synchronous and Asynchronous E-Learning)
About This Course
The "Machine Learning Algorithms and Methods" module empowers learners with a profound understanding of key concepts and practical skills required in real-world applications. Through a structured curriculum, participants delve into Regression tasks, Classification tasks, and Clustering tasks, and harness the power of Azure AI Services for Pre-built models, culminating in a focus on Text Analysis with the Language Service.
Learners master the intricacies of Regression tasks, acquiring the ability to select and apply appropriate algorithms for predictive modelling. The module extends to Classification tasks, providing insights into classifying data into distinct categories, and delves into Clustering tasks, unravelling techniques to group data points based on inherent patterns. Additionally, participants gain proficiency in leveraging Azure AI Services for Pre-built models, enhancing their toolkit with readily available models for diverse applications. The module concludes with a deep dive into Text Analysis using the Language Service, unravelling the complexities of processing, and extracting insights from textual data.
Through hands-on projects, participants implement machine learning tasks using Azure ML Designer, facilitating a comparative analysis of algorithmic performance. The culmination involves the implementation of regression, classification and clustering using Azure ML Studio. As a result, learners emerge with a robust skill set, ready to apply machine learning principles to solve complex problems in diverse domains.
What You'll Learn
Duration of this course is 130.5
Instructional Units
1. Regression tasks
2. Classification tasks
3. Clustering tasks
4. Azure AI Services for Pre-built models
5. Text Analysis with the Language Service
Knowledge Outcomes:
Machine Learning Task Understanding:
Learners grasp regression, classification, and clustering tasks in ML, understanding their principles and applications.
Algorithm Selection Knowledge:
Participants gain insights into selecting and applying appropriate algorithms for predictive modeling, including regression and classification algorithms.
Proficiency in Azure AI Services:
Students acquire skills in leveraging Azure AI Services for pre-built models, enhancing their toolkit for efficient model development.
Skills Outcomes:
Machine Learning Task Implementation:
Participants develop practical skills in implementing regression, classification, and clustering tasks using Azure ML Designer.
Algorithmic Performance Comparison:
Learners acquire skills in reviewing and comparing algorithm performances using Azure ML Designer, aiding in informed decision-making.
AutoML and Azure ML Studio Application:
Students demonstrate proficiency in implementing tasks using AutoML and Azure ML Designer, facilitating comparison and decision-making based on performance metrics.
Entry Requirements
Academic Qualification: Minimum 1 credit in O Level or its equivalent
Experience: Minimum 1-year experience in any business process