WSQ Principles of Machine Learning (SF) (Synchronous and Asynchronous E-Learning)

About This Course

The "Principles of Machine Learning" module provides participants with foundational knowledge and skills crucial for understanding and applying machine learning techniques. Delivered through a comprehensive set of Instructional Units (IUs), this program ensures participants establish a robust understanding of the principles and methodologies inherent in machine learning.
In the initial IU, participants delve into classification, mastering various algorithms to classify data into distinct categories, facilitating informed decisions based on input features. The second IU shifts focus to regression, where participants learn to create models predicting continuous numeric values using diverse regression algorithms.
The next IU concentrates on enhancing machine learning models, covering techniques such as regularization, and hyperparameter optimization using sci-kit learn. This includes refining the ability to fine-tune models and optimize hyperparameters for heightened accuracy and generalization. The fourth IU introduces tree and ensemble methods, leveraging the combination of multiple models for enhanced predictive performance.
Furthermore, the module explores optimization-based methods, exploring algorithms like Artificial Neural Networks and Support Vector Machines. This equips participants to optimize model parameters for improved performance. Throughout the module, participants apply their knowledge in a practical project involving validating, transferring, integrating, and deploying models using Microsoft Azure. This real-world project allows participants to practically apply machine learning principles, reinforcing their understanding and practical capabilities.
Upon completing this module, participants possess a comprehensive grasp of machine learning principles, along with the skills to implement various techniques, optimize models using python libraries like Sci-kit learn.