WSQ Applied Machine Learning (sf) (synchronous and Asynchronous E-Learning)

The "Applied Machine Learning" module provides a comprehensive exploration of key machine learning concepts and practical skills crucial for real-world applications. Commencing with an introduction to machine learning, learners progress through instructional units (IUs) covering classification, regression, and tree and ensemble methods. The module extends to practical aspects, guiding participants on working with data and compute in Azure Machine Learning. Learners delve into training models with scripts and gain proficiency in managing and deploying models, essential skills for effective utilization of machine learning in practical scenarios.

Throughout this module, learners acquire knowledge in machine learning fundamentals, ranging from the foundational principles of classification and regression to advanced techniques like tree and ensemble methods. They gain a deep understanding of how machine learning models operate, allowing them to make informed decisions on model selection and optimization. Additionally, participants develop expertise in leveraging Azure Machine Learning for data processing, model training, and deployment. The practical skills gained in this module are diverse and directly applicable to real-world machine learning scenarios. Learners become adept at scripting to train models, a critical skill for customizing machine learning algorithms to suit specific tasks. Moreover, they master the intricacies of managing and deploying models, ensuring seamless integration of machine learning solutions into practical applications.

In the culminating project, learners undertake the task of training, evaluating, and deploying a machine learning model for prediction. This hands-on project allows participants to synthesize their acquired knowledge and skills into a real-world application. By successfully completing the project, participants demonstrate their ability to navigate the end-to-end machine learning process, from model development to deployment.

What You'll Learn

This course is designed to target the audience who would like to gain machine learning skills. Duration of this course is 60.5 hours.

This course comprises of following Instructional Units

• Classification
• Regression
• Tree and Ensemble methods
• Work with Data and Compute in Azure Machine Learning
• Train model with scripts
• Manage and deploy models.

Knowledge Outcomes

Identify key principles and applications of machine learning, demonstrating comprehension of foundational concepts and their relevance.

Classify machine learning tasks, distinguishing between supervised and unsupervised learning methodologies, and recognize suitable applications for each.

Evaluate and select appropriate techniques for enhancing model performance, demonstrating knowledge of feature selection and hyperparameter tuning.

Demonstrate understanding of the machine learning pipeline, outlining the steps involved in preprocessing data, training models, and evaluating performance.

Summarize the importance of practical projects, showcasing the ability to apply machine learning concepts in real-world scenarios effectively.

Skills Outcomes

Develop machine learning models for classification and regression tasks, applying supervised learning techniques with proficiency.

Apply clustering and anomaly detection methods in unsupervised machine learning, showcasing skills in identifying patterns and outliers.

Improve machine learning model accuracy and efficiency through hands-on application of feature selection and hyperparameter tuning techniques.

Design and implement machine learning pipelines, demonstrating proficiency in preprocessing data, model training, and performance evaluation.

Deploy machine learning models effectively, showcasing practical skills in bringing trained models into real-world applications.

Minimum Entry Requirement

Academic Qualification – Minimum O Level credit in Maths or Minimum one credit in Nitech in STEM

Experience – 2 years experience in Programming or Data analytics