The course covers many of the cornerstones of practical machine learning, including:

• Industry Use Cases and Employer Expectations: Explore a variety of industry applications for machine learning and understand what companies are looking for in ML roles.

• Exploring Real-World data: Gain hands-on experience with data sourced from a real-world scenario, learning to navigate and interpret complex datasets.

• Building Data Workflows: Understand the architecture of data pipelines, including typical tools and techniques used in the industry.

• Model Development and Evaluation: Learn how to construct machine learning models and critically assess their performance and effectiveness. Iterate upon models with feature engineering and hyperparameter tuning.

• Model Deployment and Monitoring: Master the skills necessary to deploy models into a production environment and continuously monitor their performance.

Value to Learners:

• Applicability of Skills: The skills taught are directly transferable to real-world scenarios, equipping learners with the tools needed for a career in machine learning.

• Comprehensive Understanding: From data handling to model deployment, this course offers a holistic view of what it takes to be a machine learning engineer.

• Hands-On Experience: With a focus on practical exercises and real-world examples, learners will gain firsthand experience that goes beyond theoretical knowledge.