Practical Machine Learning
Explore data. Build ETL workflows. Train models. Deploy models. Learn to have an impact using machine learning.
Ratings 5.00 / 5.00
What You Will Learn!
- Define the roles and responsibilities of a machine learning engineer
- Work with datasets using pandas and identify key insights
- Leverage data pipeline tools to create data workflows
- Train models using libraries like scikit learn, xgboost, and PyTorch
- Learn about MLOps and deploy models using backend technology like Triton Inference Server
Description
This course is designed for learners from all backgrounds, primarily focusing on beginners.
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.
Who Should Attend!
- Software engineers who are interested in machine learning
- Python developers who want to dabble in machine learning