Practical Machine Learning

Name: Practical Machine Learning
Rating: 5.0 (4 reviews)

Explore data. Build ETL workflows. Train models. Deploy models. Learn to have an impact using machine learning.

Last updated 5/2026

English

What you'll 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

This course includes our updated coding exercises so you can practice your skills as you learn.

6 sections • 12 lectures • 1h 18m total length

Introduction1:44
I hope to give you all the high level touch points as well as demo assignments to help guide you on your journey as an machine learning engineer.
Define the roles and responsibilities of a machine learning engineer1:17
A day in the life as an ML engineer
Common applications for ML [optional]7:38
This is an optional and informal tangent lecture on some common applications of machine learning at various companies.
After this lecture, you should have a better grasp of the common applications of machine learning and where you can find a job in ML.

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.