Machine Learning Fundamentals
Use Python and scikit-learn to get up and running with the hottest developments in machine learning
Created by Hyatt Saleh
Get hands-on with machine learning by working directly with Python and scikit-learn. You'll explore the core concepts behind supervised and unsupervised models, practice applying popular algorithms, and learn how to analyze and improve your results using real-world datasets.
Packt | Nov 2018 | 240 min
What You Will Learn
You'll start by exploring the basics of scikit-learn and how to represent data for machine learning. As you move through each section, you'll practice building and evaluating models, experimenting with clustering and classification, and using visualizations to interpret your results. Each activity is grounded in practical examples so you can see how machine learning works in real scenarios.
Key Features
- Apply supervised and unsupervised algorithms to real datasets using scikit-learn
- Analyze model performance and tune hyperparameters for better results
- Understand when to use different machine learning models and techniques
Target Audience
This course is ideal for developers and programmers who are comfortable with Python and want to break into machine learning. If you're new to the field but eager to start building and understanding machine learning models using scikit-learn, you'll find the step-by-step approach and practical exercises especially helpful.
