Hands-on Scikit-learn for Machine Learning
Tackle real-world Machine Learning problems using Scikit-learn to quickly build a portfolio of practical Machine Learning tools
Created by Farhan Nazar Zaidi
Explore practical machine learning by working directly with Scikit-learn, the go-to Python library for building and improving real-world ML models. You'll learn how to tackle common data tasks and streamline your workflow, making it easier to solve business problems with confidence.
Packt | Aug 2018 | 543 min
What You Will Learn
You'll work through real datasets using step-by-step code walkthroughs, covering everything from data cleaning and feature engineering to model selection and performance tuning. Each topic is applied directly to practical problems, helping you build a toolkit of reusable Scikit-learn solutions for your own projects.
Key Features
- Build and tune ML models for classification, regression, and dimensionality reduction
- Automate data workflows using Scikit-learn pipelines and preprocessing tools
- Apply best practices for evaluating and improving model performance
Target Audience
Ideal for software developers, machine learning engineers, and data analysts who know Python and basic ML concepts. If you want to confidently use Scikit-learn for real analytics and machine learning tasks, and are ready to move beyond the basics, you'll find practical skills here to advance your work.
