This is the code repository for Hands-on Scikit-learn for Machine Learning [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
Scikit-learn is arguably the most popular Python library for Machine Learning today. Thousands of Data Scientists and Machine Learning practitioners use it for day to day tasks throughout a Machine Learning project’s life cycle. Due to its popularity and coverage of a wide variety of ML models and built-in utilities, jobs for Scikit-learn are in high demand, both in industry and academia. If you’re an aspiring machine learning engineer ready to take real-world projects head-on, Hands-on Scikit-Learn for Machine Learning will walk you through the most commonly used models, libraries, and utilities offered by Scikit-learn. By the end of the course, you will have a set of ML problem-solving tools in the form of code modules and utility functions based on Scikit-learn in one place, instead of spread over several books and courses, which you can easily use on real-world projects and data sets.
- Tackle real-world problems in Machine Learning through a structured process using Scikit-learn
- Achieve substantially more in less time and with much less code by leveraging the power and simplicity of Scikit-learn
- Develop a thorough understanding of core predictive analytics with regression, classification, and unsupervised learning such as clustering and PCA
- Create ensemble models with Random-Forest and Gradient-boosting methods and see your model performance improve drastically
- Build a portfolio of tools and techniques that can readily be applied to your own projects
- Discover the intuition behind contemporary Machine Learning models and algorithms without going into deep mathematical details
- Develop the ability to evaluate and improve the accuracy and performance of Machine Learning models
- Explore the foundations of text analytics and develop a set of tools to apply to your common text-analysis tasks
To fully benefit from the coverage included in this course, you will need:
If you are a software developer, machine learning engineer, or data analyst and want to use Scikit-learn for different Machine Learning and analytics tasks, this course is for you. You need to have a very basic understanding of Machine Learning and Data Analytics. However, no knowledge of Scikit-learn is needed. Python programming knowledge and a basic understanding of Numpy and the Pandas library are assumed.
For successful completion of this course, students will require the computer systems with at least the following: