Scikit-learn Recipes

Practical recipes for powerful data analysis with scikit-learn

Learn

Explore the most-used applications of scikit-learn used by top data scientists from around the world
Confidently use scikit-learn to build better machine learning models
Deep dive into implementing deep learning with scikit learn using neural network for faster model building and data manipulation.
Learn to find the best model and analyze data faster with cross-validation techniques in scikit-learn
Manipulate and visualize data effectively to enhance computing time for mathematical operations
Explore the feed-forward neural networks available in scikit-learn for large datasets and better results
Evaluate and fine-tune the performance of your model built-in scikit-learn

About

Scikit-learn is one of the most powerful packages that top data scientists prefer for machine learning. Powerful data analysis and machine learning require fast, accurate computations, and scikit-learn’s packages make building powerful machine learning models super-easy!

In this course, you will start with generating synthetic data for building a machine learning model, pre-process the data with scikit-learn, and build various supervised and unsupervised models. You will then deep-dive into implementing various optimization techniques like cross-validation, feature selection, regularization, and also dimensionality reduction techniques.

By the end of this course, you will be able to build your own machine learning models and take your data analysis skills to the next level!

All the code and supporting files for this course are available on GitHub at https://github.com/PacktPublishing/Scikit-learn-Recipes

Features

Feed into a wide variety of machine learning models
Build a variety of machine learning models effortlessly by leveraging the power of scikit-learn
Easy-to-understand practical recipes to help you choose the right machine learning algorithm