This is the code repository for scikit-learn Recipes [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 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! This course is targeted at those new to scikit-learn or with some basic knowledge. 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!
- 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
Data analysts and programmers already familiar with Python, and who are either new to scikit-learn or have some basic background with it and want quick solutions to common machine learning problems, will find this course useful. If you are a Python programmer wanting to take a dive into the world of machine learning in a practical manner, this course will help you too.
This course has the following requirements:
Basic familiarity with Python as well as Machine Learning concepts
scikit-learn v0.21.3
Anaconda
Jupyter Notebook