Hands-on Scikit-learn for Machine Learning [Video]
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.
All the code and supporting files for this course are available on Github at: https://github.com/PacktPublishing/Hands-on-Scikit-learn-for-Machine-Learning-V-Style and Approach
The course enables you to immediately apply its topics to real world data sets via step-by-step code walk-through. We take a data set through several concepts such as preprocessing and cleaning, data preparation, modeling, feature extraction and engineering, dimensionality reduction, hyper-parameter tuning, and model performance enhancement while giving tips and techniques on how to choose from different models and approaches and make the best use of Scikit-learn modules.
|Course Length||9 hours 3 minutes|
|Date Of Publication||27 Aug 2018|
|Applying PCA with Scikit-learn for Feature Reduction|
|Applying PCA for a Regression Problem on a Large Dataset|
|Nonlinear Methods of Feature Extraction – t-SNE and Isomap|
|Applying Dimensionality Reduction Techniques to Images|