Book Image

Python Data Science Essentials - Third Edition

By : Alberto Boschetti, Luca Massaron
Book Image

Python Data Science Essentials - Third Edition

By: Alberto Boschetti, Luca Massaron

Overview of this book

Fully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn. The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost. By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users
Table of Contents (11 chapters)

Advanced data learning representation

Some useful representations can be derived from the data science process. That is, the representation is not done directly from the data, but is achieved by using machine learning procedures, which inform us about how the algorithms operate and offer us a more precise overview of the role of each predictor in the predictions obtained. In particular, learning curves can provide a quick diagnosis to improve your models. This helps you figure out whether you need more observations, or need to enrich your variables.

Learning curves

A learning curve is a useful diagnostic graphic that depicts the behavior of your machine learning algorithm (your hypothesis) with respect to the available quantity...