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)

Summary

In this chapter, we saw the essentials and some advanced models for deep networks. We were introduced to how neural networks work and the difference between shallow networks and deep learning. Then, we learnt ho to build a CNN deep network capable of classifying images of traffic signs. We also predicted the class of an image using a pre-trained network. Detecting the sentiment of a movie review using text found in reviews was also a part of the learning.

Deep learning models are indeed very powerful, though at the cost of having many degrees of freedom to handle and many coefficients to train, which requires having at hand large amounts of data.

In the next chapter, we'll see how Spark helps when the amount of data becomes too large to be handled and processed by a single computer.