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)

Approaching deep learning

Deep learning is an extension of the classical machine-learning approach using neural networks: instead of building networks of a few layers (so-called shallow networks), we can stack hundreds of layers to create an elaborate, but more powerful, learner. Deep learning is one of the most popular methods of artificial intelligence (AI) nowadays since it's very effective and helps to solve many problems in pattern recognition, such as object or sequence identification, which seemed unbreakable using standard machine learning tools.

The idea of neural networks came from the human central nervous system, where multiple nodes (or neurons) that are able to process simple information are connected together to create a network capable of processing complex information. In fact, neural networks are so named because they can learn the weights of the model...