Аннотация
Data Science with Python begins by introducing you to data science and then teaches you to install the packages you need to create a data science coding environment. You will learn three major techniques in machine learning: unsupervised learning, supervised learning, and reinforcement learning. You will also explore basic classification and regression techniques, such as support vector machines, decision trees, and logistic regression.
As you make your way through chapters, you will study the basic functions, data structures, and syntax of the Python language that are used to handle large datasets with ease. You will learn about NumPy and pandas libraries for matrix calculations and data manipulation, study how to use Matplotlib to create highly customizable visualizations, and apply the boosting algorithm XGBoost to make predictions. In the concluding chapters, you will explore convolutional neural networks (CNNs), deep learning algorithms used to predict what is in an image. You will also understand how to feed human sentences to a neural network, make the model process contextual information, and create human language processing systems to predict the outcome.
By the end of this book, you will be able to understand and implement any new data science algorithm and have the confidence to experiment with tools or libraries other than those covered in the book.

![Pythonic Geodynamics [Implementations for Fast... Pythonic Geodynamics [Implementations for Fast Computing]](https://www.rulit.me/kotha/images/nocover.jpg)





![Few books on software project management have been as influential and timeless as The Mythical Man-Month. With a blend of software engineering facts and thought-provoking opinions, Fred Brooks offers insight for anyone managing complex projects. These essays draw from his experience as project... The Mythical Man-Month: Essays on Software Engineering [Anniversary Edition]](https://www.rulit.me/data/programs/images/the-mythical-man-month-essays-on-software-engineering-annive_492687.jpg)

Комментарии к книге "Data Science with Python"