Python Data Science Handbook: Essential Tools for Working with Data - PDF, 1st Edition For many researchers, Python is a first-class tool, primarily because of its libraries for storing, manipulating, and extracting knowledge from data. There are several resources for the individual parts of this data science stack, but the Python Data Science Handbook is the only way to get them all: IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.
Scientists and data processors familiar with reading and writing Python code will find this comprehensive reference guide ideal for solving everyday problems: manipulating, transforming, and cleaning data; Display of different types of data; and using the data to create statistical or machine learning models. Quite simply, this is the indispensable reference for scientific arithmetic in Python.
This guide will teach you how to use:
I just finished this book. The author provides a well-written introduction to machine learning with Python Scikit-Learn and illustrates each chapter with well-designed examples that are easy to follow and understand. I am very happy with this book and can definitely recommend it to anyone new to the field and wanting to quickly master practical machine learning approaches.
The book is simply fantastic: an amazing combination of detail and brevity. I had programming experience but no experience with Python before reading this book. Very suitable for my qualifications. Recommended for anyone looking to break new ground in data science with Python.
The book is without updates, so most of the examples are outdated or won't work with new updates. This is especially true of the chapter on pandas, where nearly all other examples are flawed. Don't waste your money or time on this junk book. There are also no exercises to test your practice. Unfortunately, nearly all of O'Reilly's Python books are of this caliber - a bunch of invented recipes that, even on the rare occasions they work, don't lead to real-world data problems at all.
This book is an amazing resource for anyone interested in machine learning in Python. The language provides great tools for working with data, and it's important to understand these tools to get the most out of your time and effort. If you are familiar with computer programming in other languages, this is a perfect book for learning what goes on under the hood of Python and how you can use its tools to get what you want.
I am a huge fan of this book. When I started data science, I went through page by page, taking notes and practicing the techniques. Now I use it as a quick reference guide whenever I need to brush up on syntax. Highly recommended.Machine learning tools.
Shogun is a rich machine learning solution with a focus on Support Vector Machines (SVM). It is written in C ++. Shogun offers a wide range of unified machine learning methods based on reliable and understandable algorithms.
Shogun is well documented. The disadvantages include the relative complexity of working with the API. Free distribution.
Keras is a high-level neural network API that provides a deep learning library for Python. It is one of the best tools for anyone starting out as a machine learning professional. Compared to other libraries, Keras is much more intuitive. Popular Python frameworks like TensorFlow, CNTK or Theano can work with it.
The 4 core principles behind the Keras philosophy are user friendliness, modularity, extensibility, and Python compatibility. Among the disadvantages is the relatively slow speed of work compared to other libraries.
Scikit-Learn is an open-source data mining and analysis tool. It can be used in data-science as well. The tool's API is convenient and practical, it can be used to create a large number of services. One of the main advantages is the speed of work: Scikit-Learn simply breaks records. The main features of the tool are regression, clustering, model selection, preprocessing, classification.
Pattern is a web mining module that provides capabilities for data collection, language processing, machine learning, network analysis, and all sorts of visualizations. It is well documented and comes with 50 case studies as well as 350 unit tests. And it's free!
Theano is named after the ancient Greek philosopher and mathematician who gave the world a lot of useful things. The main functions of Theano are integration with NumPy, transparent use of GPU resources, speed and stability of work, self-verification, generation of dynamic C-code. Among the disadvantages are the relatively complex API and slower performance when compared to other libraries.
SciPy is a Python-based open-source software ecosystem for mathematicians, IT professionals, and engineers. SciPy uses various packages like NumPy, IPython, Pandas, which allows you to use popular libraries for solving mathematical and scientific problems. This tool is a great option if you need to show data from serious computations. And it's free.
Dask is a solution that enables data parallelism in analytics through integration with packages such as NumPy, Pandas, and Scikit-Learn. With Dask, you can quickly parallelize existing code by changing just a few lines. The fact is that its DataFrame is the same as in the Pandas library, and NumPy working with it has the ability to parallelize tasks written in pure Python.
Numba is an open source compiler that uses the LLVM compiler framework to compile Python syntax to machine code. The main advantage of working with Numba in scientific applications is its speed when using code with NumPy arrays. Like Scikit-Learn, Numba is suitable for building machine learning applications. It's worth noting that Numba-based solutions will perform especially quickly on hardware built for machine learning or scientific research applications.
High-Performance Analytics Toolkit (HPAT) is a compiler-based framework for big data. It automatically scales analytical programs, as well as machine learning programs, to the performance level of cloud services and can optimize certain functions using the jit decorator.
Cython is the best choice for working with math code. Cython is a Pyrex-based source translator that allows you to easily write C extensions for Python. Moreover, with the addition of support for integration with IPython / Jupyter, code written using Cython can be used in Jupyter using built-in annotations, just like any other Python code.
The above tools are almost ideal for scientists, programmers, and anyone else involved with machine learning and big data. And of course, it's worth remembering that these tools are Python-specific.
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