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Hands-On Machine Learning for Algorithmic Trading: Design and implement investment strategies based on smart algorithms that learn from data using Python
Hands-On Machine Learning for Algorithmic Trading: Design and implement investment strategies based on smart algorithms that learn from data using Python

Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and Keras

Key Features

  • Implement machine learning algorithms to build, train, and validate algorithmic models
  • Create your own algorithmic design process to apply...
Python for Data Mining Quick Syntax Reference
Python for Data Mining Quick Syntax Reference
?Learn how to use Python and its structures, how to install Python, and which tools are best suited for data analyst work. This book provides you with a handy reference and tutorial on topics ranging from basic Python concepts through to data mining, manipulating and importing datasets, and data analysis.

Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks

Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You’ll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and...

Python Data Analytics: With Pandas, NumPy, and Matplotlib
Python Data Analytics: With Pandas, NumPy, and Matplotlib
Explore the latest Python tools and techniques to help you tackle the world of data acquisition and analysis. You'll review scientific computing with NumPy, visualization with matplotlib, and machine learning with scikit-learn. 

This revision is fully updated with new content on social media data analysis, image
...
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists

Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you...

Python Data Science Essentials: A practitioner's guide covering essential data science principles, tools, and techniques, 3rd Edition
Python Data Science Essentials: A practitioner's guide covering essential data science principles, tools, and techniques, 3rd Edition

Gain useful insights from your data using popular data science tools

Key Features

  • A one-stop guide to Python libraries such as pandas and NumPy
  • Comprehensive coverage of data science operations such as data cleaning and data manipulation
  • Choose scalable...
Hands-On Data Analysis with NumPy and pandas: Implement Python packages from data manipulation to processing
Hands-On Data Analysis with NumPy and pandas: Implement Python packages from data manipulation to processing

Get to grips with the most popular Python packages that make data analysis possible

Key Features

  • Explore the tools you need to become a data analyst
  • Discover practical examples to help you grasp data processing concepts
  • Walk through hierarchical indexing...
Mastering Numerical Computing with NumPy: Master scientific computing and perform complex operations with ease
Mastering Numerical Computing with NumPy: Master scientific computing and perform complex operations with ease

Enhance the power of NumPy and start boosting your scientific computing capabilities

Key Features

  • Grasp all aspects of numerical computing and understand NumPy
  • Explore examples to learn exploratory data analysis (EDA), regression, and clustering
  • Access...
Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib
Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib

Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute...

Become a Python Data Analyst: Perform exploratory data analysis and gain insight into scientific computing using Python
Become a Python Data Analyst: Perform exploratory data analysis and gain insight into scientific computing using Python

Enhance your data analysis and predictive modeling skills using popular Python tools

Key Features

  • Cover all fundamental libraries for operation and manipulation of Python for data analysis
  • Implement real-world datasets to perform predictive analytics with Python
  • ...
Hands-On Data Visualization with Bokeh: Interactive web plotting for Python using Bokeh
Hands-On Data Visualization with Bokeh: Interactive web plotting for Python using Bokeh

Learn how to create interactive and visually aesthetic plots using the Bokeh package in Python

Key Features

  • A step by step approach to creating interactive plots with Bokeh
  • Go from nstallation all the way to deploying your very own Bokeh application
  • Work...
PySpark Recipes: A Problem-Solution Approach with PySpark2
PySpark Recipes: A Problem-Solution Approach with PySpark2
Quickly find solutions to common programming problems encountered while processing big data. Content is presented in the popular problem-solution format. Look up the programming problem that you want to solve. Read the solution. Apply the solution directly in your own code. Problem solved!

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