• Implement machine learning algorithms to build, train, and validate algorithmic models
• Create your own algorithmic design process to apply...

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...

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...

• 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...

• Explore the tools you need to become a data analyst
• Discover practical examples to help you grasp data processing concepts
• Walk through hierarchical indexing...

• Grasp all aspects of numerical computing and understand NumPy
• Explore examples to learn exploratory data analysis (EDA), regression, and clustering
• Access...

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...

• Cover all fundamental libraries for operation and manipulation of Python for data analysis
• Implement real-world datasets to perform predictive analytics with Python
...

• 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...