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James Robinson,
Marc Scott,
Martin O'Hanlon,
Ross Exton,
Michael Conterio,
Sam Isaacs,
Nina Szymor,
Mac Bowley,
and
Alex Parry
Topics Covered
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Topics Covered
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Topics Covered
- Use functions with parameters and return values
- Design and apply algorithms to data
- Breaking down problems into smaller parts
- Searching and sorting
- Efficiency of algorithms
- Understanding of list structures and their uses
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Deep dive into Python's advanced features, covering metaprogramming, decorators, generators, and concurrency.
Practical guide to using Python for basic automation tasks, providing a gentle introduction to Python's core concepts and its practical applications.
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Collection of practical recipes and solutions for common Python programming tasks, covering a wide range of topics.
Comprehensive guide to Python's data analysis ecosystem, including NumPy, Pandas, and Matplotlib, with a focus on practical applications.
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Comprehensive guide to deep learning using Python, covering neural networks, convolutional neural networks, and recurrent neural networks.
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Uses a creative and engaging approach to teach Python concepts, with a focus on visual learning and hands-on exercises.
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Comprehensive guide to the basics of Python programming, covering data types, control flow, functions, object-oriented programming, and debugging.
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Is an introductory text to Python programming, with a focus on computational thinking and problem-solving.
Comprehensive guide to machine learning using Python, covering supervised and unsupervised learning, model evaluation, and deployment.
Comprehensive guide to the Python Standard Library, covering its vast collection of modules and their applications.
Practical guide to testing Python code using the pytest framework, covering unit testing, integration testing, and end-to-end testing.
Practical guide to using Python for bioinformatics tasks, covering sequence analysis, genome assembly, and data visualization.
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Comprehensive guide to using Python for financial analysis and modeling, covering data manipulation, financial calculations, and visualization.
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Concise and comprehensive reference to the Python language, covering syntax, built-in functions and objects, and advanced topics.
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Provides a comprehensive introduction to artificial intelligence, covering topics such as machine learning, natural language processing, and computer vision. It is suitable for students with a strong background in mathematics and computer science.
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Provides a comprehensive introduction to computer science, covering topics such as programming, data structures, and algorithms. It is suitable for students with a basic understanding of mathematics.
Provides a comprehensive introduction to mathematical statistics, covering topics such as probability, sampling, and hypothesis testing. It is suitable for students with a strong background in calculus.
Provides a comprehensive introduction to linear algebra, covering topics such as vector spaces, matrices, determinants, and eigenvalues. It is suitable for students with a basic understanding of algebra.
Provides a rigorous introduction to calculus, including topics such as limits, derivatives, integrals, and differential equations. It is suitable for students with a strong background in algebra and trigonometry.
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Provides a comprehensive overview of functions and graphs, covering topics such as linear functions, polynomials, rational functions, exponential functions, and logarithmic functions. It is suitable for students with a basic understanding of algebra.
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