Python (Computer program language)
Overview
| Works: | 250 works in 200 publications in 200 languages | |
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Titles
Text analysis in Python for social scientists : = discovery and exploration /
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Data visualization for social and policy research : = a step-by-step approach using R and Python /
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Text analysis in Python for social scientists : = prediction and classification /
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The art of reinforcement learning = fundamentals, mathematics, and implementations with Python /
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Introduction to responsible AI = implement ethical AI using Python /
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Quantitative trading strategies using Python = technical analysis, statistical testing, and machine learning /
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Machine learning for earth sciences = using Python to solve geological problems /
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Python for engineers and scientists : = concepts and applications /
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Introduction to synthetic aperture radar using Python and MATLAB /
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Dynamic system modelling and analysis with MATLAB and Python = for control engineers /
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Network programming in python = the basic : a detailed guide to python 3 network programming and management /
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Pandas in 7 days = utilize Python to manipulate data, conduct scientific computing, time series analysis, and exploratory data analysis /
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Enterprise automation with Python = automate Excel, web, documents, emails, and various workloads with easy-to-code Python scripts /
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Full stack development with MongoDB = covers backend, frontend, APIs, and mobile App development using PHP, NodeJS, ExpressJS, Python and react native /
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Programming techniques using Python = have fun and play with basic and advanced core Python /
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Test your skills in Python = an interactive way to introduce the world of computer programming /
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A journey to core Python = experience the applications of tuples, dictionary, lists, operators, loops, indexing, slicing, and matrices /
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An introduction to Python programming = a practical approach : using Python to solve complex problems with a burst of machine learning /
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Applied machine learning solutions with Python = production-ready ML projects using cutting-edge libraries and powerful statistical techniques /
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Python data visualization essentials guide = become a data visualization expert by building strong proficiency in Pandas, Matplotlib, Seaborn, Plotly, Numpy, and Bokeh /
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Advance core Python programming = begin your journey to master the world of Python /
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Practical data science with Jupyter = explore data cleaning, pre-processing, data wrangling, feature engineering and machine learning using Python and Jupyter /
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Machine learning cookbook with Python = create ML and data analytics projects using some amazing open datasets /
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Hands-on data analysis and visualization with pandas = engineer, analyse and visualize data, using powerful Python libraries /
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Machine learning for beginners = learn to build machine learning systems using Python /
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Hands-on supervised learning with Python = learn how to solve machine learning problems with supervised learning algorithms using Python /
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Python in-depth = use Python programming features, techniques, and modules to solve everyday problems /
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Python made simple = learn Python programming in easy steps with examples /
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Machine learning with Python = an approach to applied machine learning /
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Learning genetic algorithms with Python = empower the performance of machine learning and AI models with the capabilities of a powerful search algorithm /
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Introduction to prescriptive AI = a primer for decision intelligence solutioning with Python /
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An introduction to statistical learning = with applications in Python /
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Internet of things using single board computers = principles of IoT and Python programming /
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Physics with Excel and Python = using the same data structure.. Volume I,. Basics, exercises and tasks /
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Essentials of Excel VBA, Python, and R.. Volume I, . Financial statistics and portfolio analysis
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Django 4 by example : = build powerful and reliable python web applications from scratch /
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Dynamic system modelling and analysis with MATLAB and Python : = for control engineers /
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Programming PyTorch for deep learning : = creating and deploying deep learning applications /
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Make your first GAN with PyTorch : = a gentle introduction to generative adversarial networks, and a practical step-by-step tutorial on making your own with PyTorch /
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Deep learning for coders with fastai and PyTorch : = AI applications without a PhD /
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Deep learning : = a comprehensive guide to Python coding and programming machine learning and neural networks for data analysis /
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Machine learning with PyTorch and Scikit-Learn : = develop machine learning and deep learning models with Python /
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Hands-on Generative Adversarial Networks with PyTorch 1.x : = implement next-generation neural networks to build powerful GAN models using Python /
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Mastering PyTorch : = build powerful neural network architectures using advanced PyTorch 1.x features /
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Natural language processing with Flair : = a practical guide to understanding and solving NLP problems with Flair /
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Deep learning with PyTorch Lightning: = swiftly build high-performance Artificial Intelligence (AI) models using Python /
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Interpretable AI : = building explainable machine learning systems /
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Practical numerical and scientific computing with MATLAB and Python
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3D image reconstruction for CT and PET = a practical guide with Python /
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Embedded digital control with microcontrollers : = implementation with C and Python /
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Practical numerical and scientific computing with MATLAB and Python /
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Python machine learning by example : = build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn /
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Introduction to deep learning for engineers : = using python and google cloud platform /
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PyTorch artificial intelligence fundamentals : = a recipe-based approach to design, build and deploy your own AI models with PyTorch 1.x /
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OpenCV 4 with Python blueprints : = build creative computer vision projects with the latest version of OpenCV 4 and Python 3 /
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The data science workshop : = a new, interactive approach to learning data science /
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Artificial intelligence with Python : = your complete guide to building intelligent apps using Python 3.x and TensorFlow 2 /
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Interactive data visualization with Python : = present your data as an effective and compelling story /
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Natural language processing recipes : = unlocking text data with machine learning and deep learning using Python /
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Practical machine learning and image processing : = for facial recognition, object detection, and pattern recognition using Python /
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Data mining for business analytics : = concepts, techniques, and applications in Python /
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Advanced Python development : = using powerful language features in real-world applications /
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Python data analytics = data analysis and science using Pandas, matplotlib, and the Python programming language /
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Beginning Python visualization = crafting visual transformation scripts /
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Violent python = a cookbook for hackers, forensic analysts, penetration testers, and security engineers /
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Image processing using pulse-coupled neural networks = applications in Python /
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Practical computer vision with SimpleCV / Kurt Demaagd, ... [et al.].
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Soil physics with Python : = transport in the soil-plant-atmosphere system /
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Python image processing cookbook : = over 60 recipes to help you perform complex image processing and computer vision tasks with ease /
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PyTorch computer vision cookbook : = over 70 recipes to master the art of computer vision with deep learning and PyTorch 1.x /
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Pandas 1.x cookbook : = practical recipes for scientific computing, time series analysis, and exploratory data analysis using Python /
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Hands-on exploratory data analysis with Python : = perform EDA techniques to understand, summarize, and investigate your data /
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Django 3 web development cookbook : = actionable solutions to common problems in Python web development /
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Python forensics = a workbench for inventing and sharing digital forensic technology /
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Statistics, data mining, and machine learning in astronomy = a practical Python guide for the analysis of survey data /
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Machine learning in Python : = essential techniques for predictive analysis /
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Numeric computation and statistical data analysis on the Java platform
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Programming for computations - Python = a gentle introduction to numerical simulations with Python /
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Text analytics with Python = a practical real-world approach to gaining actionable insights from your data /
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Comparative approaches to using R and Python for statistical data analysis
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Listed volatility and variance derivatives : = a Python-based guide /
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Raspberry Pi supercomputing and scientific programming = MPI4PY, NumPy, and SciPy for enthusiasts /
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Learn Python 3 the hard way : = a very simple introduction to the terrifyingly beautiful world of computers and code /
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Derivatives analytics with Python = data analysis, models, simulation, calibration and hedging /
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Machine learning in Python = essential techniques for predictive analysis /
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Practical Python design patterns = Pythonic solutions to common problems /
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Beginning Django = web application development and deployment with Python /
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Python machine learning case studies = five case studies for the data scientist /
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A primer on process mining = practical skills with Python and Graphviz /
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The Blender Python API = precision 3D modeling and add-on development /
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Mastering machine learning with Python in six steps = a practical Implementation guide to predictive data analytics using Python /
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MicroPython for the internet of things = a beginner's guide to programming with Python on microcontrollers /
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Python for the busy Java developer = the language, syntax, and ecosystem /
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Python 2 and 3 compatibility = with Six and Python-future libraries /
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Deep learning with applications using Python = chatbots and face, object, and speech recognition with TensorFlow and Keras /
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Advanced data analytics using Python = with machine learning, deep learning and NLP examples /
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Good habits for great coding = improving programming skills with examples in Python /
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Practical web scraping for data science = best practices and examples with Python /
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Deep learning for natural language processing = creating neural networks with Python /
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Pro machine learning algorithms = a hands-on approach to implementing algorithms in Python and R /
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Practical Docker with Python = build, release and distribute your Python App with Docker /
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MySQL Connector/Python revealed = SQL and NoSQL data storage using MySQL for Python programmers /
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Learn Raspberry Pi programming with Python = learn to program on the world's most popular tiny computer /
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Personal finance with Python = using pandas, requests, and recurrent /
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Guide to programming for the digital humanities = lessons for introductory Python /
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Physics of oscillations and waves = with use of Matlab and Python /
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Practical video game bots = automating game processes using C++, Python, and AutoIt /
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Learn to program with Python 3 = a step-by-step guide to programming /
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Multiple criteria decision aid = methods, examples and Python implementations /
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Applied natural language processing with Python = implementing machine learning and deep learning algorithms for natural language processing /
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Monetizing machine learning = quickly turn Python ML ideas into web applications on the serverless cloud /
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Credit-risk modelling = theoretical foundations, diagnostic tools, practical examples, and numerical recipes in Python /
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Python descriptors = understanding and using the descriptor protocol /
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Block trace analysis and storage system optimization = a practical approach with MATLAB/Python tools /
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Data analysis and visualization using Python = analyze data to create visualizations for BI systems /
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Text analytics with Python = a practitioner's guide to natural language processing /
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Foundations of Libvirt development = how to set up and maintain a virtual machine environment with Python /
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The Python workbook = a brief introduction with exercises and solutions /
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Practical reinforcement learning : = develop self-evolving, intelligent agents with OpenAI Gym, Python, and Java /
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TensorFlow reinforcement learning quick start guide : = get up and running with training and deploying intelligent, self-learning agents using Python /
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Reinforcement learning algorithms with Python : = learn, understand, and develop smart algorithms for addressing AI challenges /
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Python reinforcement learning projects : = eight hands-on projects exploring reinforcement learning algorithms using TensorFlow /
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Computational nuclear engineering and radiological science using python /
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Python for data analysis : = data wrangling with Pandas, NumPy, and IPython /
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Python data science handbook : = essential tools for working with data /
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Machine learning pocket reference : = working with structured data in Python /
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Introduction to data science = a Python approach to concepts, techniques and applications /
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Python unit test automation = practical techniques for Python developers and testers /
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