Python (Computer program language)
Overview
| Works: | 405 works in 346 publications in 346 languages | |
|---|---|---|
Titles
Beginning Python visualization = crafting visual transformation scripts /
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(Electronic resources)
Soil physics with Python : = transport in the soil-plant-atmosphere system /
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(Language materials, printed)
Elementary mechanics using Python = a modern course combining analytical and numerical techniques /
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(Electronic resources)
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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Practical Python AI projects = mathematical models of optimization problems with Google OR-tools /
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Applied neural networks with TensorFlow 2 = API oriented deep learning with Python /
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Practical natural language processing with Python = with case studies from industries using text data at scale /
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(Electronic resources)
Beginning Python visualization = crafting visual transformation scripts /
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(Language materials, printed)
Foundations of Python network programming = the comprehensive guide to building network applications with Python /
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Violent python DUP_1 = a cookbook for hackers, forensic analysts, penetration testers, and security engineers /
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Python data analytics = data analysis and science using Pandas, matplotlib, and the Python programming language /
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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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Computational modeling and visualization of physical systems with python /
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(Language materials, printed)
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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Raspberry Pi image processing programming = develop real-life examples with Python, Pillow, and SciPy /
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Comparative approaches to using R and Python for statistical data analysis
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Raspberry Pi supercomputing and scientific programming = MPI4PY, NumPy, and SciPy for enthusiasts /
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Practical convolutional neural networks : = implement advanced deep learning models using Python /
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Introduction to machine learning with Python : = a guide for data scientists /
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Learning Scrapy : = learn the art of efficient web scraping and crawling with Python /
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Python machine learning : = machine learning and deep learning with Python, scikit-learn, and TensorFlow /
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Web scraping with Python : = collecting more data from the modern web /
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TensorFlow 1.x deep learning cookbook : = over 90 unique recipes to solve artificial-intelligence driven problems with Python /
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Web scraping with Python : = scrape data from any website with the power of Python /
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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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Introduction to Python for engineers and scientists = open source solutions for numerical computation /
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Practical machine learning with Python = a problem-solver's guide to building real-world intelligent systems /
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Simulating nonlinear circuits with Python power electronics = an open-source simulator, based on PythonTM /
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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 with PyTorch : = a practical approach to building neural network models using PyTorch /
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Machine learning applications using Python = cases studies from healthcare, retail, and finance /
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Building chatbots with Python = using natural language processing and machine learning /
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Machine learning with PySpark = with natural language processing and recommender systems /
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Numerical Python = scientific computing and data science applications with Numpy, SciPy and Matplotlib /
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Python continuous integration and delivery = a concise guide with examples /
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Natural language processing recipes = unlocking text data with machine learning and deep learning using python /
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Computational nuclear engineering and radiological science using python /
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Text analytics with Python = a practitioner's guide to natural language processing /
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Practical data science with Python 3 = synthesizing actionable insights from data /
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Practical cryptography in Python = learning correct cryptography by example /
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Python for the life sciences = a gentle introduction to python for life scientists /
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Advanced applied deep learning = convolutional neural networks and object detection /
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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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Python projects for beginners = a ten-week bootcamp approach to Python programming /
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Hands-on Scikit-Learn for machine learning applications = data science fundamentals with Python /
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Game programming with Code Angel = learn how to code in Python on Raspberry Pi or PC /
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Deep neuro-fuzzy systems with Python = with case studies and applications from the industry /
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Hands on Google Cloud SQL and Cloud Spanner = deployment, administration and use cases with Python /
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Beginning game programming with Pygame Zero = coding interactive games on Raspberry Pi using Python /
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Beginning anomaly detection using Python-based deep learning = with Keras and PyTorch /
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Building Android apps in Python using Kivy with Android Studio = with Pyjnius, Plyer, and Buildozer /
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Sensor projects with Raspberry Pi = internet of things and digital image processing /
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Thinking in Pandas = how to use the Python data analysis library the right way /
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Ensemble learning for AI developers = learn bagging, stacking, and boosting methods with use cases /
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Advanced Python development = using powerful language features in real-world applications /
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Statistical learning with math and Python = 100 exercises for building logic /
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Python crash course : = a hands-on, project-based introduction to programming /
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Practical Python data visualization = a fast track approach to learning data visualization with Python /
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Modern PyQt = create GUI applications for project management, computer vision, and data analysis /
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Deploy machine learning models to production = with Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform /
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Applied data science using Pyspark = learn the end-to-end predictive model-building cycle /
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Ontologies with Python = programming OWL 2. 0 ontologies with Python and Owlready2 /
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A Python data analyst's toolkit = learn Python and Python-based libraries with applications in data analysis and statistics /
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Analog circuit simulators for integrated circuit designers = numerical recipes in Python /
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Introduction to machine learning in the cloud with Python = concepts and practices /
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Practical machine learning for streaming data with Python = design, develop, and validate online learning models /
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Deep learning with Python = learn best practices of deep learning models with PyTorch /
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Applied reinforcement learning with Python = with OpenAI Gym, Tensorflow, and Keras /
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Learn PySpark = build Python-based machine learning and deep learning models /
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Machine learning concepts with Python and the Jupyter Notebook environment = using Tensorflow 2.0 /
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Supervised learning with Python = concepts and practical implementation using Python /
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Data science with Raspberry Pi = real-time applications using a localized cloud /
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Advanced forecasting with Python = with state-of-the-art-models including LSTMs, Facebook's Prophet, and Amazon's DeepAR /
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Introduction to deep learning for engineers = using python and google cloud platform /
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Natural language processing recipes = unlocking text data with machine learning and deep learning using Python /
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Basic Python for data management, finance, and marketing = advance your career by learning the most powerful analytical tool /
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Programming for computations - Python = a gentle introduction to numerical simulations with Python /
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Programming for computations = Python : a gentle introduction to numerical simulations with Python 3.6 /
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Essentials of Excel VBA, Python, and R.. Volume I, . Financial statistics and portfolio 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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Pandas in 7 days = utilize Python to manipulate data, conduct scientific computing, time series analysis, and exploratory data analysis /
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Network programming in python = the basic : a detailed guide to python 3 network programming and management /
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Learn coding with Python and JavaScript = a practical introduction for beginners /
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Introduction to Python in earth science data analysis = from descriptive statistics to machine learning /
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The Python Workbook = a brief introduction with exercises and solutions /
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Python forensics = a workbench for inventing and sharing digital forensic technology /
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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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Text analytics with Python = a practical real-world approach to gaining actionable insights from your data /
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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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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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Practical machine learning and image processing = for facial recognition, object detection, and pattern recognition using Python /
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Foundations of Libvirt development = how to set up and maintain a virtual machine environment with Python /
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Quantum machine learning with Python = using Cirq from Google Research and IBM Qiskit /
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Deep reinforcement learning with Python = with PyTorch, TensorFlow and OpenAI Gym /
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Programming microcontrollers with Python = experience the power of embedded Python /
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Transactional machine learning with data streams and AutoML = build frictionless and elastic machine learning solutions with Apache Kafka in the cloud using Python /
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Numerical Python in astronomy and astrophysics = a practical guide to astrophysical problem solving /
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Introduction to computational thinking = problem solving, algorithms, data structures, and more /
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Foundation Db2 and Python = access Db2 with module-based API examples using Python /
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Optimizing Visual Studio Code for Python development = developing more efficient and effective programs in Python /
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Sparse estimation with math and python = 100 exercises for building logic /
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The definitive guide to Masonite = building web applications with Python /
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Foundation dynamic web pages with Python = create dynamic web pages with Django and Flask /
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Advanced analytics in power BI with R and Python = ingesting, transforming, visualizing /
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Digital filter design using Python for power engineering applications = an open source guide /
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Machine learning in the oil and gas industry = including geosciences, reservoir engineering, and production engineering with Python /
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Building versatile mobile apps with Python and REST = RESTful web services with Django and react /
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Extending Power BI with Python and R : = ingest, transform, enrich and visualize data using the power of analytic languages /
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Machine learning on geographical data using Python : = introduction into geodata with applications and use cases /
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Practical channel-aware resource allocation = with MATLAB and Python code /
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Algorithmic decision making with Python resources = from multicriteria performance records to decision algorithms via bipolar-valued outranking digraphs /
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Music representation and transformation in software = structure and algorithms in Python /
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Kernel methods for machine learning with Math and Python = 100 exercises for building logic /
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Human-computer interaction in game development with Python = design and develop a game interface using HCI technologies and techniques /
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Automated deep learning using neural network intelligence = develop and design pytorch and tensorflow models using python /
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Productive and efficient data science with Python = with modularizing, memory profiles, and parallel/GPU processing /
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Machine learning on geographical data using Python = introduction into geodata with applications and use cases /
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Beginning MicroPython with the Raspberry Pi Pico = build electronics and IoT projects /
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Raspberry Pi image processing programming = with NumPy, SciPy, Matplotlib and OpenCV /
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Simulation with Python = develop simulation and modeling in natural sciences, engineering, and social sciences /
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Web application development with Streamlit = develop and deploy secure and scalable web applications to the cloud using a pure Python framework /
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Foundations of robotics = a multidisciplinary approach with Python and ROS /
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Data analysis with machine learning for psychologists = crash course to learn Python 3 and machine learning in 10 hours /
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The absolute beginner's guide to Python programming = a step-by-step guide with examples and lab exercises /
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Secure web application development = a hands-on guide with Python and Django /
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An introduction to statistics with Python = with applications in the life sciences /
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Finding ghosts in your data = anomaly detection techniques with examples in Python /
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Physics with Excel and Python = using the same data structure.. Volume I,. Basics, exercises and tasks /
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Internet of things using single board computers = principles of IoT and Python programming /
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Head first Python : = [a learner's guide to the fundamentals of Python programming] /
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(Language materials, printed)
Applied Recommender Systems with Python = Build Recommender Systems with Deep Learning, NLP and Graph-Based Techniques /
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Advanced data analytics using Python = with architectural patterns, text and image classification, and optimization techniques /
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PyTorch recipes = a problem-solution approach to build, train and deploy neural network models /
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(Electronic resources)
Beginner's guide to Streamlit with Python = build web-based data and machine learning applications /
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Time series algorithms recipes = implement machine learning and deep learning techniques with Python /
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Productionizing AI = how to deliver AI B2B solutions with Cloud and Python /
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Reinforcement learning for finance = solve problems in finance with CNN and RNN using the tensorflow library /
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Explainable AI recipes = implement solutions to model explainability and interpretability with Python /
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(Electronic resources)
Essentials of Excel VBA, Python, and R.. Volume II,. Financial derivatives, risk management and machine learning
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(Electronic resources)
Introduction to prescriptive AI = a primer for decision intelligence solutioning with Python /
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Machine learning for earth sciences = using Python to solve geological problems /
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Quantitative trading strategies using Python = technical analysis, statistical testing, and machine learning /
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Dynamic system modelling and analysis with MATLAB and Python = for control engineers /
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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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Python made simple = learn Python programming in easy steps with examples /
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Python in-depth = use Python programming features, techniques, and modules to solve everyday problems /
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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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Machine learning for beginners = learn to build machine learning systems using Python /
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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 cookbook with Python = create ML and data analytics projects using some amazing open datasets /
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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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Advance core Python programming = begin your journey to master the world of Python /
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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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Applied machine learning solutions with Python = production-ready ML projects using cutting-edge libraries and powerful statistical techniques /
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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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A journey to core Python = experience the applications of tuples, dictionary, lists, operators, loops, indexing, slicing, and matrices /
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Test your skills in Python = an interactive way to introduce the world of computer programming /
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Programming techniques using Python = have fun and play with basic and advanced core Python /
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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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Python debugging for AI, machine learning, and cloud computing = a pattern-oriented approach /
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Quantum computing by practice = Python programming in the cloud with Qiskit and IBM-Q /
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Beginning anomaly detection using python-based deep learning = implement anomaly detection applications with Keras and PyTorch /
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(Electronic resources)
Data structures and algorithms with Python = with an introduction to multiprocessing /
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(Electronic resources)
MicroPython for the Internet of Things = a beginner's guide to programming with Python on microcontrollers /
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(Electronic resources)
Prototyping Python dashboards for scientists and engineers = build and deploy a complete dashboard with Python /
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(Electronic resources)
Introduction to data science = a python approach to concepts, techniques and applications /
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(Electronic resources)
Introduction to Python network automation. = the essential skills for growth /. Volume I,. Laying the groundwork
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Python for accounting and finance = an integrative approach to using Python for research /
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Python for natural language processing = programming with NumPy, scikit-learn, Keras, and PyTorch /
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(Electronic resources)
Text analysis in Python for social scientists = prediction and classification /
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(Electronic resources)
Text analysis in Python for social scientists = discovery and exploration /
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The art of reinforcement learning = fundamentals, mathematics, and implementations with Python /
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Practical blockchains and cryptocurrencies = speed up your application development process and develop distributed applications with confidence /
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(Electronic resources)
Hands-on time series analysis with Python = from basics to bleeding edge techniques /
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(Electronic resources)
Practical explainable AI using Python = artificial intelligence model explanations using Python-based libraries, extensions, and frameworks /
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(Electronic resources)
Data science solutions with Python = fast and scalable models using Keras, Pyspark Mllib, H2O, XGBoost, and scikit-Learn /
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C and Python applications = embedding Python code in C programs, SQL methods, and Python sockets /
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Machine learning with Pyspark = with natural language processing and recommender systems /
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(Electronic resources)
Web app development and real-time web analytics with Python = develop and integrate machine learning algorithms into web apps /
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(Electronic resources)
Practical Docker with Python = build, release, and distribute your Python app with Docker /
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(Electronic resources)
Python unit test automation = automate, organize, and execute unit tests in Python /
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(Electronic resources)
Hands-on machine learning with Python = implement neural network solutions with Scikit-learn and PyTorch /
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(Electronic resources)
Python for MATLAB development = extend MATLAB with 300,000+ modules from the Python package index /
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(Electronic resources)
Adaptive machine learning algorithms with Python = solve data analytics and machine learning problems on edge devices /
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(Electronic resources)
Applied deep learning with TensorFlow 2 = learn to implement advanced deep learning techniques with Python /
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(Electronic resources)
Data-driven SEO with Python = solve SEO challenges with data science using Python /
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(Electronic resources)
High-performance web apps with FastAPI = the asynchronous web framework based on modern Python /
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(Electronic resources)
Practical business analytics using R and Python = solve business problems using a data-driven approach /
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(Electronic resources)
Micro:bit projects with Python and single board computers = building steam projects with code club and kids' maker groups /
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(Electronic resources)
Data visualization for social and policy research = a step-by-step approach using R and Python /
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(Electronic resources)
Practical time series analysis : = prediction with statistics and machine learning /
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(Language materials, printed)
Data and process visualisation for graphic communication = a hands-on approach with python /
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(Electronic resources)
Handbook of regression modeling in people analytics = with examples in R and Python /
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(Electronic resources)
Deep reinforcement learning with Python = RLHF for chatbots and large language models /
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(Electronic resources)
Introduction to Python network automation.. Volume II,. Stepping up : beyond the essentials for success
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(Electronic resources)
Numerical Python = scientific computing and data science applications with Numpy, SciPy and Matplotlib /
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(Electronic resources)
Data engineering for machine learning pipelines = from Python libraries to ML pipelines and cloud platforms /
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(Electronic resources)
Advanced interdisciplinary applications of machine learning Python libraries for data science
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Introduction to Python and large language models = a guide to language models /
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(Electronic resources)
Stochastic finance with Python = design financial models from probabilistic perspective /
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(Electronic resources)
Generative AI apps with LangChain and Python = a project-based approach to building real-world LLM apps /
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(Electronic resources)
Sensor projects with Raspberry Pi = internet of things and digital image processing /
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(Electronic resources)
Beginning ChatGPT for Python = build intelligent applications with OpenAI APIs /
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(Electronic resources)
Applied data science using PySpark = learn the end-to-end predictive model-building cycle /
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(Electronic resources)
Energy system modeling and optimization = a practical guide using Pyomo /
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(Electronic resources)
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