Uczenie maszynowe
Uczenie maszynowe (ang. machine learning) zajmuje się teorią i praktycznym zastosowaniem algorytmów analizujących dane — stanowi najciekawszą dziedzinę informatyki. Żyjemy w czasach przetwarzania olbrzymiej ilości informacji; za pomocą samouczących się algorytmów będących częścią uczenia maszynowego informacje te są przekształcane w rzeczywistą wiedzę. Dzięki licznym i potężnym bibliotekom o jawnym kodzie źródłowym, które powstały w ostatnich latach, prawdopodobnie teraz jest najlepszy czas, aby zainteresować się uczeniem maszynowym i nauczyć się wykorzystywać potężne algorytmy do wykrywania wzorców w przetwarzanych danych oraz prognozować przyszłe zdarzenia. Przykładami zastosowania Machine Learning są np. mechanizmy wyszukiwarek internetowych, GPS, autokorekta w edytorze tekstu czy boty w komunikatorach.
Jedną z dziedzin uczenia maszynowego jest deep learning, podczas którego komputer uczy się procesów naturalnych dla ludzkiego mózgu (tworzy sieci neuronowe). Technologia ta jest wykorzystywana np. przy identyfikacji głosu i obrazów.
Książki, ebooki, kursy video z kategorii: Uczenie maszynowe dostępne w księgarni Ebookpoint
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AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide. The ultimate guide to passing the MLS-C01 exam on your first attempt - Second Edition
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Bayesian Analysis with Python. A practical guide to probabilistic modeling - Third Edition
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Praktyczne uczenie maszynowe
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Python w uczeniu maszynowym
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Hands-On Image Processing with Python. Expert techniques for advanced image analysis and effective interpretation of image data
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TensorFlow Developer Certificate Guide. Efficiently tackle deep learning and ML problems to ace the Developer Certificate exam
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Machine Learning for Emotion Analysis in Python. Build AI-powered tools for analyzing emotion using natural language processing and machine learning
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Machine Learning Using TensorFlow Cookbook. Create powerful machine learning algorithms with TensorFlow
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Machine Learning Design Patterns
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The Deep Learning Workshop. Learn the skills you need to develop your own next-generation deep learning models with TensorFlow and Keras
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The Unsupervised Learning Workshop. Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions
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Programming PyTorch for Deep Learning. Creating and Deploying Deep Learning Applications
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Natural Language Processing with PyTorch. Build Intelligent Language Applications Using Deep Learning
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Data Science i uczenie maszynowe
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Wnioskowanie przyczynowe w Pythonie. Praktyczne wykorzystanie w branży technologicznej
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Python Machine Learning By Example. Unlock machine learning best practices with real-world use cases - Fourth Edition
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Deep Learning at Scale
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Introduction to Algorithms. A Comprehensive Guide for Beginners: Unlocking Computational Thinking
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Data Analysis Foundations with Python. Master Data Analysis with Python: From Basics to Advanced Techniques
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De-Mystifying Math and Stats for Machine Learning. Mastering the Fundamentals of Mathematics and Statistics for Machine Learning
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Privacy-Preserving Machine Learning. A use-case-driven approach to building and protecting ML pipelines from privacy and security threats
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Databricks ML in Action. Learn how Databricks supports the entire ML lifecycle end to end from data ingestion to the model deployment
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Accelerate Model Training with PyTorch 2.X. Build more accurate models by boosting the model training process
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Active Machine Learning with Python. Refine and elevate data quality over quantity with active learning
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Machine Learning: Make Your Own Recommender System. Build Your Recommender System with Machine Learning Insights
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Machine Learning with Python. Unlocking AI Potential with Python and Machine Learning
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Hands-On Entity Resolution
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Data Labeling in Machine Learning with Python. Explore modern ways to prepare labeled data for training and fine-tuning ML and generative AI models
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Machine Learning Infrastructure and Best Practices for Software Engineers. Take your machine learning software from a prototype to a fully fledged software system
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MLOps with Red Hat OpenShift. A cloud-native approach to machine learning operations
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MATLAB for Machine Learning. Unlock the power of deep learning for swift and enhanced results - Second Edition
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Deep Learning with MXNet Cookbook. Discover an extensive collection of recipes for creating and implementing AI models on MXNet
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Practical Guide to Applied Conformal Prediction in Python. Learn and apply the best uncertainty frameworks to your industry applications
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Machine Learning Interviews
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Training Data for Machine Learning
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Machine Learning with Qlik Sense. Utilize different machine learning models in practical use cases by leveraging Qlik Sense
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The Statistics and Machine Learning with R Workshop. Unlock the power of efficient data science modeling with this hands-on guide
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Delta Lake: Up and Running
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Machine Learning with LightGBM and Python. A practitioner's guide to developing production-ready machine learning systems
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Graph-Powered Analytics and Machine Learning with TigerGraph
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Zaufanie do systemów sztucznej inteligencji
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Data Augmentation with Python. Enhance deep learning accuracy with data augmentation methods for image, text, audio, and tabular data
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Machine Learning for High-Risk Applications
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Computer Vision on AWS. Build and deploy real-world CV solutions with Amazon Rekognition, Lookout for Vision, and SageMaker
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Scaling Machine Learning with Spark
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Applied Geospatial Data Science with Python. Leverage geospatial data analysis and modeling to find unique solutions to environmental problems
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The Kaggle Workbook. Self-learning exercises and valuable insights for Kaggle data science competitions
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Democratizing Application Development with Betty Blocks. Build powerful applications that impact business immediately with no-code app development
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Practicing Trustworthy Machine Learning
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Transforming Healthcare with DevOps. A practical DevOps4Care guide to embracing the complexity of digital transformation
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Applied Machine Learning and AI for Engineers
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Hands-On Healthcare Data
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Machine Learning at Scale with H2O. A practical guide to building and deploying machine learning models on enterprise systems
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Natural Language Processing with TensorFlow. The definitive NLP book to implement the most sought-after machine learning models and tasks - Second Edition
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Tidy Modeling with R
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Machine Learning on Kubernetes. A practical handbook for building and using a complete open source machine learning platform on Kubernetes
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Designing Autonomous AI
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Practical Simulations for Machine Learning
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Building Data Science Solutions with Anaconda. A comprehensive starter guide to building robust and complete models
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Natural Language Processing with Transformers, Revised Edition
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Fundamentals of Deep Learning. 2nd Edition
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Mastering Azure Machine Learning. Execute large-scale end-to-end machine learning with Azure - Second Edition
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Distributed Machine Learning with Python. Accelerating model training and serving with distributed systems
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Deep Learning with PyTorch Lightning. Swiftly build high-performance Artificial Intelligence (AI) models using Python
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Natural Language Processing with Flair. A practical guide to understanding and solving NLP problems with Flair
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Essential Mathematics for Quantum Computing. A beginner's guide to just the math you need without needless complexities
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Automated Machine Learning on AWS. Fast-track the development of your production-ready machine learning applications the AWS way
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TinyML Cookbook. Combine artificial intelligence and ultra-low-power embedded devices to make the world smarter
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Getting Started with Amazon SageMaker Studio. Learn to build end-to-end machine learning projects in the SageMaker machine learning IDE
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Unity Artificial Intelligence Programming. Add powerful, believable, and fun AI entities in your game with the power of Unity - Fifth Edition
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Reproducible Data Science with Pachyderm. Learn how to build version-controlled, end-to-end data pipelines using Pachyderm 2.0
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Modern Mainframe Development
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Time Series Analysis on AWS. Learn how to build forecasting models and detect anomalies in your time series data
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Machine Learning in Biotechnology and Life Sciences. Build machine learning models using Python and deploy them on the cloud
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Intelligent Workloads at the Edge. Deliver cyber-physical outcomes with data and machine learning using AWS IoT Greengrass
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Agile Machine Learning with DataRobot. Automate each step of the machine learning life cycle, from understanding problems to delivering value
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The TensorFlow Workshop. A hands-on guide to building deep learning models from scratch using real-world datasets
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Machine Learning for Financial Risk Management with Python
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Azure Data Scientist Associate Certification Guide. A hands-on guide to machine learning in Azure and passing the Microsoft Certified DP-100 exam
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Learn Amazon SageMaker. A guide to building, training, and deploying machine learning models for developers and data scientists - Second Edition
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IBM Cloud Pak for Data. An enterprise platform to operationalize data, analytics, and AI
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Machine Learning Engineering with Python. Manage the production life cycle of machine learning models using MLOps with practical examples
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Machine Learning with Amazon SageMaker Cookbook. 80 proven recipes for data scientists and developers to perform machine learning experiments and deployments
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Machine Learning for Time-Series with Python. Forecast, predict, and detect anomalies with state-of-the-art machine learning methods
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Reliable Machine Learning
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Conversational AI with Rasa. Build, test, and deploy AI-powered, enterprise-grade virtual assistants and chatbots
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Practical Weak Supervision
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Deep Learning with fastai Cookbook. Leverage the easy-to-use fastai framework to unlock the power of deep learning
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Exploring GPT-3. An unofficial first look at the general-purpose language processing API from OpenAI
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Machine Learning Engineering with MLflow. Manage the end-to-end machine learning life cycle with MLflow
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Getting Started with Streamlit for Data Science. Create and deploy Streamlit web applications from scratch in Python
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Graph Machine Learning. Take graph data to the next level by applying machine learning techniques and algorithms
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Machine Learning with BigQuery ML. Create, execute, and improve machine learning models in BigQuery using standard SQL queries
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Machine Learning with the Elastic Stack. Gain valuable insights from your data with Elastic Stack's machine learning features - Second Edition
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Automated Machine Learning with AutoKeras. Deep learning made accessible for everyone with just few lines of coding
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PyTorch Pocket Reference
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Machine Learning Automation with TPOT. Build, validate, and deploy fully automated machine learning models with Python
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Automated Machine Learning with Microsoft Azure. Build highly accurate and scalable end-to-end AI solutions with Azure AutoML
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Engineering MLOps. Rapidly build, test, and manage production-ready machine learning life cycles at scale
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Interpretable Machine Learning with Python. Learn to build interpretable high-performance models with hands-on real-world examples
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AWS Certified Machine Learning Specialty: MLS-C01 Certification Guide. The definitive guide to passing the MLS-C01 exam on the very first attempt
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Automated Machine Learning. Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms
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Odsłaniamy SQL Server 2019: Klastry Big Data i uczenie maszynowe
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Kubeflow Operations Guide
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Practical Fairness
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Introducing MLOps
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Codeless Deep Learning with KNIME. Build, train, and deploy various deep neural network architectures using KNIME Analytics Platform
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Python Machine Learning By Example. Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn - Third Edition
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Microsoft Power BI Quick Start Guide. Bring your data to life through data modeling, visualization, digital storytelling, and more - Second Edition
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Artificial Intelligence in Finance
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Kubeflow for Machine Learning
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AI and Machine Learning for Coders
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Wprowadzenie do uczenia maszynowego według Esposito
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Deep Learning for Beginners. A beginner's guide to getting up and running with deep learning from scratch using Python
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The Natural Language Processing Workshop. Confidently design and build your own NLP projects with this easy-to-understand practical guide
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Applied Deep Learning and Computer Vision for Self-Driving Cars. Build autonomous vehicles using deep neural networks and behavior-cloning techniques
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Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits. A practical guide to implementing supervised and unsupervised machine learning algorithms in Python
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The Deep Learning with PyTorch Workshop. Build deep neural networks and artificial intelligence applications with PyTorch
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Hands-On Simulation Modeling with Python. Develop simulation models to get accurate results and enhance decision-making processes
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Hands-On Mathematics for Deep Learning. Build a solid mathematical foundation for training efficient deep neural networks