Machine learning
Resource Information
The concept Machine learning represents the subject, aboutness, idea or notion of resources found in Sydney Jones Library, University of Liverpool.
The Resource
Machine learning
Resource Information
The concept Machine learning represents the subject, aboutness, idea or notion of resources found in Sydney Jones Library, University of Liverpool.
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- Machine learning
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- http://id.worldcat.org/fast/01004795
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- fast
671 Items that share the Concept Machine learning
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- 3D shape analysis : fundamentals, theory, and applications
- 5 questions on artificial intelligence
- 5 questions on artificial intelligence
- 6 Trends Framing the State of AI and ML
- A framework to bootstrap and scale a machine learning function
- A practical guide to algorithmic bias and explainability in machine learning
- AI and Machine Learning for Coders
- AI and Machine Learning for on-Device Development : a programmer's guide
- AI and deep learning for NLP : tools and techniques for the enterprise
- AI and machine learning for healthcare : an overview of tools and challenges for building a health-tech data pipeline
- AI and the index management problem
- AI as a Service
- AI for finance
- AUTOMATED MACHINE LEARNING : hyperparameter optimization, neural architecture search, and... algorithm selection with cloud platforms
- Achieving real business outcomes from artificial intelligence : enterprise considerations for AI initiatives
- Actionable Insights with Amazon QuickSight : Develop Stunning Data Visualizations and Machine Learning-Driven Insights with Amazon QuickSight
- Advanced R statistical programming and data models : analysis, machine learning, and visualization
- Advanced deep learning with Keras : apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more
- Advanced deep learning with Python
- Advanced deep learning with R : become an expert at designing, building, and improving advanced neural network models using R
- Advanced machine learning
- Advanced machine learning with Python : solve challenging data science problems by mastering cutting-edge machine learning techniques in Python
- Advanced machine learning with scikit-learn : tools and techniques for predictive analytics in Python
- Advanced model deployments with TensorFlow serving
- Advanced statistics and data mining for data science
- Advances in domain adaptation theory
- Advances in financial machine learning
- Advancing our understanding of deep reinforcement learning with community-driven insights
- Agile machine learning : effective machine learning inspired by the agile manifesto
- Agricultural informatics : automation using the IoT and machine learning
- Algorithmic recommendations at The New York Times
- Amazon SageMaker Best Practices : Proven Tips and Tricks to Build Successful Machine Learning Solutions on Amazon SageMaker
- Amazon machine learning
- An introduction to machine learning interpretability : an applied perspective on fairness, accountability, transparency, and explainable AI
- An introduction to machine learning models in production : how to transition from one-off models to reproducible pipelines
- Analyzing and visualizing data with F#
- Apache Spark 2 data processing and real-time analytics : master complex big data processing, stream analytics, and machine learning with Apache
- Apache Spark 2.x machine learning cookbook : over 100 recipes to simplify machine learning model implementations with Spark
- Apache Spark deep learning cookbook : over 80 recipes that streamline deep learning in a distributed environment with Apache Spark
- Apache Spark machine learning blueprints : develop a range of cutting-edge machine learning projects with Apache Spark using this actionable guide
- Apache Spark quick start guide : quickly learn the art of writing efficient big data applications with Apache Spark
- Applications of embeddings and deep learning at Groupon
- Applied analytics through case studies using SAS and R : implementing predictive models and machine learning techniques
- Applied data science with Python and Jupyter
- Applied deep learning : a case-based approach to understanding neural networks
- Applied deep learning and computer vision for self-driving cars : build autonomous vehicles using deep neural networks and behavior-cloning techniques
- Applied deep learning with Keras : solve complex real-life problems with the simplicity of Keras
- Applied machine learning for spreading financial statements
- Applied supervised learning with R : use machine learning libraries of R to build models that solve business problems and predict future trends
- Applied text analysis with Python : enabling language-aware data products with machine learning
- Applied unsupervised learning with Python : discover hidden patterns and relationships in unstructured data with Python
- Applied unsupervised learning with R
- Artificial Intelligence Business : How you can profit from AI
- Artificial Intelligence By Example - Second Edition
- Artificial Intelligence Conference 2019 : New York, New York
- Artificial and human intelligence in healthcare
- Artificial intelligence : the simplest way
- Artificial intelligence and machine learning fundamentals
- Artificial intelligence and machine learning fundamentals
- Artificial intelligence and machine learning in industry : perspectives from leading practitioners
- Artificial intelligence in 3 hours
- Artificial intelligence now : current perspectives from O'Reilly Media
- Artificial intelligence on human behavior : new insights into customer segmentation
- Automated Machine Learning with AutoKeras : Deep Learning Made Accessible for Everyone with Just Few Lines of Coding
- Automating DevOps for machine learning
- Autonomous learning systems : from data streams to knowledge in real-time
- Avoiding the pitfalls of deep learning : solving model overfitting with regularization and dropout
- Azure masterclass : manage Azure cloud with ARM templates
- BUILDING AN EFFECTIVE DATA SCIENCE PRACTICE : a framework to bootstrap and manage a successful... data science practice
- Becoming a Data Head : How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning
- Beginning AI bot frameworks : getting started with bot development
- Beginning MATLAB and Simulink : from novice to professional
- Beginning data science with Python and Jupyter
- Beginning machine learning with AWS
- Best practices for bringing AI to the enterprise
- Big data analytics for intelligent healthcare management
- Big data analytics using Apache Spark
- Big data and machine learning in quantitative investment
- Blockchain and machine learning for e-healthcare systems
- Bringing data to life : combining machine learning and art to tell a data story
- Build more inclusive TensorFlow pipelines with fairness indicators
- Building Recommender systems with machine learning and AI
- Building a big data analytics stack
- Building a recommendation system with R : learn the art of building robust and powerful recommendation engines using R
- Building advanced OpenCV 3 projects with Python
- Building and managing training datasets for ML with Snorkel
- Building enterprise data products
- Building intelligent cloud applications : develop scalable models using serverless architectures with Azure
- Building machine learning pipelines : automating model life cycles with TensorFlow
- Building machine learning powered applications : going from idea to product
- Business data science : combining machine learning and economics to optimize, automate, and accelerate business decisions
- Business forecasting : the emerging role of artificial intelligence and machine learning
- Can data science help us find what makes a hit television show
- Challenges in machine learning from model building to deployment at scale
- Clojure for data science : statistics, big data, and machine learning for Clojure programmers
- Clustering and unsupervised learning, Part 4, Introduction to real-world machine learning
- Cognitive computing with IBM Watson : build smart applications using artificial intelligence as a service
- Computational intelligence in business analytics : concepts, methods, and tools for big data applications
- Computational trust models and machine learning
- Computer vision projects with OpenCV and Python 3 : six end-to-end projects build using machine learning with OpenCV, Python, and TensorFlow
- Considering TensorFlow for the enterprise : an overview of the deep learning ecosystem
- Customizing state-of-the-art deep learning models for new computer vision solutions
- Data Mining and Machine Learning in Cybersecurity
- Data Science Solutions with Python : Fast and Scalable Models Using Keras, Pyspark MLlib, H2O, XGBoost, and Scikit-Learn
- Data analysis with Python : a modern approach
- Data analytics and machine learning fundamentals : LiveLessons
- Data analytics made easy : use machine learning and data storytelling in your work without writing... any code
- Data and social good : using data science to improve lives, fight injustice, and support democracy
- Data mining and machine learning applications
- Data mining and machine learning in cybersecurity
- Data pipelines with Apache Airflow
- Data science algorithms in a week : top 7 algorithms for scientific computing, data analysis, and machine learning
- Data science and engineering at enterprise scale : notebook-driven results and analysis
- Data science and machine learning with Python--Hands on!
- Data science fundamentals, Part 1, Learning basic concepts, data wrangling, and databases with Python
- Data science fundamentals, Part 2, Machine learning and statistical analysis
- Data science in the cloud with Microsoft Azure machine learning and Python
- Data science in the cloud with Microsoft Azure machine learning and R : 2015 update
- Data science isn't just another job
- Data science projects with Python : a case study approach to gaining valuable insights from real data with machine learning
- Data science with Microsoft Azure and R
- Data science with Python : combine Python with machine learning principles to discover hidden patterns in raw data
- Data statistics with full stack Python
- Data visualization recipes in Python
- Dealing with real-world data, Part 1, Introduction to real-world machine learning
- Deep Learning - Grundlagen und Implementierung : Neuronale Netze mit Python und PyTorch programmieren
- Deep Learning Patterns and Practices
- Deep Learning for Beginners
- Deep Learning for Natural Language Processing : Solve Your Natural Language Processing Problems with Smart Deep Neural Networks
- Deep Learning für die Biowissenschaften : Einsatz von Deep Learning in Genomik, Biophysik, Mikroskopie und medizinischer Analyse
- Deep Learning illustriert
- Deep Learning mit Python und Keras : Das Praxis-Handbuch vom Entwickler der Keras-Bibliothek
- Deep Learning with JavaScript
- Deep Learning with PyTorch
- Deep Learning with Python, Second Edition
- Deep Learning with TensorFlow : Explore neural networks and build intelligent systems with Python, 2nd Edition
- Deep Reinforcement Learning in Action
- Deep learning
- Deep learning : a practitioner's approach
- Deep learning : a visual approach
- Deep learning : das umfassende Handbuch : Grundlagen, aktuelle Verfahren und Algorithmen, neue Forschungsansätze
- Deep learning : moving toward artificial intelligence with neural networks and machine learning
- Deep learning : practical neural networks with Java : build and run intelligent applications by leveraging key Java machine learning libraries : a course in three modules
- Deep learning Kochbuch : Praxisrezepte für einen schnellen Einstieg
- Deep learning and the game of Go
- Deep learning cookbook : practical recipes to get started quickly
- Deep learning crash course
- Deep learning for computer vision with SAS : an introduction
- Deep learning for dummies
- Deep learning for natural language processing : applications of deep neural networks to machine learning tasks
- Deep learning for natural language processing : creating neural networks with Python
- Deep learning for numerical applications with SAS
- Deep learning for recommender systems, or How to compare pears with apples
- Deep learning for search
- Deep learning for strategic decision makers : understanding deep learning and how it produces business value
- Deep learning for time series data
- Deep learning from scratch : building with Python from first principles
- Deep learning illustrated : a visual, interactive guide to artificial intelligence
- Deep learning mit R und Keras : Das Praxis-Handbuch : von Entwicklern von Keras und RStudio
- Deep learning receptury
- Deep learning with Keras : implement neural networks with Keras on Theano and TensorFlow
- Deep learning with PyTorch
- Deep learning with PyTorch
- Deep learning with PyTorch : a practical approach to building neural network models using PyTorch
- Deep learning with PyTorch quick start guide : learn to train and deploy neural network models in Python
- Deep learning with Python
- Deep learning with Python video edition
- Deep learning with R
- Deep learning with R cookbook : over 45 unique recipes to delve into neural network techniques using R 3.5x
- Deep learning with R for beginners : design neural network models in R 3.5 using TensorFlow, Keras, and MXNet
- Deep learning with R in motion
- Deep learning with TensorFlow
- Deep learning with TensorFlow 2 and Keras : regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API
- Deep learning with TensorFlow : take your machine learning knowledge to the next level with the power of TensorFlow
- Deep learning with fastai cookbook : leverage the easy-to-use fastai framework to unlock the power of deep learning
- Deep learning with structured data
- Deep reinforcement learning and GANS Livelessons
- Deep reinforcement learning hands-on : apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more
- Demystifying big data, machine learning, and deep learning for healthcare analytics
- Deploying Spark ML pipelines in production on AWS : how to publish pipeline artifacts and run pipelines in production
- Deploying machine learning models as microservices using Docker : a REST-based architecture for serving ML model outputs at scale
- Developing an image classifier using TensorFlow : convolutional neural networks
- END-TO-END DATA SCIENCE WITH SAS : a hands-on programming guide;a hands-on programming guide
- Effective Amazon machine learning : machine learning in the Cloud
- Effective enterprise architecture
- Einführung in Machine Learning mit Python : Praxiswissen Data Science
- Einführung in TensorFlow : Deep-Learning-Systeme programmieren, trainieren, skalieren und deployen
- Enhance recommendations in Uber Eats with graph convolutional networks
- Ensemble machine learning cookbook : over 35 practical recipes to explore ensemble machine learning techniques using Python
- Ethics and data science
- Evaluating machine learning models : a beginner's guide to key concepts and pitfalls
- Executive briefing : an age of embeddings
- Executive briefing : explaining machine learning models
- Executive briefing : similar but different : delivering software with AI
- Executive briefing : usable machine learning - lessons from Stanford and beyond
- Executive briefing : why machine-learned models crash and burn in production and what to do about it
- Fast and lean data science with TPUs
- Fast data with the KISSS stack
- Feature engineering for machine learning : principles and techniques for data scientists
- Feature engineering made easy : identify unique features from your dataset in order to build powerful machine learning systems
- Foundations of deep reinforcement learning : theory and practice in Python
- Foundations of genetic algorithms 2
- Fraud detection without feature engineering
- Fundamentals and methods of machine and deep learning : algorithms, tools and applications
- Fundamentals of deep learning : designing next-generation machine intelligence algorithms
- GANs mit PyTorch selbst programmieren
- Game engines and machine learning
- Game theory and machine learning for cyber security
- Generative adversarial networks cookbook : over 100 recipes to build generative models using Python, TensorFlow, and Keras
- Generative adversarial networks projects : build next-generation generative models using TensorFlow and Keras
- Generative deep learning : teaching machines to paint, write, compose, and play
- Generative malware outbreak detection
- Generatives Deep Learning : Maschinen das Malen, Schreiben und Komponieren beibringen
- Genetic algorithms and machine learning for programmers : create AI models and evolve solutions
- Getting involved in the TensorFlow community
- Getting started with SAS Enterprise Miner for machine learning : learning to perform segmentation and predictive modeling
- Getting started with TensorFlow
- Getting started with artificial intelligence : a practical guide to building enterprise applications
- Getting started with deep learning
- Getting started with machine learning in Python
- Getting started with machine learning in R
- Getting started with machine learning in the cloud : using cloud-based platforms to discover new business insights
- Go machine learning projects : eight projects demonstrating end-to-end machine learning and predictive analytics applications in Go
- Google BigQuery : the definitive guide : data warehousing, analytics, and machine learning at scale
- Graph Machine Learning : Take Graph Data to the Next Level by Applying Machine Learning Techniques and Algorithms
- Graph-Powered Machine Learning
- Grokking Machine Learning
- Grokking deep learning
- Grokking deep learning in motion
- Guide to Deep Learning Basics : Logical, Historical and Philosophical Perspectives
- Hands-On Artificial Intelligence for Banking
- Hands-On Neural Networks with TensorFlow 2.0
- Hands-on Markov models with Python : implement probabilistic models for learning complex data sequences using Python ecosystem
- Hands-on Q-learning with Python : practical Q-learning with OpenAI Gym, Keras, and TensorFlow
- Hands-on Scikit-Learn for machine learning applications : data science fundamentals with Python
- Hands-on TensorFlow Lite for intelligent mobile apps
- Hands-on artificial intelligence for IoT : expert machine learning and deep learning techniques for developing smarter IoT systems
- Hands-on artificial intelligence for beginners : an introduction to AI concepts, algorithms, and their implementation
- Hands-on artificial intelligence for cybersecurity : implement smart AI systems for preventing cyber attacks and detecting threats and network anomalies
- Hands-on artificial intelligence for search : building intelligent applications and perform enterprise searches
- Hands-on artificial intelligence on Amazon Web Services : decrease the time to market for AI and ML applications with the power of AWS
- Hands-on automated machine learning : a beginner's guide to building automated machine learning systems using AutoML and Python
- Hands-on convolutional neural networks with TensorFlow : solve computer vision problems with modeling in TensorFlow and Python
- Hands-on data analysis with Pandas : efficiently perform data collection, wrangling, analysis, and visualization using Python
- Hands-on data science and Python machine learning : perform data mining and machine learning efficiently using Python and Spark
- Hands-on data science with Anaconda : utilize the right mix of tools to create high-performance data science applications
- Hands-on deep learning for images with TensorFlow : build intelligent computer vision applications using TensorFlow and Keras
- Hands-on deep learning with Apache Spark : build and deploy distributed deep learning applications on Apache Spark
- Hands-on deep learning with Caffe2
- Hands-on deep learning with TensorFlow : uncover what is underneath your data!
- Hands-on ensemble learning with Python : build highly optimized ensemble machine learning models using scikit-learn and Keras
- Hands-on ensemble learning with R : a beginner's guide to combining the power of machine learning algorithms using ensemble techniques
- Hands-on generative adversarial networks with Keras : your guide to implementing next-generation generative adversarial networks
- Hands-on image processing with Python : expert techniques for advanced image analysis and effective interpretation of image data
- Hands-on intelligent agents with OpenAI Gym : a step-by-step guide to develop AI agents using deep reinforcement learning
- Hands-on machine learning for cybersecurity : safeguard your system by making your machines intelligent using the Python ecosystem
- Hands-on machine learning for data mining
- Hands-on machine learning on Google cloud platform : implementing smart and efficient analytics using Cloud ML Engine
- Hands-on machine learning using JavaScript
- Hands-on machine learning with Azure : build powerful models with cognitive machine learning and artificial intelligence
- Hands-on machine learning with IBM Watson : leverage IBM Watson to implement machine learning techniques and algorithms using Python
- Hands-on machine learning with JavaScript : solve complex computational web problems using machine learning
- Hands-on machine learning with Scala and Spark
- Hands-on machine learning with Scikit-Learn and TensorFlow : concepts, tools, and techniques to build intelligent systems
- Hands-on machine learning with TensorFlow.js : a guide to building ML applications integrated with web technology using the TensorFlow.js library
- Hands-on meta learning with Python : meta learning using one-shot learning, MAML, Reptile, and Meta-SGD with TensorFlow
- Hands-on music generation with Magenta : explore the role of deep learning in music generation and assisted music composition
- Hands-on natural language processing with PyTorch
- Hands-on neural networks with Keras : design and create neural networks using deep learning and artificial intelligence principles
- Hands-on reinforcement learning for games : implementing self-learning agents in games using artificial intelligence techniques
- Hands-on transfer learning with Python : implement advanced deep learning and neural network models using TensorFlow and Keras
- Hands-on unsupervised learning using Python : how to discover hidden patterns in unlabeled data
- Hands-on unsupervised learning with Python : implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more
- Hardcore data science : California 2015
- Hardcore data science : NYC 2014
- Healthcare analytics made simple : techniques in healthcare computing using machine learning and Python
- Hello, TensorFlow! : building and training your first TensorFlow graph from the ground up
- How Criteo optimized and sped up its TensorFlow models by 10x and served them under 5 ms
- How automated machine learning empowers businesses
- How smart machines think
- How to build privacy and security into deep learning models
- Human recognition in unconstrained environments : using computer vision, pattern recognition and machine learning methods for biometrics
- IBM PowerAI : deep learning unleashed on IBM Power Systems Servers
- IBM Watson projects : eight exciting projects that put artificial intelligence into practice for optimal business performance
- Image analysis and text classification using CNNs in PyTorch : learn to build powerful image and document classifiers in minutes
- Implementing serverless microservices architecture patterns
- Intelligent computing for interactive system design : statistics, digital signal processing, and machine learning in practice
- Intelligent data-analytics for condition monitoring : smart grid applications
- Intelligent mobile projects with TensorFlow : build 10+ artificial intelligence apps using TensorFlow Mobile and Lite for iOS, Android, and Raspberry Pi
- Intelligent projects using Python : 9 real-world AI projects leveraging machine learning and deep learning with TensorFlow and Keras
- Interpretable and resilient AI for financial services
- Introducing Machine Learning
- Introducing data science : big data, machine learning, and more, using Python tools
- Introduction : mining the tar sands of big data
- Introduction to Amazon machine learning : learn how to build data driven predictive applications with Amazon Web Services (AWS)
- Introduction to Apache Spark 2.0 : a primer on Spark 2.0 fundamentals and architecture
- Introduction to GPUs for data analytics : advances and applications for accelerated computing
- Introduction to Pandas for developers : understand the basic workflows and gotchas of crawling, munging and plotting data
- Introduction to TensorFlow-Slim : complex TensorFlow model building and training made easy
- Introduction to cognitive computing with IBM Watson Services : break free from the myths surrounding IBM Watson to learn what it really can and can't do
- Introduction to computer vision with TensorFlow : using convolutional neural networks and TensorFlow to solve computer vision tasks
- Introduction to deep learning : concepts and fundamentals
- Introduction to deep learning and neural networks with Python
- Introduction to deep learning business applications for developers : from conversational bots in customer service to medical image processing
- Introduction to deep learning models with TensorFlow : learn how to work with TensorFlow to create and run a TensorFlow graph, and build a deep learning model
- Introduction to deep learning using PyTorch : create simple neural networks in Python using PyTorch
- Introduction to deep learning using R : a step-by-step guide to learning and implementing deep learning models using R
- Introduction to machine learning with Python : a guide for data scientists
- Java : data science made easy : data collection, processing, analysis, and more : a course in two modules
- Java data science cookbook : explore the power of MLlib, DL4j, Weka and more
- Java deep learning cookbook : train neural networks for classification, NLP, and reinforcement learning using Deeplearning4j
- Java deep learning projects : implement 10 real-world deep learning applications using Deeplearning4j and open source APIs
- Java for data science : examine the techniques and Java tools supporting the growing field of data science
- Keras 2.x projects : 9 projects demonstrating faster experimentation of neural network and deep learning applications using Keras
- Keras deep learning cookbook : over 30 recipes for implementing deep neural networks in Python
- Keras in motion
- Keras reinforcement learning projects : 9 projects exploring popular reinforcement learning techniques to build self-learning agents
- Keras to Kubernetes : the journey of a machine learning model to production
- Knowledge discovery from data streams
- Large scale machine learning with python : learn to build powerful machine learning models quickly and deploy large-scale predictive applications
- Learn Amazon SageMaker : a guide to building, training, and deploying machine learning models for developers and data scientists
- Learn R for Applied Statistics : With Data Visualizations, Regressions, and Statistics
- Learn R programming
- Learn Unity ML-Agents : fundamentals of Unity machine learning : incorporate new powerful ML algorithms such as Deep Reinforcement Learning for games
- Learn from the experts : artificial intelligence
- Learn how to build intelligent data applications with Amazon Web Services (AWS) : understanding and using AWS products and services, AWS Data Pipeline, Kinesis Analytics, RDS and Redshift databases, and Amazon Machine Learning
- Learning Apache Mahout : acquire practical skills in Big Data Analytics and explore data science with Apache Mahout
- Learning Apache Spark 2 : process big data with the speed of light!
- Learning Bayesian models with R : become an expert in Bayesian machine learning methods using R and apply them to solve real-world big data problems
- Learning Python data analysis
- Learning Python for data science
- Learning Salesforce Einstein : artificial intelligence and deep learning for your Salesforce CRM
- Learning Spark : lightening fast data analysis
- Learning TensorFlow : a guide to building deep learning systems
- Learning TensorFlow. js : machine learning in JavaScript
- Learning from multiagent emergent behaviors in a simulated environment
- Learning quantitative finance with R : implement machine learning, time-series analysis, algorithmic trading and more
- Leveraging data science in asset management
- Leveraging entity-resolution to identify customers in 3rd party data
- Leveraging multi-CDN at Riot Games
- Linear algebra for data science in Python
- Linear regression, Part 2, Introduction to real-world machine learning
- Live-coding a machine learning model from scratch
- MACHINE LEARNING USING TENSORFLOW COOKBOOK : over 60 recipes on machine learning using deep ... learning solutions from kaggle masters and google
- MATLAB for machine learning : functions, algorithms, and use cases
- MATLAB machine learning
- ML at Twitter : a deep dive into Twitter's timeline
- MLCAD '20 : proceedings of the 2020 ACM/IEEE Workshop on Machine Learning for CAD : November 16-20, 2020, Virtual Event, Iceland
- MLOps - Kernkonzepte im Überblick : Machine-Learning-Prozesse im Unternehmen nachhaltig automatisieren und skalieren
- Machine Learning - kurz & gut : Eine Einführung mit Python, Pandas und Scikit-Learn
- Machine Learning : an algorithmic perspective
- Machine Learning Approach for Cloud Data Analytics in IoT
- Machine Learning Engineering with Python : Manage the Production Life Cycle of Machine Learning Models Using MLOps with Practical Examples
- Machine Learning Forensics for Law Enforcement, Security, and Intelligence
- Machine Learning Kochbuch : Praktische Lösungen mit Python: von der Vorverarbeitung der Daten bis zum Deep Learning
- Machine Learning Pocket Reference
- Machine Learning Workshop - Second Edition
- Machine Learning for Algorithmic Trading - Second Edition
- Machine Learning for Business
- Machine Learning for OpenCV 4 : Intelligent Algorithms for Building Image Processing Apps Using OpenCV 4, Python, and Scikit-Learn, 2nd Edition
- Machine Learning for Time-Series with Python : Forecast, Predict, and Detect Anomalies with State-Of-the-art Machine Learning Methods
- Machine Learning für Softwareentwickler
- Machine Learning in Biotechnology and Life Sciences : Build Machine Learning Models Using Python and Deploy Them on the Cloud
- Machine Learning in the AWS Cloud : Add Intelligence to Applications with AWS SageMaker and AWS Rekognition
- Machine Learning kompakt : Alles, was Sie wissen müssen
- Machine Learning mit Python : das Praxis-Handbuch für Data Science, Predictive Analytics und Deep Learning
- Machine Learning mit Python und Scikit-learn und TensorFlow : das umfassende Praxis-Handbuch für Data Science, Deep Learning und Predictive Analytics
- Machine Learning with Amazon SageMaker Cookbook
- Machine Learning with R, the tidyverse, and mlr
- Machine Learning--die Referenz : mit strukturierten Daten in Python arbeiten
- Machine learning
- Machine learning 101 with Scikit-Learn and StatsModels
- Machine learning : a Bayesian and optimization perspective
- Machine learning : algorithms and applications
- Machine learning : kurz & gut
- Machine learning : les fondamentaux
- Machine learning : theory and applications
- Machine learning algorithms : reference guide for popular algorithms for data science and machine learning
- Machine learning algorithms in 7 days
- Machine learning and AI for healthcare : big data for improved health outcomes
- Machine learning and big data : concepts, algorithms, tools and applications
- Machine learning and big data with kdb+/q
- Machine learning and cognitive computing for mobile communications and wireless networks
- Machine learning and data monetization
- Machine learning and data science in the oil and gas industry : best practices, tools, and case studies
- Machine learning and data science in the power generation industry
- Machine learning and data science with Python : a complete beginners guide
- Machine learning and internet of medical things in healthcare
- Machine learning and security : protecting systems with data and algorithms
- Machine learning applications using Python : cases studies from healthcare, retail, and finance
- Machine learning approaches for convergence of IoT and Blockchain
- Machine learning at enterprise scale : how real practitioners handle six common challenges
- Machine learning avec R
- Machine learning avec Scikit-Learn : mise en œuvre et cas concrets
- Machine learning engineering with MLflow : manage the end-to-end machine learning lifecycle with MLflow
- Machine learning for absolute beginners
- Machine learning for algorithmic trading bots with Python
- Machine learning for contextual targeting
- Machine learning for designers
- Machine learning for designers : an introduction to the core technologies of machine learning and the emerging opportunities for ML-enhanced design
- Machine learning for email
- Machine learning for finance : principles and practice for financial insiders
- Machine learning for financial risk management with Python : algorithms for modeling risk
- Machine learning for future wireless communications
- Machine learning for future wireless communications
- Machine learning for healthcare analytics projects : build smart AI applications using neural network methodologies across the healthcare vertical market
- Machine learning for ios developers
- Machine learning for kids : a project-based introduction to artificial intelligence
- Machine learning for mobile : practical guide to building intelligent mobile applications powered by machine learning
- Machine learning for the web : explore the web and make smarter predictions using Python
- Machine learning for time series forecasting with Python
- Machine learning forensics for law enforcement, security, and intelligence
- Machine learning fundamentals
- Machine learning fundamentals
- Machine learning fundamentals with Amazon SageMaker on AWS : LiveLessons
- Machine learning guide for oil and gas using Python : a step-by-step breakdown with data, algorithms, codes, and applications
- Machine learning in R : automated algorithms for business analysis : applying K-Means clustering, decision trees, random forests, and neural networks
- Machine learning in action
- Machine learning in chemistry : the impact of artificial intelligence
- Machine learning in production : developing and optimizing data science workflows and applications
- Machine learning in the cloud with Azure machine learning
- Machine learning is changing the rules : ways business can utilize AI to innovate
- Machine learning logistics : model management in the real world
- Machine learning projects for .NET developers
- Machine learning projects for mobile applications : build Android and iOS applications using TensorFlow Lite and Core ML
- Machine learning projects with Java
- Machine learning quick reference : quick and essential machine learning hacks for training smart data models
- Machine learning solutions : expert techniques to tackle complex machine learning problems using Python
- Machine learning systems : designs that scale
- Machine learning using R : a comprehensive guide to machine learning
- Machine learning using R : with time series and industry-based uses in R
- Machine learning with Amazon SageMaker cookbook : 80 proven recipes for data scientists and developers to perform ML experiments and deployments
- Machine learning with Apache Spark quick start guide : uncover patterns, derive actionable insights, and learn from big data using MLlib
- Machine learning with BigQuery ML : create, execute, and improve machine learning models in BigQuery using standard SQL queries
- Machine learning with Core ML : an iOS developer's guide to implementing machine learning in mobile apps
- Machine learning with Dynamics 365 and Power Platform : the ultimate guide to apply predictive analytics
- Machine learning with Go : implement regression, classification, clustering, time-series models, neural networks, and more using the Go programming language
- Machine learning with PyTorch
- Machine learning with Python cookbook : practical solutions from preprocessing to deep learning
- Machine learning with Python for everyone
- Machine learning with R quick start guide : a beginner's guide to implementing machine learning techniques from scratch using R 3.5
- Machine learning with SAS Viya
- Machine learning with Spark : develop intelligent machine learning systems with Spark 2.x
- Machine learning with Swift : artificial intelligence for iOS
- Machine learning with TensorFlow
- Machine learning with scikit-learn : LiveLessons
- Machine learning with the Elastic Stack : expert techniques to integrate machine learning with distributed search and analytics
- Machine learning with the Raspberry Pi : experiments with data and computer vision
- Machine-to-Machine Marketing (M3) via Anonymous Advertising Apps Anywhere Anytime (A5)
- Machine-to-machine marketing (M3) via anonymous advertising apps anywhere anytime (A5)
- Mahout in action
- Making reinforcement learning practical for real-world developers
- Managing data science : effective strategies to manage data science projects and build a sustainable team
- Manipulating and Measuring Model Interpretability
- Mastering .NET machine learning : master the art of machine learning with .NET and gain insight into real-world applications
- Mastering Java machine learning : mastering and implementing advanced techniques in machine learning
- Mastering KVM virtualization : design expert data center virtualization solutions with the power of Linux KVM
- Mastering Scala machine learning : advance your skills in efficient data analysis and data processing using the powerful tools of Scala, Spark, and Hadoop
- Mastering Spark for data science : master the techniques and sophisticated analytics used to construct Spark-based solutions that scale to deliver production-grade data science products
- Mastering TensorFlow 1.x : advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras
- Mastering machine learning algorithms : expert techniques to implement popular machine learning algorithms and fine-tune your models
- Mastering machine learning for penetration testing : develop an extensive skill set to break self-learning systems using Python
- Mastering machine learning with Python in six steps : a practical implementation guide to predictive data analytics using Python
- Mastering machine learning with R : advanced machine learning techniques for building smart applications with R 3.5
- Mastering machine learning with R : advanced prediction, algorithms, and learning methods with R 3.x
- Mastering machine learning with Spark 2.x : create scalable machine learning applications to power a modern data-driven business using Spark
- Mastering machine learning with scikit-learn : learn to implement and evaluate machine learning solutions with scikit-learn
- Mastering predictive analytics with R : machine learning techniques for advanced models
- Mathematical foundation for AI and machine learning
- Meet the Expert : Laurence Moroney on A Programmer's Guide to Artificial Intelligence
- Meet the expert : Roger Magoulas on AI adoption in the enterprise in 2020
- Merkmalskonstruktion für Machine Learning : Prinzipien und Techniken der Datenaufbereitung
- Microsoft Azure machine learning : explore predictive analytics using step-by-step tutorials and build models to make prediction in a jiffy with a few mouse clicks
- Mobile artificial intelligence projects : develop seven projects on your smartphone using artificial intelligence and deep learning techniques
- Modernizing cybersecurity operations with machine intelligence : advanced threat detection, hunting, and analysis
- Monitoring and improving the performance of machine learning models : how to use ModelDB and Spark to track and improve model performance over time
- NLP from scratch : solving the cold start problem for natural language processing
- Natural language annotation for machine learning
- Natural language processing and computational linguistics : a practical guide to text analysis with Python, Gensim, spaCy, and Keras
- Natural language processing with AWS AI services : implement various NLP use cases from unstructured data using Amazon Comprehend and Amazon Textract
- Natural language processing with Java : techniques for building machine learning and neural network models for NLP
- Natural language processing with Java cookbook : over 70 recipes to create linguistic and language translation applications using Java libraries
- Natural language processing with TensorFlow : teach language to machines using Python's deep learning library
- Neural network programming with TensorFlow : unleash the power of TensorFlow to train efficient neural networks
- Neural networks and deep learning
- Neural networks with Keras cookbook : over 70 recipes leveraging deep learning techniques across image, text, audio, and game bots
- Neural networks, Part 5, Introduction to real-world machine learning
- Neural structured learning in TensorFlow
- New frontiers in ML-driven customer intelligence
- Numerical computing with Python : harness the power of Python to analyze and find hidden patterns in the data
- O'Reilly Artificial Intelligence Conference 2017, New York, New York
- O'Reilly Artificial Intelligence Conference 2017, San Francisco, CA
- O'Reilly Artificial Intelligence Conference 2019, London, United Kingdom
- O'Reilly Strata Data and AI Superstream
- O'Reilly TensorFlow World 2019, Santa Clara, CA
- Oil, gas, and data : high-performance data tools in the production of industrial power
- Online evaluation of machine learning models
- Online sentiment : machine learning and prediction
- OpenCV 3 computer vision with Python cookbook : leverage the power of OpenCV 3 and Python to build computer vision applications
- Operationalize ML by empowering people
- PRACTICAL FAIRNESS : achieving fair and secure data models
- PROGRAMMING ML.NET
- PYTORCH POCKET REFERENCE : building and deploying deep learning models
- Pattern recognition and machine learning
- Personalization at scale : challenges and practical techniques
- Personalizing the infinite jukebox : ML and the TensorFlow ecosystem at Spotify
- Practical AI for Healthcare Professionals: : Machine Learning with Numpy, Scikit-learn, and TensorFlow
- Practical MLOps: : operationalizing machine learning models
- Practical Natural Language Processing
- Practical artificial intelligence in the Cloud : exploring AI-as-a-service for business and research
- Practical artificial intelligence with Swift : from fundamental theory to development of AI-driven apps
- Practical automated machine learning on Azure : using Azure machine learning to quickly build AI solutions
- Practical big data analytics : hands-on techniques to implement enterprise analytics and machine learning using Hadoop, Spark, NoSQL and R
- Practical computer vision : extract insightful information from images using TensorFlow, Keras, and OpenCV
- Practical convolutional neural networks : implement advanced deep learning models using Python
- Practical data science with SAP : machine learning techniques for enterprise data
- Practical deep learning : a Python-based introduction
- Practical feature engineering
- Practical machine learning : a new look at anomaly detection
- Practical machine learning : innovations in recommendation
- Practical machine learning : tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques
- Practical machine learning cookbook : resolving and offering solutions to your machine learning problems with R
- Practical machine learning for computer vision : end-to-end machine learning for images
- Practical machine learning techniques for building intelligent applications
- Practical machine learning with H2O : powerful, scalable techniques for deep learning and AI
- Practical network automation : a beginner's guide to automating and optimizing networks using Python, Ansible, and more
- Practical time series analysis : master time series data processing, visualization, and modeling using Python
- Practical time series analysis : prediction with statistics and machine learning
- Pragmatic AI : an introduction to cloud-based machine learning
- Pragmatic AI and machine learning core principles : LiveLessons
- Praxisbuch unsupervised Learning : Machine-Learning-Anwendungen für ungelabelte Daten mit Python programmieren
- Praxiseinstieg Deep Learning : mit Python, Caffe, TensorFlow und Spark eigene Deep-Learning-Anwendungen erstellen
- Praxiseinstieg Machine Learning mit Scikit-Learn und TensorFlow : Konzepte, Tools und Techniken für intelligente Systeme
- Praxiseinstieg Machine Learning mit Scikit-Learn, Keras und TensorFlow, 2nd Edition
- Predictive maintenance : a world of zero unplanned downtime
- Principles and labs for deep learning
- Proceedings of the Sixth International Workshop on Machine Learning, Cornell University, Ithaca, New York, June 26-27, 1989
- Process automation using machine learning
- Programming PyTorch for deep learning : creating and deploying deep learning applications
- Programming machine learning : from coding to deep learning
- Programming skills for data science : start writing code to wrangle, analyze, and visualize data with R
- PyTorch deep learning in 7 days
- Python : deeper insights into machine learning : leverage benefits of machine learning techniques using Python : a course in three modules
- Python : real world machine learning : learn to solve challenging data science problems by building powerful machine learning models using Python
- Python Data Analysis : Perform Data Collection, Data Processing, Wrangling, Visualization, and Model Building Using Python
- Python Machine Learning - Third Edition
- Python advanced guide to artificial intelligence : expert machine learning systems and intelligent agents using Python
- Python deep learning : next generation techniques to revolutionize computer vision, AI, speech and data analysis
- Python deep learning cookbook : over 75 practical recipes on neural network modeling, reinfor