#
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**.- Label
- Machine learning

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- http://id.worldcat.org/fast/01004795

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- fast

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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