#
Machine learning
Resource Information
The concept ** Machine learning** represents the subject, aboutness, idea or notion of resources found in **Merrimack Valley Library Consortium**.

The Resource
Machine learning
Resource Information

The concept

**Machine learning**represents the subject, aboutness, idea or notion of resources found in**Merrimack Valley Library Consortium**.- Label
- Machine learning

## Context

Context of Machine learning#### Subject of

- AI for data science : artificial intelligence frameworks and functionality for deep learning, optimization, and beyond
- Achieving real business outcomes from artificial intelligence : enterprise considerations for AI initiatives
- Adaptive learning methods for nonlinear system modeling
- Advanced data analytics using Python : with machine learning, deep learning and NLP examples
- Advanced deep learning with Keras : apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more
- Advances in financial machine learning
- An introduction to machine learning interpretability : an applied perspective on fairness, accountability, transparency, and explainable AI
- 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 quick start guide : quickly learn the art of writing efficient big data applications with Apache Spark
- Applied deep learning : a case-based approach to understanding neural networks
- Applied natural language processing with Python : implementing machine learning and deep learning algorithms for natural language processing
- Applied text analysis with Python : enabling language-aware data products with machine learning
- Artificial intelligence : the simplest way
- Artificial intelligence and machine learning fundamentals
- Artificial intelligence and machine learning in industry : perspectives from leading practitioners
- Artificial intelligence now : current perspectives from O'Reilly Media
- Beginning AI bot frameworks : getting started with bot development
- Beginning application development with TensorFlow and Keras : learn to design, develop, train, and deploy TensorFlow and Keras models as real-world applications
- Best practices for bringing AI to the enterprise
- Building intelligent systems : a guide to machine learning engineering
- Building machine learning systems with Python : explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow
- C# machine learning projects : nine real-world projects to build robust and high-performing machine learning models with C#
- 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
- Data analysis with Python : a modern approach
- Data science : mindset, methodologies, and misconceptions
- Data science algorithms in a week : data analysis, machine learning, and more
- Data science algorithms in a week : top 7 algorithms for scientific computing, data analysis, and machine learning
- Data science with Java : practical methods for scientists and engineers
- Data science with SQL server quick start guide : integrate SQL server with data science
- Deep Belief Nets in C++ and CUDA C, Volume 2, Autoencoding in the complex domain
- Deep learning : a practitioner's approach
- 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 by example : a hands-on guide to implementing advanced machine learning algorithms and neural networks
- Deep learning cookbook : practical recipes to get started quickly
- Deep learning for natural language processing : creating neural networks with Python
- Deep learning for numerical applications with SAS
- Deep learning in the browser
- Deep learning with Azure : building and deploying artificial intelligence solutions on the Microsoft AI platform
- Deep learning with Keras : implement neural networks with Keras on Theano and TensorFlow
- 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 : a hands-on introduction
- Deep learning with R
- Deep learning with TensorFlow : explore neural networks and build intelligent systems with Python
- Deep learning with TensorFlow : take your machine learning knowledge to the next level with the power of TensorFlow
- Deep learning with Theano : build the artificial brain of the future, today
- Deep reinforcement learning hands-on : apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more
- Effective Amazon machine learning : machine learning in the Cloud
- Ensemble machine learning cookbook : over 35 practical recipes to explore ensemble machine learning techniques using Python
- 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
- Fundamentals of deep learning : designing next-generation machine intelligence algorithms
- 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
- Getting started with TensorFlow
- Getting started with artificial intelligence : a practical guide to building enterprise applications
- Getting started with deep learning
- Go machine learning projects : eight projects demonstrating end-to-end machine learning and predictive analytics applications in Go
- Hands-on Markov models with Python : implement probabilistic models for learning complex data sequences using Python ecosystem
- 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 search : building intelligent applications and perform enterprise searches
- Hands-on artificial intelligence with Java for beginners : build intelligent apps using machine learning and deep learning with Deeplearning4j
- 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 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 TensorFlow : uncover what is underneath your data!
- Hands-on ensemble learning with R : a beginner's guide to combining the power of machine learning algorithms using ensemble techniques
- 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 algorithmic trading : design and implement investment strategies based on smart algorithms that learn from data using Python
- Hands-on machine learning for cybersecurity : safeguard your system by making your machines intelligent using the Python ecosystem
- Hands-on machine learning on Google cloud platform : implementing smart and efficient analytics using Cloud ML Engine
- Hands-on machine learning with Azure : build powerful models with cognitive machine learning and artificial intelligence
- Hands-on machine learning with C# : build smart, speedy, and reliable data-intensive applications using machine learning
- Hands-on machine learning with JavaScript : solve complex computational web problems using machine learning
- Hands-on machine learning with Scikit-Learn and TensorFlow : concepts, tools, and techniques to build intelligent systems
- Hands-on meta learning with Python : meta learning using one-shot learning, MAML, Reptile, and Meta-SGD with TensorFlow
- Hands-on serverless deep learning with TensorFlow and AWS Lambda : training serverless deep learning models using AWS infrastructure
- 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
- Healthcare analytics made simple : techniques in healthcare computing using machine learning and Python
- 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
- 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
- Introduction to GPUs for data analytics : advances and applications for accelerated computing
- Introduction to deep learning business applications for developers : from conversational bots in customer service to medical image processing
- Introduction to deep learning using R : a step-by-step guide to learning and implementing deep learning models using R
- 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 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 reinforcement learning projects : 9 projects exploring popular reinforcement learning techniques to build self-learning agents
- Learn Keras for deep neural networks : a fast-track approach to modern deep learning with Python
- Learn R for applied statistics : with data visualizations, regressions, and statistics
- Learn Unity ML-Agents : fundamentals of Unity machine learning : incorporate new powerful ML algorithms such as Deep Reinforcement Learning for games
- Learning Apache Spark 2 : process big data with the speed of light!
- Learning Microsoft Cognitive Services : create intelligent apps with vision, speech, language, and knowledge capabilities using Microsoft Cognitive Services
- Learning Microsoft Cognitive Services : leverage machine learning APIs to build smart applications
- Learning Microsoft Cognitive Services : use Cognitive Services APIs to add AI capabilities to your applications
- Learning Salesforce Einstein : artificial intelligence and deep learning for your Salesforce CRM
- Learning TensorFlow : a guide to building deep learning systems
- Learning quantitative finance with R : implement machine learning, time-series analysis, algorithmic trading and more
- MATLAB deep learning : with machine learning, neural networks and artificial intelligence
- MATLAB for machine learning : functions, algorithms, and use cases
- Machine Learning with scikit-learn quick start guide : classification, regression, and clustering techniques in Python
- Machine learning algorithms : popular algorithms for data science and machine learning
- Machine learning algorithms : reference guide for popular algorithms for data science and machine learning
- Machine learning and AI for healthcare : big data for improved health outcomes
- Machine learning and security : protecting systems with data and algorithms
- Machine learning applications using Python : cases studies from healthcare, retail, and finance
- Machine learning for OpenCV : a practical introduction to the world of machine learning and image processing using OpenCV and Python
- Machine learning for decision makers : cognitive computing fundamentals for better decision making
- Machine learning for healthcare analytics projects : build smart AI applications using neural network methodologies across the healthcare vertical market
- Machine learning for mobile : practical guide to building intelligent mobile applications powered by machine learning
- Machine learning fundamentals
- Machine learning in production : developing and optimizing data science workflows and applications
- 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 mobile applications : build Android and iOS applications using TensorFlow Lite and Core ML
- 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 : with time series and industry-based uses in R
- Machine learning with AWS : explore the power of cloud services for your machine learning and artificial intelligence projects
- Machine learning with Apache Spark quick start guide : uncover patterns, derive actionable insights, and learn from big data using MLlib
- Machine learning with Core ML : an iOS developer's guide to implementing machine learning in mobile apps
- Machine learning with Go : implement regression, classification, clustering, time-series models, neural networks, and more using the Go programming language
- Machine learning with PySpark : with natural language processing and recommender systems
- Machine learning with Python cookbook : practical solutions from preprocessing to deep learning
- 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 TensorFlow 1.x : second generation machine learning with Google's brainchild - TensorFlow 1.x
- Machine learning with the Elastic Stack : expert techniques to integrate machine learning with distributed search and analytics
- Mastering Java machine learning : mastering and implementing advanced techniques in machine learning
- 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 exploratory analysis with pandas : build an end-to-end data analysis workflow with Python
- 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 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
- Modern Scala projects : leverage the power of Scala for building data-driven and high-performant projects
- Modernizing cybersecurity operations with machine intelligence : advanced threat detection, hunting, and analysis
- Natural language processing and computational linguistics : a practical guide to text analysis with Python, Gensim, spaCy, and Keras
- Natural language processing recipes : unlocking text data with machine learning and deep learning using Python
- Natural language processing with Java : techniques for building machine learning and neural network models for NLP
- 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 R : smart models using CNN, RNN, deep learning, and artificial intelligence principles
- Numerical computing with Python : harness the power of Python to analyze and find hidden patterns in the data
- OpenCV 3 computer vision with Python cookbook : leverage the power of OpenCV 3 and Python to build computer vision applications
- Oracle business intelligence with machine learning : artificial intelligence techniques in OBIEE for actionable BI
- Pandas cookbook : recipes for scientific computing, time series analysis and data visualization using Python
- Practical Java machine learning : projects with Google Cloud Platform and Amazon Web Services
- Practical artificial intelligence : machine learning, bots, and agent solutions using C#
- 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 machine learning cookbook : resolving and offering solutions to your machine learning problems with R
- Practical machine learning with Python : a problem-solver's guide to building real-world intelligent systems
- Practical network automation : a beginner's guide to automating and optimizing networks using Python, Ansible, and more
- Pragmatic AI : an introduction to cloud-based machine learning
- Pro deep learning with TensorFlow : a mathematical approach to advanced artificial intelligence in Python
- Pro machine learning algorithms : a hands-on approach to implementing algorithms in Python and R
- Programming skills for data science : start writing code to wrangle, analyze, and visualize data with R
- PyTorch recipes : a problem-solution approach
- Python advanced guide to artificial intelligence : expert machine learning systems and intelligent agents using Python
- Python deep learning : exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow
- 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, reinforcement learning, and transfer learning using Python
- Python deep learning projects : 9 projects demystifying neural network and deep learning models for building intelligent systems
- Python for R users : a data science approach
- Python machine learning : machine learning and deep learning with Python, scikit-learn, and TensorFlow
- Python machine learning blueprints : put your machine learning concepts to the test by developing real-world smart projects
- Python machine learning by example : easy-to-follow examples that get you up and running with machine learning
- Python machine learning case studies : five case studies for the data scientist
- Python natural language processing : explore NLP with machine learning and deep learning techniques
- Python reinforcement learning projects : eight hands-on projects exploring reinforcement learning algorithms using TensorFlow
- Python vs. R for data science
- R deep learning cooking : solve complex neural net problems with TensorFlow, H2O and MXNet
- R deep learning essentials : a step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet
- R deep learning projects : master the techniques to design and develop neural network models in R
- R machine learning projects : implement supervised, unsupervised, and reinforcement learning techniques using R 3.5
- Recurrent neural networks with Python Quick Start Guide : sequential learning and language modeling with TensorFlow
- Scala : applied machine learning : leverage the power of Scala and master the art of building, improving, and validating scalable machine learning and AI applications using Scala's most advanced and finest features : a course in three modules
- Scala : guide for data science professionals : Scala will be a valuable tool to have on hand during your data science journey for everything from data cleaning to cutting-edge machine learning : a course in three modules
- Scala for machine learning : data processing, ML algorithms, smart analytics, and more
- Scala machine learning projects : build real-world machine learning and deep learning projects with Scala
- Scikit-learn : machine learning simplified
- Scikit-learn cookbook : over 80 recipes for machine learning in Python with scikit-learn
- Security with AI and machine learning : using advanced tools to improve application security at the edge
- Serving machine learning models : a guide to architecture, stream processing engines, and frameworks
- Statistics for machine learning : build supervised, unsupervised, and reinforcement learning models using both Python and R
- TensorFlow 1.x deep learning cookbook : over 90 unique recipes to solve artificial-intelligence driven problems with Python
- TensorFlow : powerful predictive analytics with TensorFlow : predict valuable insights of your data with TensorFlow
- TensorFlow deep learning projects : 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning
- TensorFlow for deep learning : from linear regression to reinforcement learning
- TensorFlow for dummies
- TensorFlow machine learning cookbook : explore machine learning concepts using the latest numerical computing library, TensorFlow, with the help of this comprehenisive cookbook
- TensorFlow machine learning cookbook : over 60 recipes to build intelligent machine learning systems with the power of Python
- TensorFlow machine learning projects : build 13 real-world projects with advanced numerical computations using the Python ecosystem
- Thoughtful data science : a programmer's toolset for data analysis and artificial intelligence with Python, Jupyter Notebook, and PixieDust
- Thoughtful machine learning with Python : a test-driven approach
- Turning data into insight with IBM Machine Learning for z/OS
- Understanding support vector machines
- Veracity of big data : machine learning and other approaches to verifying truthfulness

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