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The Machine Learning Solutions Architect Handbook : Create machine learning platforms to run solutions in an enterprise setting

Deep Learning with TensorFlow 2 and Keras : Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition

Hands-On Genetic Algorithms with Python : Applying genetic algorithms to solve real-world deep learning and artificial intelligence problems

The The TensorFlow Workshop : A hands-on guide to building deep learning models from scratch using real-world datasets

Cleaning Data for Effective Data Science : Doing the other 80% of the work with Python, R, and command-line tools

Advanced Deep Learning with R : Become an expert at designing, building, and improving advanced neural network models using R

Machine Learning Projects for Mobile Applications : Build Android and iOS applications using TensorFlow Lite and Core ML

Machine Learning Algorithms : Popular algorithms for data science and machine learning, 2nd Edition

Hands-On Python Natural Language Processing : Explore tools and techniques to analyze and process text with a view to building real-world NLP applications

C++ Machine Learning

Open Source MLOPs : Version Control and Automation for Machine Learning Pipelines With DVC and CML

Machine Learning with Microsoft ML.Net : Develop Machine Learning Models with ML.NET from the Developer's perspective

Google Machine Learning and Generative AI for Solutions Architects : ?Build efficient and scalable AI/ML solutions on Google Cloud

Hands-On Gradient Boosting with XGBoost and scikit-learn : Perform accessible machine learning and extreme gradient boosting with Python

Machine Learning on Oracle Cloud : Mastery of Machine Learning lifecycle on Oracle Cloud

Python Machine Learning : Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2

Machine Learning with R : Expert techniques for predictive modeling

Databricks ML in Action : Learn how Databricks supports the entire ML lifecycle end to end from data ingestion to the model deployment

Hands-On Generative Adversarial Networks with PyTorch 2.x : Gain hands-on expertise in utilizing robust Generative AI models to tackle a wide array of challenges

Privacy-Preserving Machine Learning : A use-case-driven approach to building and protecting ML pipelines from privacy and security threats

Modern Computer Vision with PyTorch : A practical roadmap from deep learning fundamentals to advanced applications and generative AI

Python Machine Learning By Example : Unlock machine learning best practices with real-world use cases

Hands-On Unsupervised Learning with Python : Implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more

Machine Learning for Time-Series with Python : Forecast, predict, and detect anomalies with state-of-the-art machine learning methods

Automated Machine Learning : Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms

Hands-On Predictive Analytics with Python : Master the complete predictive analytics process, from problem definition to model deployment

TensorFlow Developer Certificate Guide : Efficiently tackle deep learning and ML problems to ace the Developer Certificate exam

Machine Learning in Java : Helpful techniques to design, build, and deploy powerful machine learning applications in Java, 2nd Edition

The Definitive Guide to Google Vertex AI : Implement Machine Learning pipelines with Google Cloud Vertex AI

Mastering Computer Vision with TensorFlow 2.x : Build advanced computer vision applications using machine learning and deep learning techniques

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