Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning
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Multi-Criteria Decision-Making and Optimum Design with Machine Learning: A Practical Guide
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Data Without Labels: Practical Unsupervised Machine Learning
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Machine Learning Models in Quantitative Finance: A Practical Guide to Forecasting, Pricing, and Signal Generation
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Practical Bioinformatics For Beginners: From Raw Sequence Analysis To Machine Learning Applications
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SCALING AI PRODUCTS IN PRODUCTION: A Practical Framework for Building Reliable, Secure, and Cost-Effective Machine Learning Systems at Scale
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C# Programming and AI Development for Beginners: Learn c#, .net, Machine Learning, And Practical Ai Application Development Step By Step
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Machine Learning with Python – Principles and Practical Techniques
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Practical Machine Learning for Computer Vision – End-to-End Machine Learning for Images – O’Reilly
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Practical Discrete Mathematics: Discover math principles that fuel algorithms for computer science and machine learning with Python
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Machine Learning for Data Streams - with Practical Examples in MOA
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Practical Machine Learning: A Beginner's Guide with Ethical Insights
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Scikit-learn Made Easy: A Beginner's Guide to Machine Learning with Python, Practical Examples, Mini Projects, and Exercises
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Hands-On Artificial Intelligence for Banking: A practical guide to building intelligent financial applications using machine learning techniques
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Statistics, Data Mining, and Machine Learning in Astronomy
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Machine Learning with Python: A Practical Guide from Foundations to Deployment
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Shipping Machine Learning Systems: A Practical Guide to Building, Deploying, and Scaling in Production
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Low-Code AI: A Practical Project-Driven Introduction to Machine Learning
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Practical Data Science on Databricks – Scaling end-to-end machine learning on Databricks
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Data Science with Python: A Beginner-Friendly Practical Guide: From Data Cleaning and Visualization to Machine Learning, Forecasting, and Real-World Projects - No Heavy Math, Step-by-Step Learning
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Machine Learning Demystified: A Practical Guide to Understanding Algorithms, from Random Forests to Support Vector Machines
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Data Mining: Practical Machine Learning Tools and Techniques (The Morgan Kaufmann Series in Data Management Systems)
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