Machine Learning and Data Science Blueprints for Finance | Paperback
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Inference and Learning from Data: Volume 3: Learning – Cambridge University Press
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Python for Data Science and Machine Learning: The Only Book You Need to Master Python for DS and ML from Scratch: A Hands-On Guide to NumPy, Pandas, Matplotlib, Seaborn and Scikit-Learn
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Machine Learning and Pattern Recognition Methods in Chemistry – Elsevier
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Data Science on the Google Cloud Platform: Implementing End-To-End Real-Time Data Pipelines: From Ingest to Machine Learning
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Statistics – The Art and Science of Learning from Data
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Data Science & Predictive Analytics for Actuaries: From Spreadsheets to Machine Learning Models: 8 (Actuarial Mastery Series: From Fundamentals to the Future of Risk Science)
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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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The Health Care Data Guide: Learning from Data for Improvement
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Statistical Methods and Analyses for Optimization Algorithms and Artificial Intelligence: An Intuitive Guide and Practical Steps of Learning from Data
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The Supervised Learning Workshop: A New, Interactive Approach to Understanding Supervised Learning Algorithms, 2nd Edition
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DATA SCIENCE & MACHINE LEARNING FOR BEGINNERS: The Step-by-Step Guide to Understanding AI, Analyzing Data, and Building Your First Machine Learning Models - Starting from Zero
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Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data
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Data Science Projects with Python: A case study approach to gaining valuable insights from real data with machine learning, 2nd Edition
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Training Data for Machine Learning: Human Supervision from Annotation to Data Science
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Data Science from Scratch to Production: A Complete Guide to Python, Machine Learning, Deep Learning, Deployment & MLOps: 1 (The Complete Data Science & AI Engineering Series)
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Learning Statistical reasoning: A Practical Guide to Understanding Probability, Distributions, and Drawing Valid Conclusions from Data
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