Applied Machine Learning: A Practical Guide to Preparing Data, Selecting Algorithms, and Implementing Machine Learning Models in the Real World Get it at https://
link.amazon/B0h2qRWxE
@kirkdborne
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Applied Machine Learning Guide Covers Data Preparation and Algorithm Selection
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Guide Explains How to Make a Neural Network
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Make Your Own Neural Network: https://
link.amazon/B0bUQ04YS -
MIT Press Resource Introduces Deep Learning
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Deep Learning (The MIT Press Essential Knowledge series): https://
link.amazon/B097R0S4p -
Introductory Resource Combines Statistics and Machine Learning
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Data Science – An Introduction to Statistics and Machine Learning: https://
link.amazon/B0evViGJw -
Deep Learning Guide Covers Neural Architectures and Natural Language Processing
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Understanding Deep Learning: Building Machine Learning Systems with PyTorch and TensorFlow: From Neural Networks (CNN, DNN, GNN, RNN, ANN, LSTM, GAN) to Natural Language Processing (NLP) Get it at https://
link.amazon/B0iM8POIL -
Prompting Guide Explores Better AI Results With Fewer Tokens
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How Prompts Work: How to Talk to Artificial Intelligence, Get Better Results, and Spend Fewer Tokens Get it at https://
link.amazon/B05ujUBy5 -
Scikit Learn Guide Teaches Building Better Machine Learning Models
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Master Machine Learning with scikit-learn: A Practical Guide to Building Better Models with Python Get it at https://
link.amazon/B0dhUyn7M -
Machine Learning From Scratch Covers Intuition, Mathematics and Code
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Machine Learning From Scratch: Intuition, Math and Code of ML Algorithms Get it at https://
link.amazon/B05Mh2yHS -
Guide Teaches Clustering, Pattern Discovery and Dimensionality Reduction
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Master Unsupervised Machine Learning Through Pattern Discovery, Clustering, and Dimensionality Reduction to Build Intelligent Real-World Applications: https://
link.amazon/B0gb9LdM1 -
Python Guide Builds Machine Learning Systems From Unlabeled Data
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Hands-On Unsupervised Learning Using Python — How to Build Applied Machine Learning Solutions from Unlabeled Data: https://
link.amazon/B0dVm2mci