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Machine Learning with scikit-learn Quick Start Guide

-learn provides. This book teaches you how to use scikit-learn for machine learning. You will start by setting up and configuring your machine;

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scikit-learn Cookbook -

variety of machine learning tasks effortlessly by leveraging the power of scikit-learn * Perform supervised and unsupervised learning with ease;

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Machine Learning Fundamentals

With the flexibility and features of scikit-learn and Python, build machine learning algorithms that optimize the programming process and;

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Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

machine learning problems Key Features Delve into machine learning with this comprehensive guide to scikit-learn and scientific Python Master the;

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Machine Learning with Scikit-Learn and TensorFlow

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Hands-on Scikit-Learn for Machine Learning Applications

Aspiring data science professionals can learn the Scikit-Learn library along with the fundamentals of machine learning with this book. The;

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Mastering Machine Learning with scikit-learn -

Use scikit-learn to apply machine learning to real-world problems About This Book * Master popular machine learning models including;

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Learning scikit-learn

experience with machine-learning algorithms is required.;

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Hands-on Machine Learning with Python

unsupervised learning Examine convolutional neural networks and Recurrent neural networks Get acquainted with scikit-learn and PyTorch Predict;

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Hands-on Machine Learning with Python

visualizations. The second section covers machine learning basics and Scikit-learn library. It also explains supervised learning, unsupervised learning;

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Hands-On Machine Learning with Scikit-Learn and TensorFlow

scikit-learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision;

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Machine Learning with Scala Quick Start Guide

Supervised and unsupervised machine learning made easy in Scala with this quick-start guide. Key Features Construct and deploy machine;

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Machine Learning mit Python und Keras, TensorFlow 2 und Scikit-learn

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Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow

Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision;

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Python Machine Learning

latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as;

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Natural Language Processing with Python Quick Start Guide

modern tooling across linguistics, text vectors and machine learning Fundamentals of NLP methods from spaCy, gensim, scikit-learn and PyTorch;

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Machine Learning for OpenCV 4

scikit-learn Get up to speed with Intel OpenVINO and its integration with OpenCV 4 Implement high-performance machine learning models with;

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Machine Learning Pocket Reference Working with Structured Data in Python

learning process and walks you through classification with structured data. You'll also learn methods for clustering, predicting a continuous value;

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Beginning Anomaly Detection Using Python-Based Deep Learning

is important in today's world Become familiar with statistical and traditional machine learning approaches to anomaly detection using Scikit-Learn;

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Python Machine Learning -

, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully;

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Kubeflow for Machine Learning From Lab to Production

Kubeflow on a cloud provider or on an in-house cluster Train models using Kubeflow with popular tools including scikit-learn, TensorFlow, and;

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Introduction to Machine Learning with Python

the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and;

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Python Feature Engineering Cookbook

Extract accurate information from data to train and improve machine learning models using NumPy, SciPy, pandas, and scikit-learn libraries;

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TensorFlow Machine Learning Cookbook

About This Book * Your quick guide to implementing TensorFlow in your day-to-day machine learning activities * Learn advanced techniques;

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Ensemble Learning for AI Developers

Use ensemble learning techniques and models to improve your machine learning results. Ensemble Learning for AI Developers starts you at;

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Thoughtful Machine Learning

risk of errors with TDD and write clean, stable machine-learning code. If you're familiar with Ruby 2.1, you're ready to start. Apply TDD to;

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Data Science Projects with Python

Gain hands-on experience of Python programming with industry-standard machine learning techniques using pandas, scikit-learn, and XGBoost;

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