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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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Hands-on Scikit-Learn for Machine Learning Applications

machine learning applications in the world of Python. What You'll Learn Work with simple and complex datasets common to Scikit-Learn Manipulate;

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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

learning, and Pytorch for deep learning with Python. It also serves as a long-term reference manual for the practitioners who will find solutions;

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Machine Learning with scikit-learn Quick Start Guide

learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine;

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scikit-learn Cookbook -

deeper in to implementing deep learning with scikit-learn. Along with covering the enhanced features on model section, API and new features like;

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Introduction to IoT with Machine Learning and Image Processing using Raspberry Pi

with Python Scikit-Learn library is explained to audience with an emphasis on supervised learning and classification. Image processing on IoT;

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Introduction to IoT with Machine Learning and Image Processing using Raspberry Pi

with Python Scikit-Learn library is explained to audience with an emphasis on supervised learning and classification. Image processing on IoT;

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Hands-On Machine Learning with Scikit-Learn and TensorFlow

shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks-scikit-learn and TensorFlow-author;

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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

shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks-Scikit-Learn and TensorFlow-author;

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Python for Probability, Statistics, and Machine Learning

, Sympy, Scikit-learn, Tensorflow, and Keras are applied to simulate and visualize important machine learning concepts like the bias/variance;

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Python for Probability Statistics and Machine Learning

, Sympy, Scikit-learn, Tensorflow, and Keras are applied to simulate and visualize important machine learning concepts like the bias/variance;

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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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Python Data Science Handbook

. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you'll learn how: IPython and Jupyter;

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Python Machine Learning -

updates to scikit-learn. Style and Approach Python Machine Learning Second Edition takes a practical, hands-on coding approach so you can;

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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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Practical Deep Learning

networks, and modern convolutional neural networks. Experiments in Python - working with leading open-source toolkits and standard datasets - give;

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Data Science Projects with Python

Gain hands-on experience with industry-standard data analysis and machine learning tools in Python Key Features Tackle data science;

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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 Machine Learning

DescriptionPython Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step;

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Hands-On Data Analysis with Pandas

Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data;

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Building Machine Learning Systems with Python

recommendation systems. With Building Machine Learning Systems with Python, you'll gain the tools and understanding required to build your own systems;

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Hands-On Image Processing with Python

processing or computer vision with deep learning. We will learn how to use image processing libraries such as PIL, scikit-mage, and scipy ndimage;

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Natural Language Processing and Computational Linguistics

Hands-on text analysis with Python, featuring natural language processing and computational linguistics algorithms Learn deep learning;

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Hands-On Machine Learning for Cybersecurity

a system using Python libraries with NumPy, Scikit-learn, and CUDA Understand how to combat malware, detect spam, and fight financial fraud;

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Python for Probability, Statistics, and Machine Learning

same code on their own computers. Modern Python modules like Pandas, Sympy, Scikit-learn, Statsmodels, Scipy, Xarray, Tensorflow, and Keras;

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