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Machine Learning with scikit-learn Quick Start Guide
Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering;
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scikit-learn Cookbook -
Learn to use scikit-learn operations and functions for Machine Learning and deep learning applications. About This Book * Handle a;
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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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Machine Learning with Scikit-Learn and TensorFlow
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Learning scikit-learn
The book adopts a tutorial-based approach to introduce the user to Scikit-learn.If you are a programmer who wants to explore machine;
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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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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 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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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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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 Python
visualizations. The second section covers machine learning basics and Scikit-learn library. It also explains supervised learning, unsupervised learning;
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Deep Learning with Applications Using Python
learning frameworks such as TensorFlow, Keras, and scikit-learn. Use face recognition and face detection capabilities Create speech-to-text;
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Beginning Anomaly Detection Using Python-Based Deep Learning
statistical and traditional machine learning methods for anomaly detection using Scikit-Learn in Python, the book then provides an introduction to;
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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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Mastering Predictive Analytics with scikit-learn and TensorFlow
can be used in the field of data science. Mastering Predictive Analytics with scikit-learn and TensorFlow covers various implementations;
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Python Data Science Handbook
do you get them all-IPython, NumPy, pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar;
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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 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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Beginning MLOps with MLFlow
the target variable. You will learn how to build logistic regression models in scikit-learn and PySpark, and you will go through the process;
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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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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 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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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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