Tag: scikit

December 28, 2023

Machine Learning Algorithms Using Scikit and TensorFlow Environments

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Free Download Machine Learning Algorithms Using Scikit and TensorFlow Environments
English | 2023 | ISBN: 1668485311 | 473 pages | True PDF | 14.24 MB
Machine learning is able to solve real-time problems. It has several algorithms such as classification, clustering, and more. To learn these essential algorithms, we require tools like Scikit and TensorFlow. Machine Learning Algorithms Using Scikit and TensorFlow Environments assists researchers in learning and implementing these critical algorithms. Covering key topics such as classification, artificial neural networks, prediction, random forest, and regression analysis, this premier reference source is ideal for industry professionals, computer scientists, researchers, academicians, scholars, practitioners, instructors, and students.

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July 18, 2023

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

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Free Download Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Géron, Aurélien
English | November 8, 2022 | ISBN: 1098125975 | 861 pages | MOBI | 14 Mb
Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.

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July 16, 2023

STROKE Analysis and Prediction Using Scikit-Learn, Keras, and TensorFlow with Python GUI

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Free Download STROKE: Analysis and Prediction Using Scikit-Learn, Keras, and TensorFlow with Python GUI
English | 2023 | ASIN : B09FSQ6YW7 | 594 Pages | True EPUB | 18 MB
In this project, we will perform an analysis and prediction task on stroke data using machine learning and deep learning techniques. The entire process will be implemented with Python GUI for a user-friendly experience. We start by exploring the stroke dataset, which contains information about various factors related to individuals and their likelihood of experiencing a stroke. We load the dataset and examine its structure, features, and statistical summary. Next, we preprocess the data to ensure its suitability for training machine learning models. This involves handling missing values, encoding categorical variables, and scaling numerical features. We utilize techniques such as data imputation and label encoding.

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