Python 3 And Feature Engineering
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Python 3 and Feature Engineering
Author: Oswald Campesato
language: en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date: 2023-12-15
This book is designed for data scientists, machine learning practitioners, and anyone with a foundational understanding of Python 3.x. In the evolving field of data science, the ability to manipulate and understand datasets is crucial. The book offers content for mastering these skills using Python 3. The book provides a fast-paced introduction to a wealth of feature engineering concepts, equipping readers with the knowledge needed to transform raw data into meaningful information. Inside, you’ll find a detailed exploration of various types of data, methodologies for outlier detection using Scikit-Learn, strategies for robust data cleaning, and the intricacies of data wrangling. The book further explores feature selection, detailing methods for handling imbalanced datasets, and gives a practical overview of feature engineering, including scaling and extraction techniques necessary for different machine learning algorithms. It concludes with a treatment of dimensionality reduction, where you’ll navigate through complex concepts like PCA and various reduction techniques, with an emphasis on the powerful Scikit-Learn framework.
Natural Language Processing with Python
Author: Cuantum Technologies LLC
language: en
Publisher: Packt Publishing Ltd
Release Date: 2025-01-16
Learn NLP with Python through practical exercises, advanced topics like transformers, and real-world projects such as chatbots and dashboards. A comprehensive guide for mastering NLP techniques. Key Features A comprehensive guide to processing, analyzing, and modeling human language with Python Real-world projects that reinforce NLP concepts, including chatbot design and sentiment analysis Foundational and advanced NLP techniques for practical applications in diverse domains Book DescriptionEmbark on a comprehensive journey to master natural language processing (NLP) with Python. Begin with foundational concepts like text preprocessing, tokenization, and key Python libraries such as NLTK, spaCy, and TextBlob. Explore the challenges of text data and gain hands-on experience in cleaning, tokenizing, and building basic NLP pipelines. Early chapters provide practical exercises to solidify your understanding of essential techniques. Advance to sophisticated topics like feature engineering using Bag of Words, TF-IDF, and embeddings like Word2Vec and BERT. Delve into language modeling with RNNs, syntax parsing, and sentiment analysis, learning to apply these techniques in real-world scenarios. Chapters on topic modeling and text summarization equip you to extract insights from data, while transformer-based models like BERT take your skills to the next level. Each concept is paired with Python-based examples, ensuring practical mastery. The final chapters focus on real-world projects, such as developing chatbots, sentiment analysis dashboards, and news aggregators. These hands-on applications challenge you to design, train, and deploy robust NLP solutions. With its structured approach and practical focus, this book equips you to confidently tackle real-world NLP challenges and innovate in the field.What you will learn Clean and preprocess text data using Python effectively Master tokenization techniques for words, sentences, and characters Build robust NLP pipelines with feature engineering methods Implement sentiment analysis with machine learning models Perform topic modeling using LDA, LSA, and other algorithms Develop chatbots and dashboards for real-world applications Who this book is for This book is ideal for students, researchers, and professionals in machine learning, data science, and artificial intelligence who want to master NLP. Beginners will benefit from the step-by-step introduction to text processing and feature engineering, while experienced practitioners can explore advanced topics like transformers and real-world projects. Basic knowledge of Python and familiarity with programming concepts are recommended to fully utilize the content. Enthusiasts with a passion for language technology will also find this guide valuable for building practical NLP applications.
Human Digital Twins for Medical and Product Engineering
The current trend towards digitalization of human-centred engineering processes in conjunction with advances in (bio-)mechanistic modelling, high-performance computing, artificial intelligence (AI) and sensor technology leads to unprecedented transformation potentials in medical, product and human factors engineering for the enhancement of human-technology interaction as well as medical treatment outcomes. Biomechanical simulations hold high potential by revealing the processes and inner strain conditions of the human body. For reliable simulation results, a model suitable for the application and a way to measure/estimate/predict the human motion behaviour and the interaction with the environment and/or interacting technology are necessary. In this context we refer to a human digital twin as an extension and connection of participant/person-specific biomechanical human models with data streams from clinical observation, operational use of technology or daily life. Each human digital twin is an instance digitally representing a specific person in healthy or pathological state suitable for the specified application.