About the Book Real-World Machine Learning will teach you the concepts and techniques you need to be a successful machine learning practitioner without overdosing you on abstract theory and complex mathematics. Found insideBuild your own pipeline based on modern TensorFlow approaches rather than outdated engineering concepts. This book shows you how to build a deep learning pipeline for real-life TensorFlow projects. Found insideThe key to unlocking natural language is through the creative application of text analytics. This practical book presents a data scientist’s approach to building language-aware products with applied machine learning. Found inside – Page iWho This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Found inside – Page 5461 https://radimrehurek.com/gensim/. 2http://pandas.pydata.org/. 3http://scikit-learn.org/stable/. 546 Y. Russac et al. 3.2 Treatment of Categorical ... Found inside – Page xiData Analysis Exploring Music21 and Gensim for Music Data Analysis and ... Luo Chen, and Zhinong Zhong 70 Semi-automated Augmentation of Pandas DataFrames . Found insideIn this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. Found inside... Python Packages Used in Chapters 10–13 Package Purpose pandas Dataframe; ... arrays and calculations NLTK Text processing Gensim Similarity indexing, ... This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. Found inside – Page 79data science use cases 24 data science libraries Gensim 9 Matplotlib 8 NLTK 9 NumPy 8 pandas 8 scikit-learn 8 SciPy 8 ... 282 DataFrame (pandas) 178, 182, 192, 211, 213,214, 366 at attribute 185 describe method 186 dropna method 366 ... Found inside – Page iThe second edition of this book will show you how to use the latest state-of-the-art frameworks in NLP, coupled with Machine Learning and Deep Learning to solve real-world case studies leveraging the power of Python. Key Features This is the first book on pandas 1.x Practical, easy to implement recipes for quick solutions to common problems in data using pandas Master the fundamentals of pandas to quickly begin exploring any dataset Book Description The ... What You'll Learn Understand machine learning development and frameworks Assess model diagnosis and tuning in machine learning Examine text mining, natuarl language processing (NLP), and recommender systems Review reinforcement learning and ... This book helps you to ramp up your practical know-how in a short period of time and focuses you on the domain, models, and algorithms required for deep learning applications. Acquire and analyze data from all corners of the social web with Python About This Book Make sense of highly unstructured social media data with the help of the insightful use cases provided in this guide Use this easy-to-follow, step-by ... You can easily jump to or skip particular topics in the book. You also will have access to Jupyter notebooks and code repositories for complete versions of the code covered in the book. Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn ... Found inside – Page 252While setting a random_state in gensim could help in reproducibility, in general, the topics found using ... Load the dataset into a pandas DataFrame. 4. Found insideThis book constitutes the refereed proceedings of the Third International Conference on Data Mining and Big Data, DMBD 2018, held in Shanghai, China, in June 2018. Found inside – Page 138Converting predictions into a pandas DataFrame This helps us convert the ... i] import numpy as np import gensim print(f"Gensim version: {gensim. Found inside – Page iiThis book: Provides complete coverage of the major concepts and techniques of natural language processing (NLP) and text analytics Includes practical real-world examples of techniques for implementation, such as building a text ... Found insideWe used the Pandas library in Python through a Jupyter notebook interface to manage texts in a Dataframe structure. In addition to offering us the ability ... Found inside – Page 200... import import import import import pandas as pd numpy as np re json gensim math ... DataFrame(data) len(df) X_train = np.reshape(X_train, (len(X_train), ... Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. Starting with the basics, this book teaches you how to choose from the various text pre-processing techniques and select the best model from the several neural network architectures for NLP issues. Found insideBecome an efficient data science practitioner by understanding Python's key concepts About This Book Quickly get familiar with data science using Python 3.5 Save time (and effort) with all the essential tools explained Create effective data ... Found inside – Page iThis book is a good starting point for people who want to get started in deep learning for NLP. Presents case studies and instructions on how to solve data analysis problems using Python. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and ... Found inside... Blueprint: Preparing Data for Gensim for word embeddings, Data Preparation-Phrases, Closing Remarks DataFrame (Pandas), Blueprint: Getting an Overview ... Found inside4. gensim: topic modelling for humans →https://radimrehurek.com/gensim/apiref.html. ... How to Speed up Pandas by 4x with one line of code (KDnuggets): ... Found inside – Page 335... from gensim.summarization import summarize import requests 3. ... The function uses pandas DataFrames so that it displays nicely in the Jupyter ... Found inside – Page ivThis book provides a complete and modern guide to web scraping, using Python as the programming language, without glossing over important details or best practices. This book has numerous coding exercises that will help you to quickly deploy natural language processing techniques, such as text classification, parts of speech identification, topic modeling, text summarization, text generation, entity ... 本_面向所有_机器__与_据挖掘的__及__感_趣的_者，_零_始，以Python_程_言_基_，在不涉及大量__模型与复__程知_的前提下，逐步___者熟悉并且掌握_下最流行的机器__、_据挖掘与自然_言 ... Found inside – Page 1She tells the intriguing story of how Russian journalist Nicolas Notovitch discovered the manuscripts in 1887 in a monastery in Ladakh. Found inside – Page iBridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. Found inside – Page 287列名がないとどの単語に対応するか把握しづらいかもしれません。pandasのデータ ... 現れる単語をカウ 1 2 3 4 5 まずはgensimライブラリを用いて辞書を作成します。 Found insideLearn to build powerful machine learning models quickly and deploy large-scale predictive applications About This Book Design, engineer and deploy scalable machine learning solutions with the power of Python Take command of Hadoop and Spark ... Found insideUsing clear explanations, standard Python libraries and step-by-step tutorial lessons you will discover what natural language processing is, the promise of deep learning in the field, how to clean and prepare text data for modeling, and how ... Found insideWhat you will learn Implement machine learning techniques to solve investment and trading problems Leverage market, fundamental, and alternative data to research alpha factors Design and fine-tune supervised, unsupervised, and reinforcement ... The Handbook of Latent Semantic Analysis is the authoritative reference for the theory behind Latent Semantic Analysis (LSA), a burgeoning mathematical method used to analyze how words make meaning, with the desired outcome to program ... Is accompanied by a supporting website featuring datasets. Applied mathematicians, statisticians, practitioners and students in computer science, bioinformatics and engineering will find this book extremely useful. Chapter 7. Found inside – Page iThis book starts with the fundamentals of Spark and its evolution and then covers the entire spectrum of traditional machine learning algorithms along with natural language processing and recommender systems using PySpark. Learn how to harness the powerful Python ecosystem and tools such as spaCy and Gensim to perform natural language processing, and computational linguistics algorithms. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. 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