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Python Data Analysis: Master Python Analytics with Machine Learning, Deep Learning, GenAI, LLMs, and Data Engineering
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Python Data Analysis: Master Python Analytics with Machine Learning, Deep Learning, GenAI, LLMs, and Data Engineering
Python Data Analysis: Master Python Analytics with Machine Learning, Deep Learning, GenAI, LLMs, and Data Engineering
商品＃: 268119645

Python Data Analysis: Master Python Analytics with Machine Learning, Deep Learning, GenAI, LLMs, and Data Engineering

商品＃: 268119645

TWD 2111

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Shop Python Data Analysis: Master Python Analytics with Machine Learning, Deep Learning, GenAI, LLMs, and Data Engineering online at a best price in Taiwan. 1806022877
  • Understand data analysis pipelines using Python Data Analysis, machine learning, pandas, scikit-learn, and data visualization techniques. Build scalable workflows for time series, NLP, image analytics, and big data processing. Key FeaturesPrepare, clean, and transform data with Python, pandas, and exploratory data analysis techniquesApply machine learning with Python using regression, classification, clustering, PCA, and Bayesian methodsScale analytics workflows using Dask, Ray, Modin, and PySparkBook DescriptionModern data analysis goes beyond cleaning and visualizing data. Today's practitioners need to build scalable data pipelines, apply machine learning, work with text and image data, and understand emerging AI techniques such as Generative AI and Large Language Models (LLMs). This guide shows you how to tackle these challenges using Python's modern data ecosystem.Unlike books focused on a single library or technique, this book provides an end-to-end approach to Python data analysis. You'll learn how to move from data preparation and exploratory analysis to machine learning, NLP, image analytics, scalable processing, and AI-powered workflows.Starting with statistical foundations, you'll learn how to clean, transform, wrangle, and visualize data. You'll then explore time series analysis, signal processing, forecasting, and predictive analytics before applying machine learning techniques such as regression, classification, clustering, PCA, probabilistic methods, and Bayesian approaches.The book also covers graph analytics, sentiment analysis, NLP, image analytics, Generative AI, and LLMs. Finally, you'll learn to scale analytics workflows using Dask, Modin, Ray, and PySpark.By the end of the book, you'll be able to build end-to-end data analysis pipelines and apply modern data science and AI techniques to solve real-world challenges.What you will learnPrepare, clean, and transform data for exploratory data analysis and data wranglingAnalyze and visualize data using Python and pandasPerform time series analysis, forecasting, and signal processingApply machine learning with Python using scikit-learn techniquesUse regression, classification, clustering, PCA, and Bayesian methodsPerform sentiment analysis, NLP, graph analytics, and image analyticsAccelerate workflows using Dask, Modin, and RayBuild scalable big data analytics pipelines with PySparkWho this book is forThis book is for data analysts, data scientists, business analysts, statisticians, students, and academic professionals who want to strengthen their Python Data Analysis skills. It is ideal for readers looking to apply data science with Python to real-world problems involving data preparation, visualization, machine learning, NLP, image analytics, and big data processing. A basic understanding of mathematics and working knowledge of Python will help you get the most from this book. Table of ContentsGetting Started with Python LibrariesNumPy and PandasStatistics for Data InsightsLinear AlgebraData VisualizationRetrieving, Processing, and Storing DataCleaning Messy DataTime-Series AnalysisSupervised Learning: Regression and ClassificationUnsupervised Learning: Dimensionality Reduction, Clustering, Anomaly DetectionEnsemble Methods: Bagging and Boosting MethodsArtificial Neural Networks and Deep LearningAnalyzing Text DataAnalyzing Image DataLLMs and Gen AIParallel Computing Using Dask, Modin, and RayBig Data Analytics using PySpark
Publisher Packt Publishing
Publication date 26 Jun. 2026
Edition 4th
Language English
Print length 766 pages
ISBN-10 1806022877
ISBN-13 978-1806022878
Item weight 1.29 kg
Dimensions 19.05 x 4.39 x 23.5 cm

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Python Data Analysis: Master Python Analytics with Machine Learning, Deep Learning, GenAI, LLMs, and Data Engineering

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