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SKU/Artículo: AMZ-B0F1FCR41D

AI for Data Science: How to Use Machine Learning for Data Analysis and Predictions Learn Python, Pandas, and AI Algorithms for Big Data and Analytics

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Paperback

Hardcover

Kindle

Paperback

Detalles del producto

Disponibilidad:
En stock

Peso con empaque:
0.66 kg

Devolución:
Sí

Condición
Nuevo

Producto de:
Amazon

Viaja desde
USA

Sobre este producto

AI for Data Science: How to Use Machine Learning for Data Analysis and Predictions is the ultimate guide to applying artificial intelligence and machine learning techniques to data science. Designed for beginners and intermediate learners, this book teaches you how to leverage Python, Pandas, and AI algorithms to work with big data, uncover insights, and make predictions. Whether you're working with financial data, healthcare records, or large datasets in any field, this book provides the tools to make data-driven decisions using advanced machine learning techniques.By breaking down complex concepts into simple, actionable steps, you’ll learn how to apply machine learning algorithms, build predictive models, and analyze large datasets using Python—without needing a deep background in programming.Inside, you’ll discover:Introduction to Data Science: Understand the fundamentals of data science, the role of machine learning, and how AI can be used to analyze and predict data in various industries.Python and Pandas for Data Analysis: Learn how to use Python and the Pandas library to manipulate, clean, and analyze large datasets. Master the essential functions for data wrangling, exploration, and visualization.Exploratory Data Analysis (EDA): Learn how to perform EDA to uncover patterns, correlations, and insights from raw data. Understand how to visualize data distributions and detect outliers using libraries like Matplotlib and Seaborn.Machine Learning Algorithms: Dive into key machine learning algorithms, such as linear regression, decision trees, and clustering. Learn how to apply them to real-world data problems, evaluate model performance, and fine-tune hyperparameters.Supervised and Unsupervised Learning: Understand the difference between supervised and unsupervised learning and learn how to choose the right algorithm for your data. Implement classification, regression, and clustering algorithms to solve practical problems.Model Evaluation and Selection: Learn how to evaluate machine learning models using metrics like accuracy, precision, recall, and cross-validation. Understand how to compare different models and select the best one for your use case.AI for Predictions and Forecasting: Master how to apply AI algorithms for predictive analytics, including time series forecasting and trend analysis. Understand how to use AI to make accurate predictions based on historical data.Working with Big Data: Explore techniques for handling and processing large datasets using distributed computing frameworks like Apache Spark, and learn how to scale your data science projects for big data environments.Real-World Projects and Case Studies: Apply your knowledge with hands-on projects, such as building a predictive model for sales forecasting, analyzing customer behavior, or predicting stock prices.By the end of this book, you’ll have a solid foundation in using machine learning for data analysis and predictions, and you’ll be ready to apply AI algorithms to big data challenges, unlocking actionable insights that drive decision-making.

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U$S 43,98

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