Ultimate Machine Learning with Scikit-Learn: Unleash the Power of Scikit-Learn and Python

Автор: literator от 10-05-2024, 20:39, Коментариев: 0


Название: Ultimate Machine Learning with Scikit-Learn: Unleash the Power of Scikit-Learn and Python to Build Cutting-Edge Predictive Modeling Applications and Unlock Deeper Insights Into Machine Learning
Автор: Parag Saxena
Издательство: Orange Education Pvt Ltd, AVA
Год: 2024
Страниц: 411
Язык: английский
Формат: pdf, epub (true)
Размер: 10.1 MB

Master the Art of Data Munging and Predictive Modeling for Machine Learning with Scikit-Learn.

“Ultimate Machine Learning with Scikit-Learn” is a definitive resource that offers an in-depth exploration of data preparation, modeling techniques, and the theoretical foundations behind powerful machine learning algorithms using Python and Scikit-Learn.

Beginning with foundational techniques, you'll dive into essential skills for effective data preprocessing, setting the stage for robust analysis. Next, logistic regression and decision trees equip you with the tools to delve deeper into predictive modeling, ensuring a solid understanding of fundamental methodologies. You will master time series data analysis, followed by effective strategies for handling unstructured data using techniques like Naive Bayes.

Transitioning into real-time data streams, you'll discover dynamic approaches with K-nearest neighbors for high-dimensional data analysis with Support Vector Machines (SVMs). Alongside, you will learn to safeguard your analyses against anomalies with isolation forests and harness the predictive power of ensemble methods, in the domain of stock market data analysis.

By the end of the book you will master the art of data engineering and ML pipelines, ensuring you're equipped to tackle even the most complex analytics tasks with confidence.

The chapters are as follows:
Chapter 1. Data Preprocessing with Linear Regression: This chapter recalls the foundational lessons established in earliest projects, setting the stage for the powerful analytics to follow.
Chapter 2. Structured Data and Logistic Regression: This chapter reflects the strategic thinking honed during my first Kaggle competition—a victory that showcased the might of simple yet effective models.
Chapter 3. Time-Series Data and Decision Trees: Drawing on experiences in stock market prediction, this chapter emphasizes the importance of understanding historical data to forecast future trends.
Chapter 4. Unstructured Data Handling and Naive Bayes: The chapter mirrors the endeavors to decode the complexity of natural language, turning unstructured murmurs into structured insights.
Chapter 5. Real-time Data Streams and K-Nearest Neighbors: Inspired by real-time data applications, this chapter highlights the critical role of both speed and accuracy in such scenarios.
Chapter 6. Sparse Distributed Data and Support Vector Machines: This chapter encapsulates experiences in harnessing the power of distributed systems to predict and plan with greater precision.
Chapter 7. Anomaly Detection and Isolation Forests: This chapter acknowledges the development of models to safeguard systems from the unexpected, finding patterns in the outliers.
Chapter 8. Stock Market Data and Ensemble Methods: This chapter captures the essence of crafting scalable solutions for the ever-expanding universe of big data.
Chapter 9. Data Engineering and ML Pipelines for Advanced Analytics: This chapter demonstrates a credit case fraud case study which combines data engineering, model building and deployment.

This book is an invitation to embark on a journey of exploration and enlightenment. It aspires to serve as a beacon for those who seek to navigate the rich and complex world of Machine Learning.

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