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
Parag Saxena
Editorial: Orange Education Pvt Ltd
Sinopsis
Master the Art of Data Munging and Predictive Modeling for Machine Learning with Scikit-LearnBook Description“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.Table of Contents1. Data Preprocessing with Linear Regression2. Structured Data and Logistic Regression3. Time-Series Data and Decision Trees4. Unstructured Data Handling and Naive Bayes5. Real-time Data Streams and K-Nearest Neighbors6. Sparse Distributed Data and Support Vector Machines7. Anomaly Detection and Isolation Forests8. Stock Market Data and Ensemble Methods9. Data Engineering and ML Pipelines for Advanced Analytics Index
Disponible desde: 06/05/2024.
Longitud de impresión: 394 páginas.
