“Ultimate Machine Learning with Scikit-Learn” is a comprehensive book presenting a detailed system of mastering data preparation and predicting modeling in Python using Scikit-Learn. From establishing the basics of data analysis techniques to learning about logistic regression and decision trees, this book assists in supplying key capabilities for strong analysis. You will understand time series data, how to deal with unstructured data by applying Naive Bayes, and dynamic approaches for data stream by K-nearest neighbors and SVMs.
Further on, “Ultimate Machine Learning with Scikit-Learn” assists in protecting analyses from anomalies with isolation forests and boost the prediction capabilities of ensemble learning methods, especially in stock market data analysis. When you finish the book, you will be able to build data engineering and ML pipelines effectively and for that reason you’ll never lack confidence when solving any complex analytics problem.
Ultimate Machine Learning with Scikit-Learn Table of Contents:
- Data Preprocessing with Linear Regression
- Structured Data and Logistic Regression
- Time-Series Data and Decision Trees
- Unstructured Data Handling and Naive Bayes
- Real-time Data Streams and K-Nearest Neighbors
- Sparse Distributed Data and Support Vector Machines
- Anomaly Detection and Isolation Forests
- Stock Market Data and Ensemble Methods
- Data Engineering and ML Pipelines for Advanced Analytics
Who is this course for?
- As this study discussed, data scientists need to have more profound insights into several machine-learning approaches that will be used to solve real-life problems.
- Python coders are seeking real applications in data preprocessing and modeling.
- Data scientists, business analysts, data engineers, and professionals who wish to build their capacity in big data analysis.
Click on the links below to Download Ultimate Machine Learning with Scikit-Learn!
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