Supervised And Unsupervised Learning For Data Science

This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a comprehensive reference to the field.

"Kernel Based Algorithms for Mining Huge Data Sets" is the first book treating the fields of supervised, semi-supervised and unsupervised machine learning collectively. The book presents both the theory and the algorithms for mining huge data sets by using support vector machines (SVMs) in an iterative way. It demonstrates how kernel based SVMs can be used for dimensionality reduction (feature elimination) and shows the similarities and differences between the two most popular unsupervised techniques, the principal component analysis (PCA) and the independent component analysis (ICA). The book presents various examples, software, algorithmic solutions enabling the reader to develop their own codes for solving the problems.

Machine Learning with Python in detail both practically and theoretically with machine learning project for data science.

Learn to Master Cluster Analysis and Unsupervised Learning for Data Science, Data Analysis, and Machine Learning [2024]

Become a professional Data Scientist and learn how to use NumPy, Pandas, Machine Learning and more!

Become a professional Data Scientist and learn how to use NumPy, Pandas, Seaborn, Matplotlib, Machine Learning and more!

Become a professional Data Scientist and learn how to use NumPy, Pandas, Machine Learning and more!