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Data Science Solutions with Python Fast and Scalable Models Using Keras, PySpark MLlib, H2O, XGBoost, and Scikit-Learn

What You Will Learn Understand widespread supervised and unsupervised learning, including key dimension reduction techniques Know the big data analytics layers such as data visualization, advanced statistics, predictive analytics, machine learning, and deep learning Integrate big data frameworks wit...

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Bibliographic Details
Main Author: Nokeri, Tshepo Chris
Format: eBook
Language:English
Published: Berkeley, CA Apress 2022, 2022
Edition:1st ed. 2022
Subjects:
Machine Learning
Data Analysis And Big Data
Artificial Intelligence
Quantitative Research
Python
Python (computer Program Language)
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
Table of Contents:
  • Chapter 1: Understanding Machine Learning and Deep Learning
  • Chapter 2: Big Data Frameworks and ML and DL Frameworks
  • Chapter 3: The Parametric Method – Linear Regression
  • Chapter 4: Survival Regression Analysis.-Chapter 5:The Non-Parametric Method - Classification
  • Chapter 6:Tree-based Modelling and Gradient Boosting
  • Chapter 7: Artificial Neural Networks
  • Chapter 8: Cluster Analysis using K-Means
  • Chapter 9: Dimension Reduction – Principal Components Analysis
  • Chapter 10: Automated Machine Learning

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