Practical Machine Learning
Learn how to build Machine Learning applications to solve real-world data analysis challenges with this Machine Learning book – packed with practical tutorials
Publisher:Packt Publishing Limited
By: Sunila Gollapudi
Paid access
|Jun 2024
This title is not yet available for download
This title is not yet available for download
Table of Contents
- Introduction to Machine learning
- Context of Large datasets for Machine learning
- Hadoop as a Machine learning platform
- ML tools and frameworks (R, Mahout, Julia, Spark and Python)
- Decision Tree learning methods
- Instance based & Kernel learning methods (KNN and SVM)
- Association rule based learning methods (Apriori& FP-growth)
- Clustering based learning methods (K-means)
- Supervised & Unsupervised Learning: Linear Methods
- Unsupervised Learning: Clustering Methods
- Deep Learning Methods
- Reinforcement learning
- Summary of all the large scale machine learning frameworks and tools
- Looking Ahead: Lamda Architectures, Polyglot Persistence and Semantic Data Platforms for Machine Learning
PDF ISBN: 978-1-78439-401-1
Publisher: Packt Publishing Limited
Copyright owner: © 2016 Packt Publishing Limited
Publication date: 2024
Language: English
Pages: 468
Related subjects:
BISAC subjects:
COM005030,
COM051360,
COM051280
Thema subjects:
UFL,
KJC,
UMX
