Practical Machine Learning > PACKT 원서리스트

1: Introduction to Machine learning
Machine learning
Performance measures
Some complementing fields of Machine learning
Machine learning process lifecycle and solution architecture
Machine learning algorithms
Machine learning tools and frameworks
Summary
2: Machine learning and Large-scale datasets
Big data and the context of large-scale Machine learning
Algorithms and Concurrency
Technology and implementation options for scaling-up Machine learning
Summary
3: An Introduction to Hadoop's Architecture and Ecosystem
Introduction to Apache Hadoop
Machine learning solution architecture for big data (employing Hadoop)
Hadoop 2.x
Summary
4: Machine Learning Tools, Libraries, and Frameworks
Machine learning tools – A landscape
Apache Mahout
R
Julia
Python
Apache Spark
Spring XD
Summary
5: Decision Tree based learning
Decision trees
Implementing Decision trees
Summary
6: Instance and Kernel Methods Based Learning
Instance-based learning (IBL)
Kernel methods-based learning
Summary
7: Association Rules based learning
Association rules based learning
Implementing Apriori and FP-growth
Summary
8: Clustering based learning
Clustering-based learning
Types of clustering
The k-means clustering algorithm
Implementing k-means clustering
Summary
9: Bayesian learning
Bayesian learning
Implementing Naïve Bayes algorithm
Summary
10: Regression based learning
Regression analysis
Regression methods
Implementing linear and logistic regression
Summary
11: Deep learning
Background
Deep learning taxonomy
Implementing ANNs and Deep learning methods
Summary
12: Reinforcement learning
Reinforcement Learning (RL)
Reinforcement learning solution methods
Summary
13: Ensemble learning
Ensemble learning methods
Implementing ensemble methods
Summary
14: New generation data architectures for Machine learning
Evolution of data architectures
Emerging perspectives & drivers for new age data architectures
Modern data architectures for Machine learning
Summary
backindex: Appendix A: Index