Book Content
chapters • 15h36m total length
1. Introduction to Machine learning
2. Context of Large datasets for Machine learning
3. Hadoop as a Machine learning platform
4. ML tools and frameworks (R, Mahout, Julia, Spark and Python)
5. Decision Tree learning methods
6. Instance based & Kernel learning methods (KNN and SVM)
7. Association rule based learning methods (Apriori& FP-growth)
8. Clustering based learning methods (K-means)
9. Supervised & Unsupervised Learning: Linear Methods
10. Unsupervised Learning: Clustering Methods
11. Deep Learning Methods
12. Reinforcement learning
13. Summary of all the large scale machine learning frameworks and tools
14. Looking Ahead: Lamda Architectures, Polyglot Persistence and Semantic Data Platforms for Machine Learning
