Difficulty Level
Intermediate
Completion Time
15h36m
Language
English
About Book
Who Is This Book For?
This book has been created for data scientists who want to see machine learning in action and explore its real-world application. With guidance on everything from the fundamentals of machine learning and predictive analytics to the latest innovations set to lead the big data revolution into the future, this is an unmissable resource for anyone dedicated to tackling current big data challenges. Knowledge of programming (Python and R) and mathematics is advisable if you want to get started immediately.
Practical Machine Learning
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 15h36m total length
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
Related Resources
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