Book Content
chapters • 22h12m total length
1. Practical Machine Learning with Spark using Scala
2. Just enough Linear Algebra for Machine Learning with Spark
3. Spark’s three data musketeers for machine learning – Perfect Together
4. Common Recipes for Implementing a Robust Machine Learning System
5. Practical Machine Learning with Regression and Classification in Spark 2.0 – Part I
6. Practical Machine Learning with Regression and Classification in Spark 2.0 – Part II
7. Recommendation engine that scales with Spark
8. Unsupervised Clustering with Apache Spark 2.0
9. Optimization – Going Down the Hill with the Gradient Descent
10. Build Machine Learning Systems with Decision Tree and Ensemble Models
11. Curse of high-dimensionality in Big Data
12. Implementing Text Analytics with Spark 2.0 ML Library
13. Spark Streaming and Machine Learning Library
