1. Introduction to Python
7. List Comprehension and Dictionary Comprehension
10. File Handling and Exception Handling
11. Object Oriented Programming using Python
13. Inheritance, Polymorphism, Overloading and Overriding
14. Regular Expression: With Case Study
15. Iterator, Generator and Collection Framework
16. Introduction to Spark
18. Spark Jobs and APIs Spark 2.0 architecture
19. Installation and Configuration
20. Internal workings of an RDD Creating RDDs
21. Global versus local scope
23. Actions Hands on Session on RDD and Spark
24. Checking for duplicates, missing observations, and outliers
25. Getting familiar with your data Visualization
26. Hands on Session Data Modeling Follow
28. Overview of the package
29. Loading and transforming the data
30. Getting to know your data
31. Creating the final dataset
32. Predicting infant survival
33. Hands on Session using PySpark MLib
34. Introduction to ML package
35. Overview of the package
36. Predicting the chances of infant survival with ML
37. Parameter hyper-tuning
38. Other features of PySpark ML in action
39. Implementation of ML Algorithm 1.Random Forest 2.Regression 3.K-means
