Machine Learning and Data Science for programming beginners using python with scikit-learn, SciPy, Matplotlib & Pandas.

The Program 

1. Introduction to Machine Learning.
2. System and Environment Preparation.
3. Learn Basics of Python.
4. Learn Basics of NumPy.
5. Learn Basics of Matplotlib.
6. Learn Basics of Pandas.
7. Understanding the CSV Data File.
8. Load and Read CSV data file using Python Standard Library.
9. Load and Read CSV Data File Using NumPy.
10. Load and Read CSV Data File Using Pandas.
11. Dataset Summary.
12. Dataset Visualization.
13. Multivariate Dataset Visualization Data Preparation (Pre-Processing).
14. Data Preparation — Standardizing Data.
15. Feature Selection.
16. Refresher Session — The Mechanism of Re-sampling, Training and Testing.
17. Algorithm Evaluation Techniques.
18. Algorithm Evaluation Metrics.
19. Classification Algorithm Spot Check.
20. Regression Algorithm Spot Check.
21. Compare Algorithms.
22. Pipelines Data Preparation and Data Modelling.
23. Performance Improvement Ensembles.
24. Performance Improvement Parameter Tuning.
25. Export, Save and Load Machine Learning Models.
26. Finalizing a Model.
27. Quick Session Imbalanced Data Set — Issue Overview and Steps.
28. Iris Dataset Finalizing Multi-Class Dataset.
29. Finalizing a Regression Model — The Boston Housing Price Dataset.
30. Real-Time Predictions.
31. Source Code.