This is a bite-size course to learn Python programming for machine learning and statistical learning. In the CRISP-DM data mining process, machine learning is at the modeling and evaluation stage.

What will you learn in this course?

You will learn Python programming for machine learning. You will be able to train your own prediction models with Naive Bayes, decision tree, KNN, neural network, linear regression, and evaluate your models very soon after learning the course.

I will create applied statistics using Python for data understanding stage and advanced data visualizations for the data understanding stage and includes some data processing for the data preparation stage in the future.

Content

1. Getting Started
2. Getting Started
3. Getting Started 3
4. Getting Started 4
5. Data Mining Process
6. Download Data Set
7. Read Data Set
8. Simple Linear Regression
9. Build Linear Regression Models: Train and Test Set
10. Build and Predict Linear Regression Models
11. KMeans Clustering
12. KMeans Clustering in Python
13. Agglomeration Clustering
14. Agglomeration Clustering in Python
15. Decision Tree ID3 Algorithm
16. Decision Tree in Python
17. KNN Classification
18. KNN in Python
19. Naive Bayes Classification
20. Naive Bayes in Python
21. Neural Network Classification
22. Neural Network in Python
23. What Algorithm to Use?
24. Model Evaluation
25. Model Evaluation using Python for Classification
26. Model Evaluation using Python for Regression