Machine Learning with Python

This is the bite size course to learn Python Programming for Machine Learning and Statistical Learning. In CRISP DM data mining process, machine learning is at the modeling and evaluation stage. 

You will need to know some Python programming, and you can learn Python programming from my "Create Your Calculator: Learn Python Programming Basics Fast" course.  You will learn Python Programming for machine learning and 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 have created Applied statistics using Python for data understanding stage and advanced data visualizations for data understanding stage and includes some data processing for data preparation stage.

You can look into the following courses to get SVBook Certified Data Miner using Python

SVBook Certified Data Miner using Python are given to people who have completed the following courses:

• - Create Your Calculator: Learn Python Programming Basics Fast (Python Basics)

• - Applied Statistics using Python with Data Processing (Data Understanding and Data Preparation)

• - Advanced Data Visualizations using Python with Data Processing (Data Understanding and Data Preparation)

• - Machine Learning with Python (Modeling and Evaluation)

and passed a 50 questions Exam. The four courses are created to help learner understand about Python programming basics, then applied statistics (descriptive, inferential, regression analysis) and data visualizations (bar chart, pie chart, boxplot, scatterplot matrix, advanced visualizations with seaborn, and Plotly interactive charts ) with data processing basics to understand more about the the data understanding and data preparation stage of IBM CRISP DM model. Learner will then learn about machine learning and confusion matrix, which is the modeling and evaluation stages of the IBM CRISP DM model. Learner will be able to do data mining project after learning the courses.

Content

1. Getting Started

2. Getting Started 2

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 Modela: 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