1. Import Data | Coursetakers

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Machine Learning Mastery: From Data to Advanced Classifiers

1. Introduction

1. Introduction (2:01)
2. Installing Jupyter (2:16)
3. How to download Python files (2:21)

2. Course Contents

1. Import Data (6:39)
2. visualizing missing data in a dataset (2:12)
3. calculating statistical information (3:47)
4. checking for duplicate rows in the DataFrame (2:06)
5. calculating the number of distinct values in each column (3:45)
6. checking for missing or null values in the DataFrame (2:21)
7. Cleaning the data (4:39)
8. creating a new column called -Label- in the DataFrame (5:03)
9. creating a histogram plot (7:55)
10. displaying the distribution of the data using a box plot (6:26)
11. displaying the distribution of the data by the different categories (9:05)
12. visualize the relationship between two variables with jointplot (8:40)
13. calculating the correlation matrix of the DataFrame (5:33)
14. creating a mask using NumPy (3:48)
15. creating a color map using seaborn (2:20)
16. creating a heatmap using seaborn (5:42)
17. calculates the number of outliers (14:38)
18. standardizing features (3:47)
19. Hypothesis testing (2:27)
20. Normalization (9:00)
21. split the data into training and testing sets (9:57)
22. Start traning SVC and Learn Hyperparameters (4:33)
23. find the best hyperparameter (6:55)
24. make predictions on the test data and avaluate the model (9:20)
25. Train RandomForestClassifier (6:49)
26. Train XGBClassifier (4:19)
27. Train KNeighborsClassifier (3:22)
28. Train LGBMClassifier (3:57)
29. calculate the (ROC) curve and the (AUC) score (7:03)
30. Supporting Files

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1. Import Data

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