MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 ChGenre: eLearning | Language: English + srt | Duration: 14 lectures (3h 4m) | Size: 761.1 MB

Go onto Kaggle website to enter a monthly tabular competition.

Create a Jupyter Notebook to enter a Kaggle monthly tabular competition.
Learn several machine learning techniques to create an algorithm in a Jupyter Notebook.
Select a model to use to train and fit the data.
Make predictions on test data provided in a the competition.
Submit predictions to Kaggle to get on the leaderboard and possibly win a prize.
The learner needs to take my first course, Enter a Kaggle competition, because that contains a lot of preliminary information.
This course gives the Python and Kaggle enthusiast the skills needed to enter Kaggle's tabular competitions and will go over the code of the twelve monthly competition questions for the year 2021. A prerequisite to this course is my first course entitled, "How to enter a Kaggle competition and get on the leaderboard".
Whilst going over the code of the twelve monthly tabular competition questions, the code for these questions will be provided. In addition, the following topics will be discussed:-
1. The student will learn about classification problems.
2. The student will learn about regression problems.
3. The student will learn about multiclass labels.
4. The student will learn about multilabel predictions.
5. The student will learn about class imbalances.
6. The student will learn how to shorted a dataframe.
7. The student will lean about feature selection using SelectKBest.
8. The student will learn about feature selection using SelectPercentile.
9. The student will learn how to normalise data.
10. The student will learn how to standardise data using StandardScaler.
11. The student will learn how to use coding to standardise data.
12. The student will learn how to ordinal code to encode object features.
13. The student will learn how to map data.
14. The student will learn how to one hot encode data.
15. The student will learn how to impute missing values.
16. The student will learn how to select classifiers.
17. The student will learn how to select regressors.
18. The student will learn about neural networks.
19. The student will learn about Pytorch.
Bner to intermediate machine learning practitioners.






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