8. Filling continuous missing values

9. Dealing with other data issues

10. Dealing with stray characters (I)

11. Dealing with stray characters (II)

12. Method chaining

PART 3. Conforming to Statistical Assumptions
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#MODULE9 #FeatureEngineering

1.Data distributions

2.What does your data look like (I)

3.What does your data look like (II)

4.Scaling and transformations

5.Normalization

6.Standardization

7.Log transformation

8.Removing outliers

9.Percentage based outlier removal

10.Statistical outlier removal

11.Scaling and transforming new data

12.Train and testing transformations (I)

13.Train and testing transformations (II)

Self Paced Videos

Module 1 - Introduction to Data Science
Module 2 - Introduction to Python
Module 3 - Pandas
Module 4 - Merging Data Frames with Pandas

Module 5 - Introduction to Data Visualisation Using Matplotlib
https://t.me/c/1454674396/8
Part- 1,2,3,4

Module 6 - Data Visualization with Seaborn
https://t.me/c/1454674396/61
Part - 1,2

Module 7 - Descriptive Statistics with Python
https://t.me/c/1454674396/106

Module 8 - Inferential Statistics with Python
https://t.me/c/1454674396/129

Module 9 - Feature Engineering
https://t.me/c/1454674396/155
Part- 1,2,3,4

Module 9.5 - Exploratory Data Analysis
Module 10 - Machine Learning for Everyone
Module 11 - Supervised Learning with scikit-learn
Module 12 - Machine Learning with Tree-Based Models in Python
Module 12.5 - Extreme Gradient Boosting with XGBoost
Module 13 - Dimensionality Reduction and Unsupervised Learning
Module 14 - Machine Learning for Time Series Data