PART 4. Dealing with text Data
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#MODULE9 #FeatureEngineering

1.Encoding text

2.Cleaning up your text

3.High level text features

4.Word counts

5.Counting words I

6.Counting words II

7.Limiting your features

8.Text to DataFrame

9.Term frequency-inverse document frequenc

10.Tf-idf

11.Inspecting Tf-idf values

12.Transforming unseen data

13.N-grams

14.Using longer n-grams

15.Finding the most common words

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
https://t.me/Machine_Learning_Deep_Learning/225

Module 11 - Supervised Learning with scikit-learn
https://t.me/Machine_Learning_Deep_Learning/241
Part - 1,2,3,4

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