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

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👇MAchine Learning for Everyone👇

1. What is machine learning

2. Machine learning concepts

3. Machine learning workflow

4. Supervised learning

5. Evaluating performance

6. Improving performance

7. Deep learning

8. Computer vision

9. Natural Language Processing

10. Limits of machine learning