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💡 Note for Readers:
- Certain lines in this document contain comments that may appear unnecessary. These comments are included solely for my study purposes. Thank you for your understanding.
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Python : This python for python developers, but data science aspirants should also know more about it, so check it out
- Definition, importance, and real-world applications
- difference between data science, data analytics and machine learning
- Data collection, exploration, modeling, evaluation, and deployment.
- python, tablue, R, sql
- case study
- IBM Explanation
- python vs R
- jupyter notebook
- Data Preprocessing steps
- Test , train and validation data
- Modelling Approaches
- Deployment platforms
- deaplarning, machine learning , AI and data science