This repository contains basic to advanced codes related to data science and machine learning concepts using python.
This is a learning endeavour using several online resources (listed under References).

Libraries

• Pandas
• Numpy
• Matplotlib
• Scikit Learn
• TensorFlow 2.3

References

• Complete Machine Learning and Data Science: Zero to Mastery | Taught by Andrei Neagoie & Daniel Bourke
• Data ScienceTutorial for Beginners from Kaggle
• Python for Data Analysis, 2nd Edition - Book
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition - Book
• Machine Learning Crash Course with TensorFlow APIs | Google's fast-paced, practical introduction to machine learning
• Data Science Course at Sololearn
• Machine Learning Course at Sololearn
• Python Documentation
• Numpy Documentation
• Pandas Documentation
• Matplotlib Documentation
• Scikit-Learn User Guide
• TensorFlow Docs - 2.3
• TensorFlow Tutorials
• Broadcast Visualization
• Seaborn Heatmap
• sklearn.metrics.precision_score
• sklearn.metrics.recall_score
• sklearn.metrics.f1_score
• Confusion matrix
• Cross validation
• sklearn.metrics.classification_report
• sklearn.metrics.roc_auc_score
• Model Evaluation
• Beyond accuracy, precision and recall
• Library function for root mean square error rmse in python

Will be updating periodically
Collaborations are always welcome.

Just kidding. Please let me know if you find errors :)
Have fun!

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© Indraneel Chakraborty | 2021