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!
© Indraneel Chakraborty | 2021