The statquest illustrated guide to machine learning
Material type:![Text](/opac-tmpl/lib/famfamfam/BK.png)
- 9798986924021
- 006.31 STA-S
Contents:
1. Fundamental Concepts in Machine Learning!!! 2. Cross Validation!!! 3. Fundamental Concepts in Statistics!!! 4. Linear Regression!!! 5. Gradient Descent!!! 6. Logistic Regression!!! 7. Naive Bayes!!! 8. Assessing Model Performance!!! 9. Preventing Overfitting with Regularization!!! 10. Decision Trees!!! 11. Support Vector Classifiers and Machines (SVMs)!!! 12. Neural Networks!!!
Item type | Current library | Collection | Call number | Status | Notes | Date due | Barcode | Item holds |
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IIITD Reference | Computer Science and Engineering | CB 006.31 STA-S (Browse shelf(Opens below)) | Available | DBT Project Grant | 012690 |
Total holds: 0
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CB 006.31 MOR-D Grokking deep reinforcement learning | CB 006.31 MUR-M Machine learning : a probabilistic perspective | CB 006.310 MUR-P Probabilistic machine learning : advanced topics | CB 006.31 STA-S The statquest illustrated guide to machine learning | CB 006.31 SUT-R Reinforcement learning : an introduction | CB 006.31 TRA-D Grokking deep learning | CB 006.312 WIC-R R for data science : |
1. Fundamental Concepts in Machine Learning!!! 2. Cross Validation!!! 3. Fundamental Concepts in Statistics!!! 4. Linear Regression!!! 5. Gradient Descent!!! 6. Logistic Regression!!! 7. Naive Bayes!!! 8. Assessing Model Performance!!! 9. Preventing Overfitting with Regularization!!! 10. Decision Trees!!! 11. Support Vector Classifiers and Machines (SVMs)!!! 12. Neural Networks!!!
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