Basics of Machine Learning: Machine Learning and Materials/Chemistry

Basics of Machine Learning: Machine Learning and Materials/Chemistry

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Basics of Machine Learning: Machine Learning and Materials/Chemistry

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Basics of Machine Learning: Machine Learning and Materials/Chemistry

1. Introduction to Machine Learning

│ │ ├─ 001.0. Software Installation.mp4

│ │ ├─ 002.1. Introduction to Machine Learning.mp4

10. XGBoost (4 sections)

│ │ ├─ 001.10.1. Coulomb Matrix.mp4

│ │ ├─ 002.10.2. XGBoost.mp4

│ │ ├─ 003.10.3. Predicting Perovskite Formation Energy.mp4

│ │ ├─ 004.10.4. Principal Component Analysis.mp4

11. Clustering (1 section)

│ │ ├─ 001.11. Clustering Algorithm.mp4

12. Frontiers of Machine Learning (1 section)

│ │ ├─ 001.12. Frontiers of Machine Learning.mp4

2. Simple Models

│ │ ├─ 001.2.1. Linear Regression Algorithm.mp4

│ │ ├─ 002.2.2. Bimetallic Adsorption Energy.mp4

│ │ ├─ 003.2.3. Multiple Linear Regression.mp4

│ │ ├─ 004.2.4. Predicting Melting Points.mp4

│ │ ├─ 005.2.5. Prediction-Actual Value Plot.mp4

│ │ ├─ 006.2.6. Quadratic Regression Model.mp4

3. Model Evaluation

│ │ ├─ 001.3.1. Underfitting and Overfitting.mp4

│ │ ├─ 002.3.2. Holdout Method.mp4

│ │ ├─ 003.3.3. Cross-Validation Method.mp4

│ │ ├─ 004.3.4. Bootstrap Method.mp4

│ │ ├─ 005.3.5. Irrelevant Data.mp4

4. Classification Algorithms

│ │ ├─ 001.4.1. Decision Tree Classification.mp4

│ │ ├─ 002.4.2. Organic Small Molecule Classification.mp4

│ │ ├─ 003.4.3. Probability.mp4

│ │ ├─ 004.4.4. Bayesian Classification.mp4

│ │ ├─ 005.4.5. Accuracy.mp4

│ │ ├─ 006.4.6. k-Nearest Neighbors Classification.mp4

│ │ ├─ 007.4.7. MOF Classification (k-Nearest Neighbors).mp4

│ │ ├─ 008.4.8. Support Vector Machine Classification.mp4

│ │ ├─ 009.4.9. MOF Classification (Support Vector Machine).mp4

│ │ ├─ 010.4.10. P-R Curve.mp4

│ │ ├─ 011.4.11. ROC Curve.mp4

5. Regression Algorithms

│ │ ├─ 001.5.1. Literature Data Import.mp4

│ │ ├─ 002.5.2. Partial Least Squares Principle.mp4

│ │ ├─ 003.5.3. Predicting d-band Center (Partial Least Squares).mp4

│ │ ├─ 004.5.4. Gaussian Process Regression.mp4

│ │ ├─ 005.5.5. Data Correlation.mp4

6. Advanced Regression Algorithms

│ │ ├─ 001.6.1. Support Vector Machine Regression.mp4

│ │ ├─ 002.6.2. Neural Networks.mp4

7. Machine Learning and High-throughput Screening

│ │ ├─ 001.7.1. Matminer Library.mp4

│ │ ├─ 002.7.2. High-throughput Screening.mp4

│ │ ├─ 003.7.2.1 Solutions for Slow Characterization.mp4

│ │ ├─ 004.7.3 Using MP Database.mp4

8. Database Machine Learning

│ │ ├─ 001.8.1. Predicting Bulk Modulus.mp4

│ │ ├─ 002.8.2. Decision Regression Trees.mp4

│ │ ├─ 003.8.3. Cross-Validation Prediction.mp4

│ │ ├─ 004.8.4. Viewing Error Distribution.mp4

9. Ensemble Learning

│ │ ├─ 001.9.1. Feature Importance.mp4

│ │ ├─ 002.9.2. Random Forest.mp4

│ │ ├─ 003.9.3. Extremely Randomized Trees.mp4

│ │ ├─ 004.9.4. AdaBoost.mp4

│ │ ├─ 005.9.5. Gradient Boosting.mp4

│ │ ├─ 006.9.6. Hyperparameter Tuning.mp4

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