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[Python] Machine Learning
1. Introduction
1.1. What is AI and ML (22:07)
1.2. Which skill is necessary for ML Engineer (26:04)
2. Libraries for ML
2.1. Python syntax (31:57)
2.2. Python Numpy (18:14)
2.3. Matplotlib (10:36)
2.4. Pandas (22:00)
3. Model
3.1. What is Model (14:45)
3.2. Supervised and Unsupervised learning (9:12)
3.3. Classification và€ regression (11:18)
3.4. Dataset (33:10)
3.5. Practise - House Price prediction (21:45)
3.6. Practise - Gradient Descent (8:39)
3.7. Evaluate Metrics (2:41)
3.8. Accuracy anf confusion matrix (9:00)
3.9. F1-score Precision and Recall (18:56)
3.10. ROC curve and AUC (14:22)
4. Machine Learning Algorithms
4.1.1. Bayes Rule (13:18)
4.1.2. Naive Bayes (13:31)
4.2.1. Clustering Introduction (7:31)
4.2.2. K-Mean Clustering Part 1 (19:42)
4.2.3. K-Mean Clustering part 2 (6:40)
4.2.4. DBSCAN -Density Based (22:49)
4.3. Support Vector Machine(SVM) (33:06)
4.4. K-NEAREST NEIGHBOR (20:56)
4.5. Logistic Regression (9:00)
5. Practise
5.1.1. Titanic (Kaggle) part 1 (20:01)
5.1.2. Titanic (Kaggle) part 2 (20:13)
5.1.3. Titanic (Kaggle) part 3 (19:53)
5.1.4. Titanic (Kaggle) part 4 (20:08)
5.1.5. Titanic (Kaggle) part 5 (29:11)
5.2.1. Digit Recognizer (Kaggle) Part 1 (20:03)
5.2.2. Digit Recognizer (Kaggle) part 2 (20:12)
5.2.3. Digit Recognizer (Kaggle) part 3 (20:06)
5.2.4. Digit Recognizer (Kaggle) part 4 (8:13)
5.2.5. Digit Recognizer (Kaggle) part 5 (18:17)
5.1.4. Titanic (Kaggle) part 4
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