- Defining Machine Learning: What can ML do?
- How ML works?
- Machine Learning terminologies
- Supervised Learning, Unsupervised Learning, Deep Learning
- Supervised Learning: Linear Regression
- train data, test data
- Understanding the equation of a straight line
- feature coefficient (slope, gradient, m)
- bias coeffcient (y-interccept, c)
- domain: x-axis, independent variable
- range: y-axis, dependent variable
- loss function, cost function, objective function, error function
- bias-variance tradeoff, overfitting, underfitting
- ordinary least square method
- gradient descent method
- residual, error, squared error, RMSE - Root Mean Squared Error
- Supervised Learning: Linear Regression
- Accuracy metric in Regression problem
- Mean Absolute Error (MAE)
- Mean Absolute Percentage Error (MAPE)
- Mean Squared Error (MSE)
- Root Mean Squared Error (RMSE)
- R-squared or coefficient of determination
- Prediction result evaluation
- Supervised Learning - Classification: Logistic Regression
- Supervised Learning - Classification: Logistic Regression
- Confusion Matrix
- Accuracy, Precision, Recall/Sensitivity/True Positive Rate, F1 score, False Positive Rate
- ROC: Receiver Operating Characterisitcs and AUC: Area Under the Curve
- Classification report
- Decision Tree Classification and Regression
- Decision Tree Classification
- Cross-Validation
- Grid Search
- Unsupervised Learning
- K-means Clustering
- Elbow method