Lecture 1: Difference between Supervised Learning & Unsupervised Learning

• Defining Machine Learning: What can ML do?
• How ML works?
• Machine Learning terminologies
• Supervised Learning, Unsupervised Learning, Deep Learning

Lecture 2: Supervised Learning: Linear Regression

• 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

Lecture 3: Supervised Learning: Linear Regression and Regression accuracy metrics

• 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

Lecture 4: Supervised Learning - Classification: Logistic Regression

• Supervised Learning - Classification: Logistic Regression

Lecture 5: Supervised Learning - Classification: Accuracy Metrics

• 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

Lecture 6: Supervised Learning - Decision Tree

• Decision Tree Classification and Regression

Lecture 7: Supervised Learning - Decision Tree, Cross-validation and Grid Search

• Decision Tree Classification
• Cross-Validation
• Grid Search

Lecture 8: Unsupervised Learning - K-means Clustering

• Unsupervised Learning
• K-means Clustering
• Elbow method