Machine Learning for Beginner

1. Fundamental of Machine Learning; Introduction, types of machine learning, applications
2. Supervised, Unsupervised and Reinforcement learning
3. Principal Component Analysis (PCA); Introduction, mathematical and graphical concepts
4. Confusion matrix, Under-fitting and Over-fitting, classification and regression of machine model
5. Support Vector Machine (SVM) Classifier; Introduction, linear and non-linear SVM model, optimal hyperplane, kernel trick, project in Python
6. K-Nearest Neighbors (KNN) Classifier; Introduction, k-value, Euclidean and Manhattan distances, outliers, project in Python
7. Naive Bayes Classifier; Introduction, Bayes rule, project in Python
8. Logistic Regression Classifier; Introduction, non-linear logistic regression, sigmoid function, project in Python
9. Decision Tree Classifier; Introduction, project in Python