CURRICULUM
- What is machine learning?
- Learning system model
- Training and testing
- Performance
- Algorithms
- Machine learning structure
- What are we seeking?
- Learning techniques
- Applications
- Instance Based Classifiers
- Nearest-Neighbor Classifiers
- Lazy vs. Eager Learning
- k-NN variations
- How to determine the good value for k
- When to Consider Nearest Neighbors
- Condensing
- Nearest Neighbour Issues
- Naïve Bayes Learning
- Conditional Probability
- Bayesian Theorem: Basics
- The Bayes Classifier
- Model Parameters
- Naïve Bayes Training
- Types of errors
- Sensitivity and Specificity
- ROC Curve
- Key Requirements
- Decision Tree as a Rule Set
- How to Create a Decision Tree
- Choosing Attributes
- ID3 Heuristic
- Entropy
- Pruning Trees – Pre and Post
- Subtree Replacement, Raising
- Cross-validation
- Ensemble Approaches
- Bagging Model
- Boosting
- Gradient Boosting
- Random Forests
- Advantages, Disadvantages
- Background of Brain and Neuron
- Neural Networks
- Neurons Diagram
- Neuron Models- step function
- Perceptrons
- Network Architectures
- single-layer feed-forward
- Support Vector Machines for Classification
- Linear Discrimination
- Nonlinear Discrimination
- SVM Mathematically
- Extensions
- Application in Drug Design
- Data Classification
- Kernel Functions
- XgBoost for Classification
- What is XGBoost? Why is it so good?
- How does XGBoost work?
- Understanding XGBoost Tuning Parameters
- Adaboost usuage with examples