Machine Learning
Explore a preview of a Machine Learning Curriculum. To interact with nodes, view detailed descriptions, and expand the map, open this curriculum in the interactive workspace.
Core Concepts in Machine Learning
The curriculum framework above covers the following key areas. Open the workspace to dive deeper into any of these concepts:
- Mathematics Foundations
- Linear Algebra
- Calculus
- Probability and Statistics
- Data Representation
- Optimization Concepts
- Uncertainty and Inference
- Feature Engineering
- Model Training
- Supervised Learning
- Regression
- Classification
- Evaluation Metrics
- Model Selection
- Regularization
- Cross Validation
- Ensemble Methods
- Decision Trees
- Random Forests
- Boosting
- Unsupervised Learning
- Clustering
- Dimensionality Reduction
- Neural Networks
- Deep Learning
- Convolutional Networks
- Recurrent Networks
- Practical Deployment
- Model Monitoring
- Ethics and Fairness