What You Need to Know Before
You Start
Starts 8 June 2025 03:05
Ends 8 June 2025
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24 minutes
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Overview
Explore advanced techniques for optimizing machine learning models and improving their performance in real-world applications.
Syllabus
- Introduction to Advanced Machine Learning
- Model Optimization Techniques
- Advanced Algorithms and Techniques
- Improving Model Generalization
- Real-World Applications and Performance
- Model Evaluation and Interpretation
- Ethics and Responsibilities in Machine Learning
- Future Directions in Machine Learning
- Final Review and Project
Overview of Course Objectives
Understanding Real-World Application Challenges
Hyperparameter Tuning
Automatic ML (AutoML) Tools
Feature Selection and Engineering
Ensemble Learning Methods
Bagging, Boosting, and Stacking
Dimensionality Reduction Techniques
PCA, t-SNE, LDA
Neural Network Optimizations
Dropout, Batch Normalization, Learning Rate Schedules
Regularization Techniques
L1 and L2 Regularization
Cross-Validation Strategies
K-Fold, Leave-One-Out
Error Analysis and Mitigation
Case Studies of ML Implementation
Dealing with Imbalanced Datasets
Scalability and Deployment
Advanced Metrics for Model Evaluation
Precision, Recall, F1-Score, AUC-ROC
Interpretability Tools
SHAP, LIME
Bias and Fairness Considerations
Privacy and Security Concerns
Trends and Emerging Technologies
The Role of AI in Society
Course Summary
Real-World Project Application
Presentations and Feedback Session
Subjects
Conference Talks
