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Duration 21 hours
Course Outline
Introduction to Advanced Machine Learning Models
- Examination of complex models: Random Forests, Gradient Boosting, and Neural Networks
- Identifying optimal use cases and best practices for advanced models
- Fundamentals of ensemble learning techniques
Hyperparameter Tuning and Optimization
- Techniques for grid search and random search
- Automating hyperparameter tuning processes within Google Colab
- Application of advanced optimization methods, including Bayesian optimization and Genetic Algorithms
Neural Networks and Deep Learning
- Construction and training of deep neural networks
- Leveraging transfer learning with pre-trained models
- Performance optimization strategies for deep learning models
Model Deployment
- Overview of strategic model deployment approaches
- Deploying models in cloud environments via Google Colab
- Managing real-time inference and batch processing workflows
Leveraging Google Colab for Large-Scale Machine Learning
- Collaborative workflows for machine learning projects in Colab
- Utilizing Colab for distributed training and GPU/TPU acceleration
- Integration with cloud services for scalable model training
Model Interpretability and Explainability
- Exploration of interpretability techniques such as LIME and SHAP
- Principles of Explainable AI for deep learning models
- Addressing bias and ensuring fairness in machine learning models
Real-World Applications and Case Studies
- Application of advanced models in healthcare, finance, and e-commerce sectors
- Analysis of successful model deployment case studies
- Discussion of current challenges and future trends in advanced machine learning
Summary and Next Steps
Requirements
- Comprehensive understanding of machine learning algorithms and core concepts
- Strong proficiency in Python programming
- Practical experience with Jupyter Notebooks or Google Colab
Target Audience
- Data scientists
- Machine learning practitioners
- AI engineers
Testimonials (2)
Getting people that never used AI some repetition in prompting and people that do use AI to consider different methods to using it.
Matthew Gay - Tarsus Pharmaceuticals
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Working from first principles in a focused way, and moving to applying case studies within the same day
