Course Outline
Introduction to Advanced Machine Learning Models
- Overview of complex models: Random Forests, Gradient Boosting, Neural Networks
- When to use advanced models: Best practices and use cases
- Introduction to ensemble learning techniques
Hyperparameter Tuning and Optimization
- Grid search and random search techniques
- Automating hyperparameter tuning with Google Colab
- Using advanced optimization techniques (Bayesian, Genetic Algorithms)
Neural Networks and Deep Learning
- Building and training deep neural networks
- Transfer learning with pre-trained models
- Optimizing deep learning models for performance
Model Deployment
- Introduction to model deployment strategies
- Deploying models in cloud environments using Google Colab
- Real-time inference and batch processing
Working with Google Colab for Large-Scale Machine Learning
- Collaborating on machine learning projects in Colab
- Using Colab for distributed training and GPU/TPU acceleration
- Integrating with cloud services for scalable model training
Model Interpretability and Explainability
- Exploring model interpretability techniques (LIME, SHAP)
- Explainable AI for deep learning models
- Handling bias and fairness in machine learning models
Real-World Applications and Case Studies
- Applying advanced models in healthcare, finance, and e-commerce
- Case studies: Successful model deployments
- Challenges and future trends in advanced machine learning
Summary and Next Steps
Requirements
- Strong understanding of machine learning algorithms and concepts
- Proficiency in Python programming
- Experience with Jupyter Notebooks or Google Colab
Audience
- Data scientists
- Machine learning practitioners
- AI engineers
Testimonials (2)
I really liked the end where we took the time to play around with CHAT GPT. The room was not set up the best for this- instead of one large table a couple of small ones so we could get into small groups and brainstorm would have helped
Nola - Laramie County Community College
Course - Artificial Intelligence (AI) Overview
Working from first principles in a focused way, and moving to applying case studies within the same day
