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
- Solid understanding of machine learning algorithms and core concepts.
- Proficiency in Python programming.
- Experience using 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
