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 typical use cases.
- Introduction to ensemble learning techniques.
Hyperparameter Tuning and Optimization
- Grid search and random search techniques.
- Automating hyperparameter tuning with Google Colab.
- Utilizing advanced optimization techniques (Bayesian optimization, Genetic Algorithms).
Neural Networks and Deep Learning
- Building and training deep neural networks.
- Transfer learning with pre-trained models.
- Optimizing deep learning models for peak performance.
Model Deployment
- Introduction to various 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 within Colab.
- Leveraging 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.
- Addressing bias and fairness in machine learning models.
Real-World Applications and Case Studies
- Applying advanced models in healthcare, finance, and e-commerce sectors.
- Case studies: Successful model deployments.
- Challenges and future trends in advanced machine learning.
Summary and Next Steps
Requirements
- A strong grasp of machine learning algorithms and core concepts.
- Proficiency in Python programming.
- Experience working with Jupyter Notebooks or Google Colab.
Audience
- Data scientists.
- Machine learning practitioners.
- AI engineers.
Testimonials (2)
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Course - MLflow
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
