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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
21 Hours
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.
