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Course Outline
Hyperparameter Tuning and Optimization
Introduction to Advanced Machine Learning Models
Model Deployment
Model Interpretability and Explainability
Neural Networks and Deep Learning
Real-World Applications and Case Studies
Summary and Next Steps
Working with Google Colab for Large-Scale Machine Learning
- Applying advanced models in healthcare, finance, and e-commerce
- Case studies: Successful model deployments
- Challenges and future trends in advanced machine learning
- Building and training deep neural networks
- Transfer learning with pre-trained models
- Optimizing deep learning models for performance
- Collaborating on machine learning projects in Colab
- Using Colab for distributed training and GPU/TPU acceleration
- Integrating with cloud services for scalable model training
- Exploring model interpretability techniques (LIME, SHAP)
- Explainable AI for deep learning models
- Handling bias and fairness in machine learning models
- Grid search and random search techniques
- Automating hyperparameter tuning with Google Colab
- Using advanced optimization techniques (Bayesian, Genetic Algorithms)
- Introduction to model deployment strategies
- Deploying models in cloud environments using Google Colab
- Real-time inference and batch processing
- 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
Requirements
Audience
- Data scientists
- Machine learning practitioners
- AI engineers
- Strong understanding of machine learning algorithms and concepts
- Proficiency in Python programming
- Experience with Jupyter Notebooks or Google Colab
21 Hours
Delivery Options
Private Group Training
Our identity is rooted in delivering exactly what our clients need.
- Pre-course call with your trainer
- Customisation of the learning experience to achieve your goals -
- Bespoke outlines
- Practical hands-on exercises containing data / scenarios recognisable to the learners
- Training scheduled on a date of your choice
- Delivered online, onsite/classroom or hybrid by experts sharing real world experience
Private Group Prices RRP from €6840 online delivery, based on a group of 2 delegates, €2160 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
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Public Training
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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.
