- 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
Advanced machine learning models play a critical role in pushing the boundaries of AI and data science. This course delves into sophisticated techniques for building, optimizing, and deploying machine learning models using Google Colab, allowing participants to leverage powerful cloud-based tools for their projects.
This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to enhance their knowledge of machine learning models, improve their skills in hyperparameter tuning, and learn how to deploy models effectively using Google Colab.
By the end of this training, participants will be able to:
- Implement advanced machine learning models using popular frameworks like Scikit-learn and TensorFlow.
- Optimize model performance through hyperparameter tuning.
- Deploy machine learning models in real-world applications using Google Colab.
- Collaborate and manage large-scale machine learning projects in Google Colab.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
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
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