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Duration 14 hours
Course Outline
Foundations of Machine Learning and Google Colab
- Introduction to machine learning principles
- Initial setup of the Google Colab environment
- Review of key Python concepts
Supervised Learning using Scikit-learn
- Building regression models
- Developing classification models
- Techniques for model assessment and optimization
Unsupervised Learning Methodologies
- Exploring clustering algorithms
- Strategies for dimensionality reduction
- Learning association rules
Advanced Concepts in Machine Learning
- Deep learning and neural network architectures
- Application of support vector machines
- Implementation of ensemble methods
Specialized Topics in Machine Learning
- Effective feature engineering
- Optimizing hyperparameters
- Enhancing model interpretability
The Machine Learning Project Lifecycle
- Preprocessing data for analysis
- Selecting appropriate models
- Deploying trained models
Capstone Project
- Formulating the problem statement
- Collecting and refining data
- Training and assessing the final model
Conclusion and Future Pathways
Requirements
- Foundational knowledge of core programming principles
- Practical experience writing code in Python
- Basic familiarity with statistical concepts
Target Audience
- Data scientists
- Software developers
Testimonials (2)
Getting people that never used AI some repetition in prompting and people that do use AI to consider different methods to using it.
Matthew Gay - Tarsus Pharmaceuticals
Course - Artificial Intelligence (AI) Overview
Working from first principles in a focused way, and moving to applying case studies within the same day
