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Duration 14 hours
Course Outline
Foundations of Machine Learning and Google Colab
- General overview of machine learning
- Setting up the Google Colab environment
- Key Python concepts refresher
Supervised Learning using Scikit-learn
- Regression modeling
- Classification modeling
- Techniques for model evaluation and optimization
Unsupervised Learning Approaches
- Clustering algorithms
- Dimensionality reduction methods
- Learning association rules
Advanced Machine Learning Principles
- Neural networks and deep learning
- Support vector machines
- Ensemble learning methods
Specialized Topics in Machine Learning
- Feature engineering techniques
- Hyperparameter tuning
- Enhancing model interpretability
Machine Learning Project Lifecycle
- Data preprocessing steps
- Strategies for model selection
- Deploying machine learning models
Capstone Project
- Defining the problem statement
- Data collection and cleaning processes
- Model training and performance evaluation
Conclusion and Future Directions
Requirements
- A solid grasp of fundamental programming concepts
- Practical experience with Python programming
- Knowledge of basic statistical principles
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
