Key Learning Areas:
1. Machine Learning Concepts:
- Develop a deep understanding of foundational machine learning principles.
- Explore the nuances of supervised and unsupervised learning techniques.
2. Feature Engineering:
- Learn techniques to extract relevant features from data.
- Understand the crucial role of feature engineering in building effective models.
3. Model Evaluation and Application:
- Delve into methods for evaluating model performance.
- Apply machine learning concepts to real-world scenarios for practical solutions.