Artifact Type: Analytical comparison and application-based evaluation
Machine learning and deep learning are closely related, but they are not interchangeable. This artifact shows how different AI methods are better suited to different problem types. It focuses on matching the right method to the right context rather than treating all AI solutions as the same.
To differentiate between machine learning and deep learning by applying both concepts to realistic scenarios and evaluating their strengths and limitations.
I reviewed core concepts related to machine learning and deep learning, selected representative applications for each approach, and analyzed why each one was a strong fit for its problem. I focused on data structure, feature extraction, computational cost, and practical suitability.
This example represents a traditional machine learning use case. A business can use structured customer data such as contract length, monthly charges, usage history, and account behavior to predict whether a customer is likely to leave. This problem fits machine learning well because the data is structured and models such as logistic regression or support vector machines can provide strong performance without unnecessary complexity.
This example represents a deep learning use case. Image data is high-dimensional and contains complex patterns that are difficult to capture with manual feature engineering. Deep neural networks, especially convolutional neural networks, are well suited for automatically learning useful visual features directly from raw input.
This artifact reflects a core skill in AI/ML work: choosing appropriate methods based on the nature of the problem. It demonstrates my ability to move beyond simple definitions and think critically about practical implementation.