Practical Applications and Choosing the Right Approach

Advanced

🧭 Choosing Between AI, ML, and Deep Learning

Choosing between AI, ML, and Deep Learning depends on the problem complexity, data size, and computational resources:

  • 🤖 AI (Rule-Based): For straightforward, well-defined tasks such as simple automation or decision trees.
  • 📊 Machine Learning: Suitable for problems with structured data, like fraud detection, customer churn prediction, or recommendation systems.
  • 🧠 Deep Learning: Best for unstructured data like images, audio, and text, for example, facial recognition, speech-to-text, and language translation.

📌 Example Scenario

  • ✉️ Developing an email spam filter:
    ML algorithms like Naive Bayes or Logistic Regression are effective.

  • 🚗 Creating a self-driving car vision system:
    Deep Learning with Convolutional Neural Networks (CNNs) is essential.

💡 Practical Tip

Evaluate:

  • 📂 Data availability
  • 🧩 Task complexity
  • ⚙️ Computational resources

📈 Starting simple with ML is common, then advancing to DL when more complex pattern recognition is needed.