This content originally appeared on DEV Community and was authored by ClickIT - DevOps and Software Development
One of the most common questions when getting into AI is:
Should I start with Machine Learning or jump straight into Deep Learning?
In a recent podcast snippet, we broke down a simple roadmap that avoids frustration and helps you actually improve your models.
Start with Machine Learning (ML)
Before diving into neural networks, it pays off to learn the foundations:
- Data cleaning & preprocessing: understanding the pipeline is key.
- Classical models: linear regression, logistic regression, decision trees.
- Interpretability: you can explain why your model makes a prediction.
This stage builds the intuition you'll need later.
Move into Deep Learning (DL)
Once you’re comfortable with ML basics, it’s time to level up:
- Neural networks for tasks where classic ML hits its limits.
- Specialized architectures like CNNs (vision) or RNNs/Transformers (language).
- Scalability: handling bigger datasets and more complex problems.
Deep Learning is powerful, but without the ML foundations, it’s easy to feel lost.
Here's a snippet from our podcast, how did you approach learning ML vs DL?
💡 We usually share content on AI, ML/DL, and dev tools on YouTube.
This content originally appeared on DEV Community and was authored by ClickIT - DevOps and Software Development
ClickIT - DevOps and Software Development | Sciencx (2025-09-18T20:32:24+00:00) How to Learn Machine Learning vs Deep Learning?. Retrieved from https://www.scien.cx/2025/09/18/how-to-learn-machine-learning-vs-deep-learning/
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