Python is the default glue language for AI prototypes: notebooks for exploration, libraries for classic ML, and HTTP clients for cloud model APIs. This track stays conceptual—implementation depth lives on specialized courses.
Typical stack layers
- Data — pandas, SQL connectors
- Classic ML — scikit-learn for baselines
- Deep learning — PyTorch / TensorFlow (when needed)
- Serving — FastAPI, batch jobs, cloud functions
Scikit-learn baseline pattern
# pip install scikit-learn
# Conceptual fit/predict — run locally with small CSV
from sklearn.linear_model import LogisticRegression
X = [[1.0], [2.0], [3.0], [4.0]]
y = [0, 0, 1, 1]
model = LogisticRegression()
model.fit(X, y)
print(model.predict([[2.5]]))Practice: Optional Python pseudocode—pair with Python and data science tracks for hands-on depth.
When not to train locally
Large language and vision models usually call hosted APIs or download pretrained weights—training from scratch is rarely a literacy exercise.
Important interview questions and answers
- Q: Why scikit-learn first?
A: Fast baselines and clear APIs before deep learning complexity. - Q: Notebook vs production?
A: Notebooks explore; tested modules and pipelines ship.
Self-check
- Name four stack layers.
- Why start with logistic regression on tabular data?
Tip: scikit-learn baselines on tabular data before reaching for deep learning.
Interview prep
- scikit-learn role?
- Fast interpretable baselines on tabular data.
- Notebook vs production?
- Notebooks explore; tested pipelines and modules ship.