From NumPy and pandas to trained, evaluated models on real Indian datasets.
Learn how machine learning works under the hood and how to apply it. You clean data, train and compare models, and deploy one behind an API.
# Classify resumes by job role
from sklearn.pipeline import make_pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
model = make_pipeline(
TfidfVectorizer(ngram_range=(1, 2)),
LogisticRegression(max_iter=1000),
)
scores = cross_val_score(model, X, y, cv=5)
print(f"Accuracy: {scores.mean():.2%}")
model.fit(X, y)
Tools and technologies covered:
Each module ends with a graded assignment and a code review from your mentor. Classes run on weekday evenings and weekends, and every session is recorded.
Every project lives on your GitHub and is reviewed like a real pull request.
Sort resumes by job role with TF-IDF and logistic regression.
Regression on district-level agriculture data, with feature engineering.
A PyTorch model for plant leaf disease, served with FastAPI.
We provide placement support. We do not guarantee a job or a salary.
Live classes, recordings for one year, mentor code reviews, projects, and interview preparation. Pay in full online, or ask about monthly instalments at no added interest.
Placeholder fee. Change it in the admin area.Exclusive of 18% GST. EMI amounts are indicative; online checkout charges the full fee.
Sit in on a live 60-minute session of Machine Learning with Python, meet the mentor and ask anything about the course.
A free, self-paced start in Python for anyone heading into the AI & ML track.
View course detailsPrompting, embeddings, retrieval augmented generation and shipping an AI feature inside a web app.
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