Top 5 Machine Learning Courses in Delhi Compared for 2026

QUESTIONS TO ASK AT AN ML COURSE DEMO

1. What Python level do I need before joining?
2. How much statistics and linear algebra is taught?
3. Which libraries: NumPy, pandas, scikit-learn, others?
4. Do you teach train/test splits, cross-validation
   and metrics beyond accuracy?
5. How many end-to-end projects, and on what data?
6. Who reviews my notebooks and gives feedback?
7. Is deep learning covered, and when?
8. What happens if I miss classes — repeats, recordings?

RED FLAGS IN AN ML COURSE

[ ] 'Become an AI expert in a week' type promises
[ ] Guaranteed job or '100% placement' claims
[ ] No statistics or evaluation in the syllabus
[ ] Projects only on tiny, perfectly clean datasets
[ ] Only accuracy reported, even for imbalanced data
[ ] Copy-paste notebooks with no explanation
[ ] Fee and syllabus details avoided until payment

# Requires: pip install scikit-learn
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression

X, y = load_breast_cancer(return_X_y=True)

# Scaling inside the pipeline avoids data leakage in CV
model = make_pipeline(StandardScaler(), LogisticRegression(max_iter=1000))

scores = cross_val_score(model, X, y, cv=5, scoring="f1")
print("F1 per fold:", scores.round(3))
print("Mean F1    :", scores.mean().round(3))

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, roc_auc_score

X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, stratify=y, random_state=42)

clf = RandomForestClassifier(n_estimators=200, random_state=42)
clf.fit(X_train, y_train)

print(classification_report(y_test, clf.predict(X_test)))
print("ROC-AUC:", round(roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1]), 3))

# Always compare with a simple baseline
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.dummy import DummyClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.metrics import f1_score

X, y = load_breast_cancer(return_X_y=True)
X_tr, X_te, y_tr, y_te = train_test_split(X, y, stratify=y, random_state=0)

baseline = DummyClassifier(strategy="most_frequent").fit(X_tr, y_tr)
model = GradientBoostingClassifier(random_state=0).fit(X_tr, y_tr)

print("Baseline F1:", round(f1_score(y_te, baseline.predict(X_te)), 3))
print("Model F1   :", round(f1_score(y_te, model.predict(X_te)), 3))

# train.py — save a model
import joblib
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
X, y = load_iris(return_X_y=True)
joblib.dump(RandomForestClassifier(random_state=0).fit(X, y), "model.joblib")

# api.py — serve it (pip install fastapi uvicorn joblib scikit-learn)
import joblib
from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()
model = joblib.load("model.joblib")

class Flower(BaseModel):
    features: list[float]  # 4 numbers

@app.post("/predict")
def predict(f: Flower):
    return {"class": int(model.predict([f.features])[0])}

# Run: uvicorn api:app --reload

MACHINE LEARNING COURSE SCORECARD (1-5)

Criterion                          A     B     C
---------------------------------  ----  ----  ----
Python + pandas foundations         __    __    __
Statistics and maths taught         __    __    __
Core algorithms in scikit-learn     __    __    __
Evaluation, CV and leakage          __    __    __
Projects on realistic data          __    __    __
Feedback on my notebooks            __    __    __
Schedule, mode and location fit     __    __    __
Clear fees, terms and inclusions    __    __    __
TOTAL (/40)                         __    __    __

AM I READY FOR AN ML COURSE?

[ ] I can write Python functions, loops and list comprehensions
[ ] I have used pandas to filter and group a CSV
[ ] I know mean, median, standard deviation
[ ] I understand what a percentage and a probability are
[ ] I can plot a simple chart

0-2 ticks : start with a Python + statistics course first
3-4 ticks : an ML course with a short refresher fits
5 ticks   : go straight into core ML

DECISION NOTE

Goal           : ML engineer / data scientist / upskill
Current level  : beginner / some Python / developer
Hours per week : __
Preferred mode : classroom (Noida / Laxmi Nagar) / online
Chosen course  : ____________  Format: ____________
Why            : ______________________________
First project  : ______________________________