⭐ Python + scikit-learn

📊 AI Intermediate

Machine Learning with Python

Build real ML models, analyse Kaggle datasets, and publish your first AI project.

📅 20 Sessions · 40 Hours👤 Ages 12–16💻 Python🔧 Computer Lab (Google Colab)👥 Max 12 students📊 Intermediate Level
₹10,000
Full program fee
✅ 20 Sessions · 40 Hours
✅ Ages 12–16
✅ Max 12 students
✅ Certificate of Completion
✅ Project portfolio
✅ Demo Day for parents
✅ Starts April 28, 2026
Register Now
📞 83743 77311 · Limited seats

About This Course

Hands-on Machine Learning with Python. Students learn data analysis, model training, NLP, and computer vision using real datasets. By the end, every student has a Kaggle profile, a trained ML model, and a capstone project published on GitHub.

What You'll Get

✓Python + pandas + scikit-learn
✓Kaggle profile and first competition
✓Train classifiers on real-world data
✓NLP — sentiment analysis
✓OpenCV computer vision basics
✓Model evaluation — accuracy, F1, ROC
✓Capstone ML project
✓GitHub project portfolio

📅 Course Curriculum

20 sessions · 20 Sessions · 40 Hours

D1
Python Refresher for ML
Lists, dicts, loops, functions recap. Jupyter Notebooks. NumPy intro.
D2
NumPy Array Operations
ndarray. Indexing, slicing, broadcasting. Matrix math. Exercises.
D3
pandas Data Analysis
DataFrame, Series. read_csv(). Filter, groupby.
🔨 Project: Titanic exploration
D4
matplotlib & seaborn Visualisation
Line, bar, scatter, heatmap. subplots.
🔨 Project: Data story dashboard
D5
Intro to Machine Learning
Supervised learning. train_test_split. scikit-learn pipeline. Iris classifier.
D6
Linear & Logistic Regression
Cost function. Gradient descent intuition. Logistic regression. House price predictor.
D7
Decision Trees & Random Forest
Splitting criteria. Overfitting. Ensemble methods.
🔨 Project: Spam classifier
D8
K-Nearest Neighbors
Distance metrics. Choosing K.
🔨 Project: Movie recommender
D9
Model Evaluation
Accuracy, precision, recall, F1. Confusion matrix. ROC curve and AUC.
D10
Feature Engineering
One-hot encoding. StandardScaler. Feature importance. Improve accuracy by 10%.
D11
Natural Language Processing
Tokenization, stopwords, stemming. Bag of Words, TF-IDF.
D12
Sentiment Analysis
VADER and ML sentiment. Twitter sentiment classifier. Precision vs recall.
D13
Computer Vision with OpenCV
Image channels. Edge detection (Canny). Contour finding.
D14
Image Classification
KNN on MNIST. CNN intro.
🔨 Project: Handwritten digit recognizer
D15
Kaggle Competitions
Platform tour. Download datasets. First prediction submission.
D16
Unsupervised Learning — K-Means
K-Means algorithm. Elbow method.
🔨 Project: Customer segmentation
D17
Capstone Project Planning
Select real ML problem. Define dataset and target. Plan pipeline. Mentor approval.
D18
Capstone Build
Data cleaning, model training, hyperparameter tuning. Full ML pipeline.
D19
Testing & Refinement
Cross-validation. Ensemble methods. Results report and slides.
D20
Demo Day & Certificate
Present ML project. Kaggle profile + GitHub repo. Certificate of AI Intermediate.

🏗️ Projects You'll Build

9 hands-on projects — from Day 1

🌸
Iris Classifier
First ML model on the classic dataset.
🏠
House Price Predictor
Linear regression on real estate data.
📧
Spam Classifier
Decision tree to filter spam emails.
🎬
Movie Recommender
KNN-based movie recommendation.
🚢
Titanic Survival
Predict Titanic survivors on Kaggle.
😊
Sentiment Analyser
Twitter review sentiment with NLP.
✍️
MNIST Digit Recognizer
Classify handwritten digits.
👥
Customer Segmentation
K-Means clustering on customer data.
🏆
Capstone ML Project
Student-defined ML project on Kaggle.

Frequently Asked Questions

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Ready to join AI Intermediate?

Limited to max 12 students. Starts April 28, 2026.

Enroll for ₹10,000 →📞 83743 77311

All programs · 20 sessions · 2 hrs/day · Certificate included · Above Agarwal Bhavan, Chambenhalli Sarjapura Road, Bangalore