Build intelligent systems using Python, TensorFlow, and scikit-learn. Solve real-world problems with supervised and unsupervised learning.
Build a regression model to predict house prices based on features like location, size, and amenities using the Boston Housing or Bangalore housing dataset.
Predict which telecom customers are likely to churn using classification algorithms on the Telco Customer Churn dataset.
Build a spam detection system using NLP techniques — TF-IDF and Naive Bayes — on the SMS Spam Collection dataset.
Build a collaborative and content-based filtering recommendation system using the MovieLens dataset.
Train a neural network to recognize handwritten digits from the MNIST dataset with 98%+ accuracy.
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