Machine Learning Basics

Discover the fundamentals of building intelligent systems through data-driven algorithms and pattern recognition.

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What You'll Learn

Supervised Learning

Train models using labeled datasets where inputs and desired outputs are provided for pattern recognition.

Unsupervised Learning

Discover hidden patterns in data without explicit instruction using clustering and association techniques.

Reinforcement Learning

Train models through reward-based learning systems that learn by trial and error.

Hands-On Learning

Linear Regression Project

Predict housing prices by training models on historical datasets with housing market features.

1 hour

Image Classification

Build computer vision models using convolutional neural networks for image recognition tasks.

3 hours
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Essential Tools for ML Development

Scikit-Learn

Start with classical ML algorithms for classification, regression, and clustering with simple Python APIs.

Python

TensorFlow / PyTorch

Leverage modern deep learning frameworks for building complex neural network architectures and large-scale models.

Keras / PyTorch

Ready to Build Your First Model?

Start with our interactive linear regression project and expand your ML knowledge with hands-on experiments.

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