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Introduction to Machine Learning

A gentle introduction to machine learning concepts, covering supervised learning, neural networks, and practical applications.

K
Kartik Gupta
Author
April 12, 2026
Published
1 min read
Read time

Types of Learning

Supervised Learning

The algorithm learns from labeled examples.

python
from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier()
model.fit(X_train, y_train)
predictions = model.predict(X_test)

Unsupervised Learning

The algorithm finds patterns in unlabeled data.

python
from sklearn.cluster import KMeans

kmeans = KMeans(n_clusters=3)
kmeans.fit(X)
labels = kmeans.labels_

Neural Networks

At their core, neural networks are function approximators:

Input → Hidden Layers → Output

Each "neuron" applies a weighted sum followed by an activation function.

Practical Tools

  • scikit-learn: Great for traditional ML
  • PyTorch: Flexible deep learning
  • Hugging Face: Pre-trained models and fine-tuning

Getting Started

  1. Pick a problem you care about
  2. Find a dataset
  3. Start simple (linear regression, then gradient descent)
  4. Iterate

The best ML projects come from understanding the domain, not just the algorithms.

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