Introduction to Machine Learning: A Developer Guide

Types of ML

• Supervised Learning: Labeled data training
• Unsupervised Learning: Pattern discovery
• Reinforcement Learning: Reward-based learning

Getting Started with Python

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# Load and split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Train model
model = LogisticRegression()
model.fit(X_train, y_train)

# Evaluate
predictions = model.predict(X_test)
accuracy = accuracy_score(y_test, predictions)

Key Concepts

1. 1Features: Input variables
2. 2Labels: Output variables
3. 3Training: Learning from data
4. 4Inference: Making predictions

Tools

• scikit-learn: Classic ML algorithms
• TensorFlow: Deep learning
• PyTorch: Research-focused DL

Start with simple problems and iterate!