Machine learning is transforming every industry. Here's what you need to know to get started.

What is Machine Learning?🔗

Machine learning is a subset of artificial intelligence where computers learn patterns from data without being explicitly programmed.

Types of Machine Learning🔗

1. Supervised Learning🔗

Learn from labeled examples:

  • Classification: Cat or dog?
  • Regression: What price?
from sklearn.linear_model import LogisticRegression

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

2. Unsupervised Learning🔗

Find patterns in unlabeled data:

  • Clustering: Group similar items
  • Dimensionality Reduction: Compress features

3. Reinforcement Learning🔗

Learn by trial and error with rewards:

  • Game playing (AlphaGo)
  • Robotics
  • Autonomous vehicles

The ML Pipeline🔗

  1. Data Collection: Gather relevant data
  2. Preprocessing: Clean and transform
  3. Feature Engineering: Extract meaningful features
  4. Model Selection: Choose the right algorithm
  5. Training: Fit the model
  6. Evaluation: Measure performance
  7. Deployment: Put into production

Key Metrics🔗

from sklearn.metrics import accuracy_score, precision_score, recall_score

accuracy = accuracy_score(y_true, y_pred)
precision = precision_score(y_true, y_pred)
recall = recall_score(y_true, y_pred)

Getting Started🔗

The best way to learn is by doing. Start with:

  1. Kaggle competitions
  2. Course: Andrew Ng's ML Specialization
  3. Practice: Build something real

Machine learning is a journey. Take the first step today!