Lesson 30: Introduction to Machine Learning Concepts

What Is Machine Learning?

Machine learning (ML) is a field of computer science where algorithms learn patterns from data instead of being explicitly programmed. ML powers recommendation systems, image recognition, fraud detection, language models, and more.

Types of Machine Learning

• Supervised Learning — learn from labeled data (e.g., predicting house prices).
• Unsupervised Learning — find patterns in unlabeled data (e.g., clustering customers).
• Reinforcement Learning — learn through rewards and penalties (e.g., game-playing AI).

Supervised Learning

Supervised learning uses input–output pairs to train a model.

Common Algorithms

• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forests
• Support Vector Machines
• Neural Networks

Example: Linear Regression

from sklearn.linear_model import LinearRegression
import numpy as np

X = np.array([[1], [2], [3], [4]])
y = np.array([2, 4, 6, 8])

model = LinearRegression()
model.fit(X, y)

print(model.predict([[5]]))

Unsupervised Learning

Unsupervised learning finds structure in data without labels.

Common Algorithms

• K‑Means Clustering
• Hierarchical Clustering
• Principal Component Analysis (PCA)

Example: K‑Means

from sklearn.cluster import KMeans
import numpy as np

data = np.array([[1,2],[1,4],[1,0],[10,2],[10,4],[10,0]])

kmeans = KMeans(n_clusters=2)
kmeans.fit(data)

print(kmeans.labels_)

Reinforcement Learning

Reinforcement learning trains an agent to make decisions by rewarding good actions and penalizing bad ones. It is used in robotics, games, and autonomous systems.

Training and Testing

To evaluate a model, data is split into:

• Training set — used to teach the model.
• Test set — used to evaluate performance.

Overfitting and Underfitting

Two common problems in ML:

• Overfitting — model memorizes training data but fails on new data.
• Underfitting — model is too simple to learn the patterns.

Evaluation Metrics

• Accuracy
• Precision / Recall
• F1 Score
• Mean Squared Error (MSE)
• Confusion Matrix

Why Machine Learning Matters

• It automates decision‑making.
• It finds patterns humans might miss.
• It powers modern AI applications.
• It scales to massive datasets.

Next Steps

Now that you understand the basics of machine learning, you're ready to explore neural networks in Lesson 31: Introduction to Neural Networks.

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