Machine Learning Fundamentals: Concepts & Algorithms
Step into the exciting world of Machine Learning! This module provides a solid foundation in core ML concepts, common algorithms, and how to apply them to solve real-world problems. Ideal for those looking to understand the intelligence behind modern applications and to take their first steps as an ML practitioner.
1. Introduction to Machine Learning
What is Machine Learning and why is it so impactful? This section covers the definition of ML, distinguishes between its main types (supervised learning, unsupervised learning, and reinforcement learning), and explores various real-world applications where ML algorithms are making a difference.
Key Types of Machine Learning
- **Supervised Learning:** Learning from labeled data to make predictions (e.g., classification, regression).
- **Unsupervised Learning:** Finding patterns in unlabeled data (e.g., clustering, dimensionality reduction).
- **Reinforcement Learning:** Learning through trial and error with rewards and penalties.
2. Supervised Learning: Regression
Dive into regression, a supervised learning task used for predicting continuous values. This section focuses on Linear Regression, a foundational algorithm, explaining its principles, how to train a basic model, and evaluate its performance using metrics like Mean Squared Error (MSE) and R-squared.
Code Example: Simple Linear Regression with Scikit-learn
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
# Sample data: house sizes (X) and prices (y)
X = np.array([50, 60, 70, 80, 90]).reshape(-1, 1) # Features (2D array required)
y = np.array([200, 220, 250, 280, 300]) # Target (prices in thousands)
# Create and train the model
model = LinearRegression()
model.fit(X, y)
# Make predictions
y_pred = model.predict(X)
# Evaluate the model
print(f"Coefficients: {model.coef_}")
print(f"Intercept: {model.intercept_}")
print(f"Mean squared error: {mean_squared_error(y, y_pred):.2f}")
print(f"Coefficient of determination (R-squared): {r2_score(y, y_pred):.2f}")
# Predict price for a new house size (e.g., 75 sq meters)
new_house_size = np.array([[75]])
predicted_price = model.predict(new_house_size)
print(f"Predicted price for 75 sq meters: ${predicted_price[0]:.2f}K")
3. Supervised Learning: Classification
Explore classification, another supervised learning task used for predicting categorical labels. This section introduces algorithms like Logistic Regression and Decision Trees, and how to evaluate classification models using metrics such as accuracy, precision, and recall.
4. Unsupervised Learning: Clustering
Uncover patterns in unlabeled data with unsupervised learning. This section introduces K-Means clustering, a popular algorithm for grouping similar data points together, and discusses its applications in customer segmentation, anomaly detection, and data exploration.
Module Summary
You've successfully built a solid understanding of Machine Learning fundamentals, covering its main types and diving into core algorithms for regression and classification in supervised learning, as well as an introduction to unsupervised clustering. You're now ready to explore more advanced ML topics and build intelligent applications.
What You've Learned:
- Understood the definition and types of Machine Learning.
- Explored supervised learning algorithms for regression (Linear Regression).
- Introduced classification algorithms (Logistic Regression, Decision Trees).
- Understood basic unsupervised learning with K-Means clustering.
Next Steps & Related Modules
Continue your ML journey with specialized modules on deep learning, natural language processing, or dive into practical applications using advanced Python libraries.