Introduction to Machine Learning
Machine Learning (ML) is a subset of artificial intelligence (AI) that enables systems to learn and
make decisions without being explicitly programmed.
ML uses algorithms and statistical models to analyze and interpret data, allowing computers to
perform specific tasks such as prediction, classification, and clustering.
Key Topics Covered in Machine Learning:
- Supervised Learning: Learning with labeled data (e.g., Regression, Classification).
- Unsupervised Learning: Identifying patterns in unlabeled data (e.g., Clustering, Dimensionality
Reduction).
- Reinforcement Learning: Learning to make decisions by interacting with an environment.
Applications of Machine Learning:
- Healthcare: Predicting diseases, drug discovery.
- Finance: Fraud detection, stock market prediction.
- Retail: Recommendation systems, customer segmentation.
- Autonomous Vehicles: Object detection, navigation.
Supervised Learning
Supervised learning involves training a model using labeled data. The model learns to map input
data (features) to the correct output (target).
Types of Supervised Learning:
1. Regression: Predicting continuous outcomes (e.g., house prices, stock prices).
2. Classification: Assigning data into discrete categories (e.g., spam detection, image classification).
Example Algorithms:
- Linear Regression
- Logistic Regression
- Support Vector Machines (SVM)
- Decision Trees and Random Forests
- Neural Networks
Workflow:
1. Data Collection and Preprocessing
2. Model Selection
3. Training and Validation
4. Evaluation
5. Deployment
