Machine Learning (ML) is a subset of Artificial Intelligence (AI) that allows computers to learn and make predictions or decisions without being explicitly programmed.
Why Machine Learning?
- Automates tasks that are difficult to program manually.
- Learns from data to improve accuracy over time.
- Widely used in fields like image recognition, natural language processing, and finance.
Types of Machine Learning
- Supervised Learning:
- The model is trained on labeled data (input-output pairs).
- Goal: Predict the output based on new input data.
- Spam email classification
- House price prediction
- Linear Regression
- Support Vector Machines (SVM)
- Decision Trees
- Unsupervised Learning:
- The model is trained on unlabeled data and must find patterns.
- Goal: Discover hidden structures in the data.
- Customer segmentation
- Anomaly detection
- K-means clustering
- Principal Component Analysis (PCA)
- Reinforcement Learning:
- The model learns by interacting with an environment and receiving rewards or penalties.
- Goal: Learn an optimal policy through trial and error.
- Game AI (like AlphaGo)
- Robot navigation
- Q-Learning
- Deep Q Networks (DQN)
Key Concepts
- Feature: A measurable property or characteristic of the data.
- Model: A mathematical representation used to make predictions.
- Training: Process where the model learns from the data.
- Testing: Evaluating the model’s performance on unseen data.
- Overfitting: Model performs well on training data but poorly on new data.
- Underfitting: Model is too simple and fails to capture patterns in the data.
- Hyperparameters: Settings that control the training process (e.g., learning rate).
Machine Learning Workflow
- Data Collection: Gather relevant data.
- Data Preprocessing: Clean and prepare the data (handle missing values, scaling).
- Feature Selection/Engineering: Select relevant features for the model.
- Model Selection: Choose a suitable algorithm.
- Training: Train the model on the data.
- Evaluation: Measure model performance using metrics (e.g., accuracy, precision).
- Optimization: Tune hyperparameters to improve performance.
- Deployment: Use the model in real-world applications.
Popular ML Tools and Libraries
- Programming Languages: Python, R
- Libraries:
- Scikit-learn (classic ML algorithms)
- TensorFlow (deep learning)
- PyTorch (deep learning)
- Keras (high-level neural networks)
Applications of Machine Learning
- Image and speech recognition
- Medical diagnosis and drug discovery
- Self-driving cars
- Fraud detection
- Personalized recommendations (Netflix, Amazon)
