Machine Learning (ML)

 

Machine Learning (ML) is a subset of Artificial Intelligence (AI) that enables computers to learn from data and make predictions or decisions without being explicitly programmed. It is widely used in various fields, including healthcare, finance, robotics, and more.

Types of Machine Learning

1. Supervised Learning

   • The model learns from labeled data (input-output pairs).
   • Common algorithms:
     • Linear Regression
     • Logistic Regression
     • Decision Trees
     • Random Forest
     • Support Vector Machines (SVM)
     • Neural Networks
   • Applications: Spam detection, fraud detection, medical diagnosis

2. Unsupervised Learning

• The model finds patterns in unlabeled data.
• Common algorithms:
  • K-Means Clustering
  • Hierarchical Clustering
  • Principal Component Analysis (PCA)
  • Autoencoders
• Applications: Customer segmentation, anomaly detection, recommendation systems

1. Semi-Supervised Learning

   • Uses a small amount of labeled data along with a large amount of unlabeled data.
   • Applications: Speech recognition, text classification

2. Reinforcement Learning (RL)

• The model learns by interacting with an environment and receiving rewards or penalties.
• Common algorithms:
  • Q-Learning
  • Deep Q-Networks (DQN)
  • Proximal Policy Optimization (PPO)
  • A3C (Asynchronous Advantage Actor-Critic)
• Applications: Robotics, self-driving cars, game playing (e.g., AlphaGo)

Key Components of ML

• Dataset: Collection of data used for training/testing models.
• Features: Input variables used for predictions.
• Model: Mathematical representation of patterns in data.
• Training: Process of learning from data.
• Loss Function: Measures model performance.
• Optimization: Improves model accuracy (e.g., Gradient Descent).
• Evaluation Metrics: Accuracy, Precision, Recall, F1-score, RMSE

Popular ML Frameworks & Tools

• TensorFlow
• PyTorch
• Scikit-Learn
• Keras
• XGBoost
• LightGBM
• Hugging Face Transformers

Challenges in ML

• Data quality and availability
• Model overfitting & underfitting
• Bias and fairness
• Interpretability & explainability
• Scalability and computational costs