Introduction to Machine Learning
• Machine Learning (ML) is a subset of Artificial Intelligence (AI).
• It enables systems to learn from data and improve performance without explicit programming.
Types of Machine Learning
• Supervised Learning: Learning from labeled data (e.g., classification, regression).
• Unsupervised Learning: Discovering hidden patterns in unlabeled data (e.g., clustering).
• Reinforcement Learning: Learning through trial and error using rewards and penalties.
Key Algorithms in Machine Learning
• Linear Regression and Logistic Regression.
• Decision Trees and Random Forests.
• Support Vector Machines (SVM).
• Neural Networks and Deep Learning.
Applications of Machine Learning
• Healthcare: Disease prediction and drug discovery.
• Finance: Credit scoring and fraud detection.
• Retail: Personalized recommendations.
• Transportation: Self-driving cars and route optimization.
Challenges in Machine Learning
• Need for large, high-quality datasets.
• Bias and fairness issues in models.
• High computational cost for training complex models.
• Difficulty in model interpretability.
Future of Machine Learning
• Integration with Internet of Things (IoT).
• Advances in Natural Language Processing (NLP).
• Real-time decision-making with edge computing.
• Stronger focus on explainable AI (XAI).
