What is Machine Learning (ML)? Core Models and Applications

Machine Learning (ML) is a subfield of artificial intelligence that enables computer systems to learn from data and make predictions without being explicitly programmed. Today, it forms the foundation of decision support systems in areas such as e-commerce, finance, healthcare, and industry. This article will thoroughly explore the fundamental concepts, key models, and application areas of machine learning.

Core Concepts

• Data: Numerical, textual, or visual information used for learning
• Label: The value the model is expected to predict
• Feature: Input values (age, income, category, etc.)
• Model: The structure that learns from data and makes predictions
• Training/Test: Adjusting the model with data and validating its accuracy
• Overfitting/Underfitting: Excessive model fitting or insufficient learning

Types of Machine Learning and Models

- Supervised Learning

• Regression: Linear Regression, Ridge, Lasso
• Classification: Decision Trees, KNN, SVM, Logistic Regression

- Unsupervised Learning

• Clustering: K-Means, DBSCAN
• Dimensionality Reduction: PCA, t-SNE

- Reinforcement Learning

• Agents: Structures that learn through interaction with their environment
• Reward System: Learning based on approaching the goal (Q-learning, DQN)

ML Libraries and Platforms

• Python Libraries: scikit-learn, TensorFlow, Keras, PyTorch
• Development Environments: Google Colab, Jupyter Notebook
• Automated ML Tools: H2O.ai, AutoML, Google Vertex AI

Real-World Applications

• E-commerce: Recommendation engines, price prediction
• Finance: Credit scoring, fraud detection
• Healthcare: Medical image analysis, early diagnosis systems
• Marketing: Campaign optimization, segmentation
• Industry: Product failure prediction, quality control systems

Things to Consider When Getting Started

• Data Preprocessing: Cleaning, feature selection
• Model Selection: Choosing the right algorithm for the problem type
• Performance Metrics: Accuracy, Precision, Recall, F1-score
• Ethics and Bias: Data imbalance and learned biases

Future Perspective

• Integration of ML + Artificial Intelligence + IoT
• Autonomous systems and Edge AI
• Learning systems supported by LLMs (GPT-based)
• The role of ML in decision support systems

• Gürkan Azlağ
• 5 January 2024, 18:21:09