Components of Machine Learning for Effective AI Implementation
Explore the key components of machine learning - data, features, model, algorithm, and learning algorithm - essential for creating AI applications like recommendation systems and self-driving cars. Dive into the foundational concepts and importance of each component to enhance your understanding of Machine Learning.
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Dr. SNS RAJALAKSHMI COLLEGE OF ARTS & SCIENCE (Autonomous) Coimbatore -641049 DEPARTMENT OF COMPUTER APPLICATIONS(PG) COURSE NAME : 22UDA804 - Basics of Machine Learning II CS DA /II SEMESTER Unit 1 Topic 1 : Components of learning 3/15/2024 Software Process Improvement
Components of learning Definition: "Machine Learning is a subset of AI that enables systems to learn and improve from experience without being explicitly programmed." Importance: Used in AI applications like recommendation systems, self-driving cars, and fraud detection. AWS FUNDAMENTALS
Components of learning Key Components of Learning in ML Machine learning involves several components: Data Features Model Algorithm Loss Function Optimization Evaluation & Feedback AWS FUNDAMENTALS
Components of learning Component 1 - Data Data is the foundation of machine learning. Types of Data: Structured Data (e.g., databases, tables) Unstructured Data (e.g., images, text, audio) Importance of Data Preprocessing (Cleaning, Normalization, Handling Missing Values) AWS FUNDAMENTALS
Components of learning Component 2 - Features Features are variables/input attributes that help the model learn. Feature Engineering: Selection: Choosing relevant features Extraction: Transforming raw data into useful features Scaling: Standardizing values for better learning AWS FUNDAMENTALS
Components of learning Component 3 - Model The model is the mathematical representation of the learning process. Types of ML Models: Supervised Learning Models (Regression, Classification) Unsupervised Learning Models (Clustering, Dimensionality Reduction) Reinforcement Learning Models (Agent-based learning) AWS FUNDAMENTALS
Components of learning Component 4 - Learning Algorithm The algorithm defines how the model learns from data. Common ML Algorithms: Linear Regression, Decision Trees, Neural Networks, SVM, K-Means, etc. Choosing the right algorithm depends on the problem type and data. AWS FUNDAMENTALS
Components of learning Component 5 - Loss Function Loss function measures the difference between predicted and actual values. Examples: Mean Squared Error (MSE) for Regression Cross-Entropy Loss for Classification AWS FUNDAMENTALS
Components of learning Component 6 - Optimization Optimization fine-tunes the model to minimize error. Common Optimization Techniques: Gradient Descent (SGD, Adam, RMSprop) Hyperparameter Tuning (Grid Search, Random Search) Component 7 - Evaluation & Feedback Evaluating model performance using: Accuracy, Precision, Recall, F1 Score Confusion Matrix, ROC Curve Feedback loop for continuous model improvement AWS FUNDAMENTALS
Components of learning Machine learning involves: Data collection & preprocessing Feature selection & engineering Model building using algorithms Optimization and evaluation The goal is to create accurate, efficient, and scalable ML models. AWS FUNDAMENTALS
Components of learning Machine learning involves: Data collection & preprocessing Feature selection & engineering Model building using algorithms Optimization and evaluation The goal is to create accurate, efficient, and scalable ML models. AWS FUNDAMENTALS
