Machine_Learning_Basic_Tutorial____.pptx
- 1. Machine Learning Tutorial Introduction to Concepts and Techniques
- 2. Introduction to Machine Learning • - Machine Learning (ML) is a subset of Artificial Intelligence (AI). • - Enables systems to learn from data and make predictions or decisions. • - Types of ML: Supervised, Unsupervised, and Reinforcement Learning.
- 3. Supervised Learning • - Involves labeled data for training. • - Algorithms learn a mapping from inputs to outputs. • - Examples: Linear Regression, Decision Trees, Neural Networks.
- 4. Unsupervised Learning • - Involves unlabeled data for training. • - Identifies patterns or structures in data. • - Examples: Clustering (e.g., K-Means), Dimensionality Reduction (e.g., PCA).
- 5. Reinforcement Learning • - Agents learn by interacting with an environment. • - Uses rewards and penalties to optimize decision-making. • - Applications: Robotics, Game AI, Self-driving Cars.
- 6. Popular Machine Learning Algorithms • - Linear Regression • - Logistic Regression • - Decision Trees • - Support Vector Machines (SVM) • - Neural Networks
- 7. Tools for Machine Learning • - Programming Languages: Python, R • - Libraries: scikit-learn, TensorFlow, PyTorch • - Platforms: Google Colab, Jupyter Notebook