Introduction to Data Analytics and AI

Introduction to Data Analytics and AI. © Copyright New Zealand Skills and Education College 2024.

Artificial Intelligence (AI). 6/10/2024. A blue circle with white text Description automatically generated.

Machine Learning (ML). ML is a subset of Artificial Intelligence (AI) It is a method of data analysis ML uses algorithms and statistical models that allow machines to identify patterns and make decisions based on data. In essence, machine learning is about teaching computers to learn from data and perform tasks such as prediction, classification, or clustering, without human intervention..

Supervised Learning. 6/10/2024. © Copyright New Zealand Skills and Education College 2024.

Model-Free RL: Learns directly from interactions without an explicit model of the environment. Examples: Q-Learning, SARSA. Model-Based RL: Uses a model of the environment to predict future states and plan actions. Examples: Monte Carlo Methods, Dynamic Programming. Policy-Based Methods: Focuses on learning the policy directly. Examples: REINFORCE, Actor-Critic Methods. Value-Based Methods: Estimates the value of actions to derive the optimal policy. Examples: Q-Learning, Deep Q-Network (DQN). Deep Reinforcement Learning: Combines RL with Deep Learning to solve complex, high-dimensional problems. Examples: Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG)..

Recommendation Algorithms. machine learning methods designed to suggest relevant items to users based on their preferences, behaviors, and interactions. e-commerce online streaming services social networks and content platforms. Goal: to provide personalized experiences by predicting what a user might like or need. Why Use Recommendation Systems? Personalization: Tailoring the content for each user to enhance engagement. Increased Sales/Engagement: Recommending items users are likely to buy or interact with. Content Discovery: Helping users find new content or products they might not have discovered otherwise. Improved User Experience: Streamlining decision-making for users by reducing information overload..

Deep Learning. Transformed AI by enabling machines to solve highly complex problems and achieve performance levels comparable to humans in specific tasks. Its growing popularity continues to push the boundaries of what AI systems can accomplish..

Model=Training(Data+ Algorithm). Validation: tune model parameters and evaluate its performance during training. Test: assess the final performance of the trained model..

Generative AI. 6/10/2024. © Copyright New Zealand Skills and Education College 2024.