• Focus: Data analysis involves examining, cleaning, transforming, and modeling data to discover useful information, inform conclusions, and support decision-making. It's the process of digging into data to extract meaningful insights, such as trends, patterns, and relationships.
• Methodologies: It includes statistical analysis, visualization, data mining, and the initial steps of data preprocessing.
• Application: Business strategies, research conclusions, etc. It's a more manual, hands-o approach to understanding data and typically requires human interpretation.
• Focus: Machine learning is a subset of Al that focuses on building systems that learn from data. Instead of being explicitly programmed to perform a task, these systems are trained using large amounts of data and algorithms that enable them to learn how to perform the task.
• Methodologies: It includes supervised learning, unsupervised learning, reinforcement learning, and deep learning. Machine learning models are trained on datasets to identify patterns and make predictions or decisions based on new data.
• Application: Machine learning is used for predictive analytics, speech recognition, imag recognition, and many other tasks that require the interpretation of complex data sets It automates the extraction of insights from data.
• Focus: AI is a broader concept that refers to machines or systems capable of performing tasks that typically require human intelligence. This includes reasoning, learning, perception, and natural language understanding.
• Methodologies: AI encompasses machine learning, deep learning, natural language processing (NLP), robotics, and more. It involves creating algorithms that enable machines to mimic human cognitive functions.
• Application: AI applications range from virtual assistants and chatbots to autonomous vehicles and sophisticated decision-making systems. Al aims to create systems that can operate intelligently and independently.
• Data Analysis vs Machine Learning: Data analysis is primarily concerned with extracting insights from data, often requiring human interpretation and decision-making. Machine learning, on the other hand, automates the extraction of insights by learning from data patterns and making predictions or decisions without human intervention.
• Machine Learning as a subset of AI: Machine learning is a core part of AI, focusing specifically on algorithms that allow computers to learn from and make decisions based on data. AI includes machine learning but also encompasses other technologies that enable machines to perform tasks that would normally require human intelligence.
• AI as the broader goal: AI represents the broader goal of creating intelligent machines. Machine learning is a means towards achieving AI by providing systems the ability to automatically learn and improve from experience. Data analysis techniques can be used within machine learning to understand and preprocess the data fed into these systems
PPT Link: https://github.com/MikkoDT/MexEE402_AI/blob/main/Machine_Learnng/Introduction%20to%20ML.pptx
