Unit 10.2
AI Concepts: Data Science, Machine Learning, and Neural Networks
IT 231: IT and Application
Learning Objectives
By the end of this chapter, you will be able to:
- ✅ Define Data Science and Machine Learning.
- ✅ Understand the relationship between AI, ML, and Data Science.
- ✅ Grasp the basic concept of an Artificial Neural Network.
The Big Picture: AI & Its Subfields
Artificial Intelligence is a broad field. Let's see how our key concepts fit together.
Artificial Intelligence (AI)
The overall concept of creating intelligent machines.
Machine Learning (ML)
A subset of AI where machines learn from data.
Neural Networks / Deep Learning
A powerful type of ML inspired by the human brain.
What is Data Science? 📊
It's an interdisciplinary field focused on extracting knowledge from data.
Data Science is the practice of using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
Think of it as a combination of:
- Computer Science
- Statistics & Mathematics
- Domain Expertise (e.g., knowledge of finance, healthcare, etc.)
Machine Learning: AI's Engine ⚙️
Instead of being explicitly programmed with rules, ML systems learn patterns directly from data.
Machine Learning (ML) is a subset of AI that focuses on building systems that can learn from data, identify patterns, and make decisions with minimal human intervention.
Key Idea: The system is "trained," not "programmed."
Data Science vs. Machine Learning
They are related, but not the same. ML is a tool often used in Data Science.
Data Science 🔍
- A broad field
- Focuses on the entire data processing methodology
- Goal is to extract insights and knowledge
- Includes data cleaning, analysis, and visualization
Machine Learning 🧠
- A subset of AI
- Focuses on building predictive models
- Goal is to make accurate predictions or decisions
- A key component used within data science
Neural Networks: Brain-Inspired AI
This powerful type of Machine Learning is behind many recent AI breakthroughs like ChatGPT and image recognition.
An Artificial Neural Network (ANN) is a machine learning model inspired by the structure and function of the human brain, consisting of interconnected nodes ("neurons") organized in layers.
Neural Networks are the foundation of Deep Learning.
How a Neural Network "Thinks" (Simplified)
Data flows through layers, with each layer recognizing more complex patterns.
Input Layer
Receives raw data (e.g., pixels of an image, words in a sentence).
Hidden Layer(s)
Performs computations and finds patterns. The "deep" in deep learning refers to many hidden layers.
Output Layer
Produces the final result (e.g., "This is a cat," a predicted stock price).
Why are Neural Networks so powerful? ⚡
- Pattern Recognition: They excel at finding complex, non-linear patterns in massive datasets.
- Unstructured Data: They can process data like images, audio, and text that traditional algorithms struggle with.
- Adaptability: They can learn and improve their performance over time as they are exposed to more data.
Practical Applications in Nepal
AI isn't just a global concept; it's being applied here at home.
Examples in a Nepali Context
- Agriculture: Using drones and ML to analyze crop health and detect diseases in the Terai region.
- Fintech: Companies like eSewa and Khalti use ML algorithms to detect fraudulent transactions and improve security.
- Language: Developing Nepali Natural Language Processing (NLP) models for better translation, chatbots, and sentiment analysis.
- Healthcare: Research into using neural networks to help diagnose diseases from medical images in rural health posts.
Summary & Key Takeaways 🎯
Let's recap the core concepts from this chapter.
- Data Science is the broad field of extracting knowledge and insights from data, combining stats, computer science, and domain expertise.
- Machine Learning is a subset of AI where systems are trained on data to make predictions, rather than being explicitly programmed.
- Neural Networks are a powerful type of ML model, inspired by the human brain, that excel at finding complex patterns and form the basis of deep learning.
Thank You!
Any Questions?