Introduction to AI & ML
What is AI?
Artificial Intelligence (AI) refers to the simulation of human intelligence in machines programmed to think and act like humans.
AI encompasses various technologies, including machine learning, natural language processing, and robotics.
What is ML?
Machine Learning (ML) is a subset of AI that focuses on the development of algorithms that allow computers to learn from and make predictions based on data.
Types of ML
- Supervised Learning: Algorithms learn from labeled datasets.
- Unsupervised Learning: Algorithms find patterns in unlabeled data.
- Reinforcement Learning: Algorithms learn by receiving feedback from actions taken.
Applications of AI & ML
- Healthcare: Predicting diseases and personalizing treatment.
- Finance: Fraud detection and algorithmic trading.
- Retail: Customer recommendation systems and inventory management.
Best Practices in AI & ML
- Always validate your models with independent data.
- Ensure data quality and relevance.
- Regularly update models to adapt to new data.
FAQ
What is the difference between AI and ML?
AI is a broader concept that encompasses ML. ML is a specific approach to achieving AI.
Can ML operate without AI?
No, ML is a subset of AI, meaning it relies on AI frameworks to function.
graph TD;
A[Start] --> B{Is data available?}
B -- Yes --> C[Preprocess Data]
B -- No --> D[Collect Data]
C --> E[Choose Algorithm]
E --> F[Train Model]
F --> G[Evaluate Model]
G --> H{Is performance acceptable?}
H -- Yes --> I[Deploy Model]
H -- No --> E