Welcome to our AI and Machine Learning tutorial! This guide will take you through the basics of AI and ML, covering key concepts, techniques, and tools.
Key Concepts
- Artificial Intelligence (AI): AI refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions.
- Machine Learning (ML): ML is a subset of AI that focuses on building systems that learn from data, identify patterns, and make decisions with minimal human intervention.
Techniques
- Supervised Learning: This technique involves training a model on labeled data, where the input and output are provided.
- Unsupervised Learning: Unsupervised learning involves training a model on unlabeled data, where the model tries to find patterns and relationships in the data.
- Reinforcement Learning: This technique involves training a model to make decisions based on feedback from the environment.
Tools
- Python: Python is one of the most popular programming languages for AI and ML, thanks to its simplicity and the availability of numerous libraries.
- TensorFlow: TensorFlow is an open-source library for dataflow programming across a range of tasks, but it is especially good at training neural networks.
- PyTorch: PyTorch is another open-source machine learning library that provides a deep learning platform.
Learn More
For a more in-depth understanding of AI and ML, check out our comprehensive guide on AI & ML Fundamentals.
Images
- Neural Network
- Python
- TensorFlow
- PyTorch