🧠
AI and ML Fundamentals
Fundamental concepts of Artificial Intelligence and Machine Learning
⏱️ Estimated reading time: 20 minutes
Introduction to AI
Artificial Intelligence (AI) is the capability of machines to perform tasks that normally require human intelligence.
1. Weak AI (Narrow AI): Designed for specific tasks
2. Strong AI (General AI): Capability to perform any intellectual task a human can
3. Superintelligence: Surpasses human intelligence in all aspects
Types of AI
1. Weak AI (Narrow AI): Designed for specific tasks
2. Strong AI (General AI): Capability to perform any intellectual task a human can
3. Superintelligence: Surpasses human intelligence in all aspects
🎯 Key Points
- ✓ Distinguish AI, ML and Deep Learning to choose the right approach
- ✓ Understanding learning types (supervised, unsupervised, reinforcement) is key
- ✓ Data quantity and quality strongly influence ML outcomes
- ✓ Deep Learning needs more data and compute but solves complex tasks
- ✓ Validation and testing are essential to avoid overfitting
Machine Learning
ML is a subset of AI that enables machines to learn from data without being explicitly programmed.
- Uses labeled data
- Predicts outcomes based on patterns
- Examples: classification, regression
- Uses unlabeled data
- Finds hidden patterns
- Examples: clustering, dimensionality reduction
- Learns through trial and error
- Uses rewards and penalties
- Examples: gaming, robotics
Types of Learning
Supervised Learning
- Uses labeled data
- Predicts outcomes based on patterns
- Examples: classification, regression
Unsupervised Learning
- Uses unlabeled data
- Finds hidden patterns
- Examples: clustering, dimensionality reduction
Reinforcement Learning
- Learns through trial and error
- Uses rewards and penalties
- Examples: gaming, robotics
🎯 Key Points
- ✓ Choose learning type based on labeled data availability and objective
- ✓ Preprocessing and feature engineering often improve models more than complex algorithms
- ✓ For low-data problems consider transfer learning or augmentation
- ✓ Reinforcement learning is useful when clear reward signals and interactive environments exist
- ✓ Measure with business-relevant metrics, not just accuracy
Deep Learning
Deep Learning is a subset of ML that uses artificial neural networks with multiple layers.
- Processes large volumes of data
- Learns hierarchical representations
- Useful for images, audio, and text
- Computer vision
- Natural language processing
- Speech recognition
Characteristics
- Processes large volumes of data
- Learns hierarchical representations
- Useful for images, audio, and text
Applications
- Computer vision
- Natural language processing
- Speech recognition
🎯 Key Points
- ✓ Deep networks learn hierarchical representations useful for images, audio and text
- ✓ Regularization, dropout and normalization help train robust deep nets
- ✓ Transfer learning speeds deployment when data is scarce
- ✓ Monitor compute and training times to control costs
- ✓ Consider interpretability when models affect sensitive decisions