Artificial Intelligence (AI) refers to the development of computer systems that perform tasks requiring human-like intelligence, including learning, problem-solving, and pattern recognition.

• Machine Learning (ML): Algorithms learning from data

Machine Learning Fundamentals

What is Machine Learning?

Machine Learning (ML) is a subset of AI that enables systems to automatically learn and improve from experience without explicit programming. ML algorithms build mathematical models based on sample data (training data) to make predictions or decisions.

Key Components

Training Data

Labeled datasets used to teach algorithms

Algorithm

Mathematical rules (e.g., Decision Trees, Neural Networks)

Model

Trained representation of patterns in data

ML Types

Type	Description	Example
Supervised	Learns from labeled data	Spam detection
Unsupervised	Finds patterns in unlabeled data	Customer segmentation
Reinforcement	Learns through trial/error	Game AI (AlphaGo)

Workflow Process

1. Data Collection & Cleaning
2. Feature Engineering
3. Model Selection
4. Training & Validation
5. Deployment & Monitoring



Popular Algorithms

Linear Regression

Predict continuous values

Random Forest

Ensemble decision trees

CNN

Image processing

• Deep Learning (DL): Neural networks for complex data

Deep Learning Fundamentals

What is Deep Learning?

Deep Learning (DL) is a specialized subset of machine learning that uses artificial neural networks with multiple processing layers to learn representations of data with increasing levels of abstraction.

Key Differentiator: While traditional ML requires manual feature engineering, DL automatically discovers the representations needed for detection or classification.

Neural Network Architecture

Input Layer

Receives raw data (pixels, words, etc.)

Hidden Layers

Multiple processing layers (2-100+)

Output Layer

Produces predictions/classifications



Types of Deep Learning

Type	Description	Applications
CNNs	Convolutional Neural Networks	Image recognition, medical imaging
RNNs	Recurrent Neural Networks	Speech recognition, time series
Transformers	Attention-based models	ChatGPT, language translation

How Deep Learning Works

1. Forward Propagation: Data flows through network layers
2. Loss Calculation: Compares output to true values
3. Backpropagation: Adjusts weights via gradient descent
4. Optimization: Minimizes error through iterations

Weight update equation: w = w – η(∂L/∂w)

Where η is learning rate and L is loss function

Why Deep Learning Excels

• Automatic feature extraction from raw data
• State-of-the-art performance on complex tasks
• Scalability with large datasets
• Continuous improvement with more data
• Neural Networks: Brain-inspired pattern recognition

1. Data Input (text/images/sensors)
2. Pattern Analysis
3. Output Generation

• Algorithmic bias in training data
• Privacy and surveillance concerns
• Job market transformations