Machine Learning Overview and Types

Machine Learning (ML) is a subset of artificial intelligence (AI) that enables systems to learn from data, identify patterns, and make decisions with minimal human intervention.

Supervised Learning
- Involves training a model on a labeled dataset (input-output pairs).
- Common algorithms: Linear regression, Decision trees, Support Vector Machines, Neural networks.
- Applications: Classification (spam detection), Regression (predicting prices).
Unsupervised Learning
- Involves training a model on an unlabeled dataset (no explicit output).
- Common algorithms: K-means clustering, Hierarchical clustering, Principal Component Analysis (PCA).
- Applications: Customer segmentation, Anomaly detection.
Semi-Supervised Learning
- Combines a small amount of labeled data with a large amount of unlabeled data.
- Useful when labeling data is expensive or time-consuming.
Reinforcement Learning
- Involves training an agent to make decisions by receiving rewards or penalties based on its actions.
- Commonly used in robotics, gaming, and autonomous systems.

Features: Individual measurable properties or characteristics used as input for models.
Training: The process of feeding data to a machine learning model to enable it to learn.
Model: A mathematical representation of a real-world process based on the training data.
Overfitting: When a model learns the training data too well, including noise and outliers, leading to poor performance on new data.
Underfitting: When a model is too simple to capture the underlying trend of the data.

Accuracy: The proportion of correct predictions out of total predictions.
Precision: The ratio of true positive predictions to the total predicted positives.
Recall (Sensitivity): The ratio of true positive predictions to the total actual positives.
F1 Score: The harmonic mean of precision and recall, useful for imbalanced datasets.
Confusion Matrix: A table that summarizes the performance of a classification algorithm.

Programming Languages: Python, R, Java.
Libraries:
- TensorFlow: Open-source library for deep learning.
- Scikit-learn: Library for classical machine learning algorithms.
- Keras: High-level neural networks API.
Platforms: Google Cloud ML, AWS SageMaker, Microsoft Azure ML.

Data quality and quantity: Requires large amounts of clean, labeled data.
Model interpretability: Understanding how models make decisions.
Bias and fairness: Ensuring models do not propagate or amplify biases present in training data.

Machine Learning (ML) is a subset of artificial intelligence (AI) focused on enabling systems to learn from data autonomously.
It identifies patterns and makes decisions while minimizing human intervention.

Supervised Learning
- Trains models on labeled datasets comprising input-output pairs.
- Common algorithms include Linear Regression, Decision Trees, Support Vector Machines, and Neural Networks.
- Applications encompass Classification (e.g., spam detection) and Regression (e.g., price prediction).
Unsupervised Learning
- Trains models on unlabeled datasets lacking explicit outputs.
- Common algorithms involve K-means Clustering, Hierarchical Clustering, and Principal Component Analysis (PCA).
- Applications include Customer Segmentation and Anomaly Detection.
Semi-Supervised Learning
- Combines a limited set of labeled data with a vast amount of unlabeled data.
- Particularly beneficial when obtaining labeled data is costly or labor-intensive.
Reinforcement Learning
- Trains agents to make decisions based on rewards or penalties derived from their actions.
- Widely applied in sectors like robotics, gaming, and autonomous systems.

Features: Measurable properties or characteristics used as input for models.
Training: Feeding data into a model for the purpose of learning.
Model: Mathematical representation of a process derived from training data.
Overfitting: Occurs when a model excessively learns training data specifics, including noise, negatively impacting performance on new data.
Underfitting: Happens when a model is overly simplistic, failing to capture underlying data trends.

Accuracy: Ratio of correct predictions to total predictions.
Precision: Ratio of true positive predictions to total predicted positives.
Recall (Sensitivity): Ratio of true positive predictions to actual positives.
F1 Score: Harmonic mean of precision and recall, beneficial for imbalanced datasets.
Confusion Matrix: A tabular representation summarizing the performance of a classification model.

Programming Languages: Predominantly Python, R, and Java.
Libraries:
- TensorFlow: An open-source library for deep learning.
- Scikit-learn: A framework for classical machine learning algorithms.
- Keras: A high-level API for creating neural networks.
Platforms: Google Cloud ML, AWS SageMaker, and Microsoft Azure ML provide environments for deploying ML applications.

Data Quality and Quantity: Dependency on substantial amounts of clean, labeled data for effective learning.
Model Interpretability: The challenge of understanding the decision-making processes of models.
Bias and Fairness: The need to ensure models do not reinforce existing biases found in training data.