Machine Learning: Basic Concepts pt1

1. Machine Learning

Machine learning is a subset of Artificial Intelligence (AI) that involves the use of statistical models and algorithms to enable computers to learn from data without being explicitly programmed. It's like common sense, but performed by a computer.

2. Supervised Learning

Supervised learning is a type of machine learning used on labeled data and consists of building Models that predict the labels for unseen data.

3. Unsupervised Learning

Unsupervised learning is a type of machine learning where the algorithm learns from unlabeled data to identify patterns and relationships.

The main branches of unsupervised learning are clustering, dimensionality reduction and generative Algorithms.

4. Reinforcement Learning

Reinforcement learning is a type of machine learning where the algorithm learns by interacting with an enviroment to maximaze a reward function.

5. Deep Learning

Deep Learning is a subset of machine learning that involves the use of neural networks with multiple layers to learn complex patterns and relationships in data.

6. Neural Network

Neural network is a type of machine learning algorithm that is designed to simulate the behavior of the human brain by processing information through a network of interconnected nodes or neurons.

7. Overfitting

Overfitting occurs when a machine learning algorithm learns from noise or irrelevant features in the training data, resulting in poor performance on new or unseen data.

8. Underfitting

Underfitting occurs when a machine learning algorithm is too simple and cannot capture the complexity of the data, resulting in poor performance on both the training and test data.

9. Cross-validation

Cross-validation is a technique used to evaluate the performance of a machine learning algorithm by splitting the data into multiple subsets and using each subset for both training and testing.

10. Feature Selection

Feature selection is the process of selecting the most important features or variables in a dataset to improve the performance of a machine learning algorithm.

11. Feature Extraction

Feature extraction is the process of transforming raw data into a set of features that can be used for machine learning.

12. Data Preprocessing

Data preprocessing is the process of cleaning, transforming, and preparing data for analysis and modeling in machine learning.

13. Feature scaling

Feature scaling is a technique used in machine learning to normilize the range and scale of different features or variables in a dataset to improve the preformance of the model.

14. Hyperparameter

A hyperparamete is a parameter in a machine learning algorithm that is not learned from data but is set by the user.

15. Confusion Matrix

A confusion matrix is a table that is used to evaluate the performance of a machine learning algorithm by comparing the predicted and actual values of the target variable.