Algorithms and Machine Learning

An algorithm is a finite and ordered sequence of instructions or steps that solve a problem or perform a specific task. In computer science, algorithms are fundamental to data processing and analysis. There are several types of algorithms, including:

1. Search algorithmsFind elements within a data structure (e.g., binary search).
2. Sorting algorithms: Sort elements according to a criterion (e.g., quicksort).
3. Graph algorithms: Solve problems in graphs, such as shortest path (e.g., Dijkstra).
4. Machine learning algorithmsThey learn patterns from data (e.g., neural networks).

In Data Science, machine learning algorithms are used to create models. These are the algorithms you will learn in Ubiqum.

1. Linear RegressionThis algorithm is used to predict a continuous value based on one or more independent variables. An example would be the prediction of housing prices, based on factors such as size in m2, location and number of rooms to see how they influence the price.
2. Logistic RegressionBinary classification: Mainly used for binary classification, this algorithm predicts the probability that an instance belongs to a particular class. An example is medical diagnosis, where the probability that a patient has a disease is estimated based on symptoms and test results.
3. Decision Trees and Random ForestDecision trees divide the data set into subsets based on the feature that provides the maximum information gain. Random Forest, which combine multiple decision trees, are used in image classification and predictive analytics in the financial industry.
4. K-Nearest Neighbors (K-NN)This algorithm classifies an instance based on the majority of its K nearest neighbors. It is common in recommender systems, where products are suggested to a user based on the preferences of similar users.
5. Clustering (K-means)K-clustering: This algorithm groups data into K clusters based on similar characteristics. It is useful in customer segmentation in marketing, where customers with similar buying behaviors are grouped together to personalize sales strategies.
6. Neural Networks (advanced)Deep neural networks: Inspired by the human brain, these networks are effective for complex tasks such as speech recognition, machine translation and advanced medical diagnostics. Deep learning neural networks are used in artificial intelligence applications such as autonomous driving.
7. Time Series Analysis (Advanced) . Widely used for demand or inventory forecasting.