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Machine Learning - Marcador
Machine Learning - Detalles
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| 🇬🇧 | 🇬🇧 |
| SUPERVISED LEARNING | Supervised learning (SL) is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples. |
| SUBCATEGORIES OF SUPERVISED LEARNING | Classification and regression. |
| CLASSIFICATION IN SUPERVISED LEARNING | The job of a classification algorithm is to then take an input value and assign it a class, or category, that it fits into based on the training data provided. |
| EXAMPLE OF CLASSIFICATION IN SUPERVISED LEARNING | The most common example of classification is determining if an email is spam or not. With two classes to choose from (spam, or not spam), this problem is called a binary classification problem. The algorithm will be given training data with emails that are both spam and not spam. The model will find the features within the data that correlate to either class and create the mapping function mentioned earlier: Y=f(x). Then, when provided with an unseen email, the model will use this function to determine whether or not the email is spam. |
| POPULAR CLASSIFICATION ALGORITHMS | Linear Classifiers, Support Vector, Machines Decision Trees, K-Nearest Neighbor, Random Forest |
| REGRESSION IN SUPERVISED LEARNING | Regression is a predictive statistical process where the model attempts to find the important relationship between dependent and independent variables. The goal of a regression algorithm is to predict a continuous number such as sales, income, and test scores. |
| MOST COMMON TYPES OF REGRESSION ALGORITHMS | Linear Regression, Logistic Regression, Polynomial Regression |
| UNSUPERVISED LEARNING | Unsupervised learning (UL) is a type of algorithm that learns patterns from untagged data. The hope is that through mimicry, the machine is forced to build a compact internal representation of its world. In contrast to supervised learning (SL) where data is tagged by a human, e.g. as "car" or "fish" etc, UL exhibits self-organization that captures patterns as neuronal predilections or probability densities. |
| TWO BOARD METHODS IN UL | Neural Networks and Probabilistic Methods |
| UNSUPERVISED LEARNING ALGORITHMS | Unsupervised Learning Algorithms allow users to perform more complex processing tasks compared to supervised learning. Although, unsupervised learning can be more unpredictable compared with other natural learning methods. Unsupervised learning algorithms include clustering, anomaly detection, neural networks, etc. |
| TYPES OF UNSUPERVISED LEARNING ALGORITHM | Clustering and Association |
| CLUSTERING | Clustering is a method of grouping the objects into clusters such that objects with most similarities remains into a group and has less or no similarities with the objects of another group. Cluster analysis finds the commonalities between the data objects and categorizes them as per the presence and absence of those commonalities. |
| ASSOCIATION | An association rule is an unsupervised learning method which is used for finding the relationships between variables in the large database. It determines the set of items that occurs together in the dataset. Association rule makes marketing strategy more effective. |
| REINFORCEMENT LEARNING | Reinforcement Learning(RL) is a type of machine learning technique that enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences. |
| REINFORCEMENT LEARNING ALGORITHMS | Value-Based, Policy-based, Model-Based |
| LEARNING MODELS OF REINFORCEMENT | Markov Decision Process, Q learning |
| TYPES OF REINFORCEMENT LEARNING | Positive and Negative |
| Q LEARNING | Q learning is a value-based method of supplying information to inform which action an agent should take. |