Machine Learning Algorithms

Types of Machine Learning

Supervised Machine Learning :

Supervised machine learning is when the algorithm learns by analyzing data that has been labeled. Labeled data is divided into two groups: training data (used to train the model) and testing data (used to see how well the model performed). Supervised machine learning aims to use the labeled data to teach the AI to make predictions about future events or behaviors on unlabeled data.

Unsupervised Machine Learning:

Unsupervised machine learning is when an algorithm learns from unlabeled or unknown data points without any known output, called unlabeled sets. Without known target outputs, unsupervised learning can group different data points according to some criteria like clustering. Unsupervised methods allow machines to learn from natural data such as images or human speech.

Reinforcement Learning:

Reinforcement learning is an area of machine learning that deals with the problem of how an artificial agent can figure out what action to take in a particular environment to maximize some kind of reward. It differs from other methods, such as supervised and unsupervised learning, by providing feedback after each step in the form of the reward. The agent then uses this feedback to create a policy that it can use to select its future actions. Here are some ways reinforcement learning helps us learn more about our world. It is often used in artificial intelligence applications to solve problems. Reinforcement learning allows an agent to learn by trial and error in a dynamic environment with many possible states. The agent learns by receiving "rewards" or "penalties" after every single action it takes. The goal of reinforcement learning is to find out how to maximize the total reward received over time, which usually means finding the best sequence of actions to take to achieve the desired objective. 

Linear Regression :

Linear regression is a statistical technique used to predict and analyze the relationship between two variables. It is also called linear regression analysis and is considered a method of multiple linear regression. A simple example: we would like to know how long it takes for a car to travel at different speeds. We want to know how this affects the length of the trip. Linear regression will tell us what speeds are best for getting somewhere in the shortest amount of time, how much more gas we will use, or which route will take less time. A major benefit of linear regression is that it allows us to use data from both quantitative and qualitative sources to make predictions about future events. With this information, we can make better decisions for ourselves and our organizations. A linear regression equation takes the form of Y = c+ βX, where Y stands for the predicted value of the dependent variable (for example, profit), X stands for the independent variable (for example, an individual's income), and β represents the slope coefficient. The slope coefficient indicates how much Y changes for every one-unit increase in X; it is also called the beta coefficient or simply beta. Linear regression is useful because it simplifies complex systems into a straight line with an equation that can be easily interpreted and calculated.

Logistic Regression :

Logistic regression is an extension of linear regression. Linear regression is when the dependent variable is continuous, and logistic regression deals with categorical data. Logistic regression is a statistical technique that allows for the analysis of data with binary outcome variables. This model is used when the dependent variable has two values, one success and one failure. The dichotomous outcome variable can be broken down into multiple explanatory variables. These variables are then tested to see if they are significant or not. Logistic regression should not be confused with other types of regressions because it only works with binary outcome variables, and this distinction cannot be ignored when interpreting results. Logistic regression models predict the probability that an event will occur by using one or more predictor variables. These variables can be continuous (like age) or categorical (like gender). Typically, the goal of these models is to estimate the probability of a certain outcome occurring (i.e., whether a customer will buy this product). This information can then be used to make informed decisions about marketing campaigns or product design.

Naive Bayes Algorithm:

The Naive Bayes algorithm is a probabilistic machine learning model, a classification algorithm that belongs to the family of conditional probability algorithms. Naive Bayes assumes that all features are independent of one another and uses Bayes theorem or a variant thereof for calculations. Unlike many other algorithms in the family of probabilistic classification algorithms, it can be computationally effective with a small amount of training data.

Naive Bayes is often used for text classification, such as spam filtering or sentiment analysis, because texts have few features and tend to be drawn from a limited vocabulary. In these domains, naive Bayesian models tend to perform well both empirically and theoretically. It uses conditional probabilities, which are the probabilities of each possible outcome given the other outcomes, to predict the value for an unobserved variable. The algorithm can be used in many different ways, but classification is one of its most common applications. Naive Bayes classifiers are easy to train and perform well when the categories are mutually exclusive (i.e., no overlap in the data). It is often used in text classification applications where words can have more than one meaning, and there is no reliable way to assign different weights to different meanings.

This can be attributed to words having multiple meanings or synonyms. For example, a naïve Bayes classifier may classify "chicken" as a member of both the "bird" and "meta" categories. They are especially popular on email spam filtering, market basket analysis, and photo tagging.

K-Nearest Neighbors:

KNN is a supervised learning algorithm, which means that it uses labeled examples to learn. It is a machine learning algorithm that categorizes data based on its distance to other pieces of data. KNN classifies data by finding the most similar labeled example in its neighborhood. It is used in classification, regression analysis, cluster analysis, density estimation, anomaly detection, and many other analytical tasks. This algorithm has been applied in many fields such as marketing, finance, and medicine.

Decision Trees :

Decision trees are well-known machine learning algorithms because they are commonly used in planning software systems and data analytics. The algorithm is usually used in conjunction with clustering algorithms. When a set of input data is first fed into a decision tree, it only has one possible outcome. The next input to the algorithm does not have any effect on what the tree will do next. As the set of inputs continues to grow and multiple outcomes become possible, the branches of the tree branch out and connect. A decision tree is useful in determining the optimal way to plan the execution of algorithms. A decision tree does not create .new variables but allows a set of variables to be attached to different branches.

Random Forests :

One of the most popular machine learning algorithms is random forests. Its simplicity and ability to utilize past data is what makes it such a powerful and popular algorithm. A machine learning technique, random forests can significantly reduce prediction errors in a wide variety of fields. It is most often used for either regression or classification. For regression, the goal is to predict a real-valued outcome based on many input features. This is done by creating individual decision trees with bootstrapped data samples that are sampled at random. The final prediction is achieved by averaging the predictions from all trees. Each tree creates its own rules to classify its input data points into one of two classes (e.g., yes or no). This classifier can make accurate predictions if it has encountered enough data points that share commonalities with the new data point being predicted.

Random forests have a series of steps that are:

Step 1: Train a model using a set of training data.

Step 2: Reduce the model over several runs.

Step 3: Use the information gained from Step 2 to predict a new set of data using the algorithm again.

The random forests method is highly recommended when learning through patterns and generating predictions from a set of training data. It is especially useful for problems with large numbers of input variables and no clear boundaries between classes. Random forests are not dependent on the number of samples in the training data. They are robust to missing values and can handle nonlinear relationships between variables. They also show better generalization performance than other machine-learning models, such as support vector machines and linear discriminant analysis.

Random forests, or random decision trees, are a type of ensemble learning. Ensemble learning is when we use more than one algorithm to solve a problem and combine the results. Random forests are an example of bagging where we take many random samples from our data and then average all the samples together to create a new model. This means that every time we go through the process, we get a different answer. By averaging out our errors, we can get a more accurate model than just one tree would give us.

Support Vector Machines (SVM) :

For more than half a century, the support vector machine algorithm has been one of the most widely used machine learning algorithms. It is a mathematical model that has many applications in many different areas. Support Vector Machines are a type of supervised machine learning model that can be used for classification and regression tasks. They can be generalized as a linear classifier, where the vector space is a high dimensional space with a hyperplane that separates instances of two classes. Support Vector Machine is an algorithm that finds the optimum separating hyperplane, which maximizes the margin between two classes. It is very fast and accurate, and it offers some advantages over other methods such as Logistic Regression. The algorithm will find the linear border between two different classes to be used to create a classifier. An SVM can also be used for regression, where the goal is to predict values instead of classifying instances into categories. A Support Vector Machine can be understood as a binary decision boundary that attempts to maximize the distance between the closest points in each of the classes being separated by the decision boundary. SVMs are classified as nonparametric classifiers because they don't make assumptions about the underlying distribution of the data. SVM classification algorithms are trained with labeled examples that have been annotated with their target label or category. After training, an SVM can classify new inputs based on these annotations. When applied for text classification, an SVM will learn to identify both positive and negative words in sentences, phrases, and paragraphs.