A machine learning algorithm refers to the programming code (mathematical or programming logic) that enables professionals to study, analyze, understand, and explore large, complex datasets. Each algorithm follows a series of instructions to achieve the goal of making predictions or categorizing information by learning and discovering patterns embedded in the data. It also specifies the rules and processes that a system should consider when solving a particular problem. These algorithms analyze and simulate data to predict an outcome within a predetermined range. Additionally, as new data is fed into these algorithms, they learn, optimize, and improve based on feedback from past performance in predicting outcomes. Simply, machine learning algorithms tend to get "smarter" with each iteration. Depending on the type of algorithm, machine learning models use parameters such as gamma parameter, max depth, N neighbors, and others to analyze the data and produce accurate results. These parameters result from the training data, representing the more extensive data set. Machine learning algorithms are divided into four types based on learning techniques: supervised learning, unsupervised, and reinforcement learning. Regression and classification algorithms are the most popular options for predicting values, identifying similarities, and discovering unusual data patterns. These are also considered algorithms for beginners.

Supervised Learning Algorithms

The supervised algorithm uses labeled data sets to make predictions. This learning technique is beneficial when you know what kind of outcome you want to achieve. For example, suppose you have a data set that specifies the rainfall that has occurred in a certain geographic area during a certain period over the past 200 years. You intend to know the expected rain during this particular period for the next ten years. Here the result is derived based on the labels existing in the original data set i.e. rainfall, geographical area, season, and year.

Unsupervised Learning algorithms

Unsupervised learning algorithms use unlabeled data. This learning technique labels unlabeled data by categorizing the data or expressing its type, form, or structure. This technique is useful when we do not know the type of result. For example, when using a dataset of Facebook users, you intend to classify users who show an inclination (based on Likes) to similar Facebook advertising campaigns. In this case, the dataset is not labeled. However, the result will have labels because the algorithm finds similarities between data points when classifying users.

Reinforcement learning algorithms

Reinforcement learning algorithms use the outcome or result as a benchmark to decide the next action step. In other words, these algorithms learn from previous results, receive feedback after each step, and then decide whether to continue with the next step or not. The system will learn whether it made a right, wrong or neutral decision in the process. For example, you design a self-driving car and you intend to monitor whether the car complies with road traffic rules and ensures road safety. By applying reinforcement learning, the vehicle learns through experience and reinforcement tactics.

Here is the list of top machine learning algorithms for beginners

1. Linear Regression Linear regression belongs to the supervised learning algorithm. It provides the relationship between the input (x) and the output variable (y), also referred to as the independent and dependent variables. Let's understand this using an example where several plastic boxes of different sizes need to be arranged on separate shelves based on their corresponding weight. The task must be completed without manually weighing the boxes. Instead, you have to guess the weight just by looking at the height, dimensions, and sizes of the boxes. In short, the whole task is managed based on visual analysis. So you have to use a combination of visible variables for the final arrangement on the shelves. Linear regression in machine learning is of a similar kind, where the relationship between independent and dependent variables is determined by fitting them to a regression line.

2. Logistic regression

Logistic regression is one of the most popular machine learning algorithms that fall under the supervised learning technique. It is used to predict a categorical dependent variable using a given set of independent variables. It predicts the output of a categorical dependent variable. Therefore, the result must be a categorical or discrete value. It can be either Yes or No, 0 or 1, true or false, etc., but instead of an exact value like 0 and 1, it gives probability values ??that lie between 0 and 1. It is very similar to linear regression except in how they are used. Linear regression is used to solve regression problems while logistic regression is used to solve classification problems. It can be used to classify observations using different data types and can easily determine the most effective variables used for classification.

3. Decision Trees

Using a decision tree, you can visualize a map of potential outcomes for a series of decisions. Decision tree algorithms can potentially predict the best option based on a mathematical construct and can also be useful when brainstorming a specific decision. The tree starts with the root node (the decision node) and then branches to subnodes representing potential outcomes. Each result can further create child nodes that can open up other possibilities. The algorithm generates a tree structure that is used for classification problems. For example, consider the decision tree below that helps finalize a weekend schedule based on the weather forecast.

4. Random Forest

Random Forest is a popular machine learning algorithm that belongs to the supervised learning algorithm. It can be used for both classification and regression problems in ML. It is based on the concept of ensemble learning, which is the process of combining multiple classifiers to solve a complex problem and improve model performance. As the name suggests, "Random Forest is a classifier that contains a number of decision trees on different subsets of a given data set and takes the average to improve the predictive accuracy of that data set." Instead of relying on a single decision tree, a random forest takes a prediction from each tree, and based on the majority of votes it predicts and predicts the final output.

5. KNN Classification Algorithm

The K Nearest Neighbors (KNN) algorithm is used for both classification and regression problems. It stores all known use cases and classifies new use cases (or data points) by dividing them into different classes. This classification is done based on the similarity score of recent use cases with available cases. KNN is a supervised machine learning algorithm, where "K" denotes the number of neighboring points we consider when classifying and segregating known n groups. The algorithm learns at each step and iteration, eliminating the need for any specific learning phase. Classification is based on neighbor majority voting. The algorithm uses the following steps to perform the classification: For the training data set, calculate the distance between the data points to be classified and the rest of the data points. Select the closest "K" elements based on the distance or function used. Consider "majority voting" among K points - the class or label dominating all data points reveals the final ranking.

6. K-Means

K-Means is a distance-based unsupervised machine learning algorithm that performs clustering tasks. In this algorithm, you classify data sets into clusters (K clusters), where data points within one set remain homogeneous and data points from two different clusters remain heterogeneous. Clusters under K-Means are formed using the following steps: Initialization: The K-means algorithm selects the centroid for each cluster (number of 'K' points). Assigning objects to the centroid: Clusters are formed with the closest centroids (K clusters) at each data point. Update centroid: Create new centroids based on existing clusters and determine the closest distance for each data point based on the new centroids. If necessary, the position of the center of gravity is also updated here. Repeat: Repeat the process until the center of gravity changes

7. Naive Bayes

Naive Bayes refers to a probabilistic machine learning algorithm based on the Bayesian probability model and used to solve classification problems. The basic assumption of the algorithm is that the considered elements are independent of each other and a change in the value of one does not affect the value of the other. The naive Bayesian approach is easy to develop and implement. It is capable of handling massive datasets and is useful for making real-time predictions. Its applications include spam filtering, sentiment analysis and prediction, document classification, and more.

8. Support Vector Machine (SVM)

Support Vector Machine algorithms are used to perform both classification and regression tasks. These are supervised machine learning algorithms that plot each piece of data in an n-dimensional space, where n denotes the number of features. Each feature value is associated with a coordinate value, making it easy to draw features. In addition, the classification is further done by distinctly determining the hyper-plane that separates the two sets of support vectors or classes. Good separation ensures good classification between plotted data points.