In a world where almost everything is automated, the process of doing things manually is fast being eliminated. Today, there are many different types of machine learning algorithms, some of which can help computers play chess, doctors perform remote surgeries, retailers increase sales, and software development companies personalize applications.
We live in an age where technologies are constantly advancing. It is enough to look at the recent developments in the last two years to see what great developments have occurred and continue to predict what will happen in the coming years.
One of the main features and indicators of the technological revolution is the automation and intelligence of tools, services and hardware that we use daily. Over the past five years, machine learning engineers have developed sophisticated algorithms for data collection and analysis through the use of advanced techniques and achieved surprising results.
These smart algorithms help companies solve complex real-world problems. Machine learning algorithms have two key features, the ability to do things automatically and self-learning are the most important of them. Combining these two features with each other will improve their performance over time. Before we explore the top 10 machine learning algorithms, let's first take a look at the different types of machine learning algorithms and how they are classified. Machine learning algorithms are classified into the following 4 types:
Supervised
Supervised learning or supervised learning is an algorithm that learns from labeled training data to help us predict outcomes for unpredicted data. In supervised learning, we train the machine using data that has been labeled. More precisely, some data is already labeled with correct answers. When training a supervised learning algorithm, the training data consists of inputs that are matched with correct outputs, meaning that the data contains correct output labels. During training, the algorithm looks for patterns in the data that are related to the desired outputs and learns those patterns.
Unsupervised Learning
Unsupervised learning uses unlabeled data sets for analysis and clustering. These algorithms, without the need for human intervention, discover hidden patterns or different groups in the data.
Semi-supervised learning
Semi-supervised learning uses a combination of a limited amount of labeled data and a significant amount of unlabeled data to train models. This algorithm is a combination of supervised learning that uses labeled training data and unsupervised learning that uses unlabeled training data.
Reinforcement Learning
In reinforcement learning, there is an agent in an environment that can perform actions, just like humans. Every action has a result. The agent is trying to maximize his rewards. The result of each action is either a positive reward or a negative reward. Over time, the agent learns from these results to improve its actions, hence, we can say that reinforcement learning is feedback-based learning. If we look at the case more specifically, we must say that reinforcement learning is one of the types of learning models in the field of machine learning, in which an agent learns to reach a goal in an uncertain and complex environment. There are two main methods for teaching correct decision-making in reinforcement learning environments, the first is policy-based and the second is called Q-Learning or Value Function.
Each of these four main classifications includes different algorithms that are used for specific applications.
10 Popular Machine Learning Algorithms
In the world of machine learning, there are many different types of algorithms, however, some of them are more widely used and more important. These algorithms are as follows:
- Linear Regression
- Logistic Regression
- Dimensionality Reduction
- Decision Tree
- Support Vector Machine model
- Naïve Bayes
- k to the nearest neighbor (K Nearest Neighbors)
- Random Forest
- K-means
- Apriori algorithm
1. Linear regression
To understand how linear regression works, imagine how you can sort logs by weight. At first, this seems a bit difficult. In a situation where weighing each of the tree trunks seems to be a simple task, but it is time-consuming. A simple solution to the above problem is to look at the height and girth of the wood (visual analysis) and sort them based on a combination of observable parameters. This is how linear regression works in machine learning. In this process, a relationship is established between independent and dependent variables by fitting them in a line. This line is known as the "regression line" and is represented by the linear equation Y= aX + b. In this equation, the variables are as follows:
Y is the dependent variable.
a shows the slope of the line.
X is the independent variable.
b shows the value of the width from the origin of the line axis.
In the above equation, the coefficients a and b are obtained by minimizing the square of the difference between the data points and the regression line.
2. Logistic regression
Logistic regression is used to estimate discrete values (usually binary values such as zero and one) from a set of independent variables. The above model helps to predict the probability of an event by fitting the data to a logit function, which is also called logit regression. However, machine learning engineers use the following methods to improve the performance of logistic regression models:
Interactive effect
Remove features
Ordering techniques
Using a non-linear model
3. Decision tree
The decision tree algorithm in machine learning is one of the most popular algorithms used today. A decision tree is a supervised learning algorithm used to classify problems. The decision tree has a good performance in classifying definite and continuous dependent variables. This algorithm divides the population (data set) into two or more homogeneous sets based on the most important features/independent variables.
4. Support Vector Machine algorithm
The support vector machine algorithm for raw data classification plots points in an n-dimensional space where n is the number of features. Each feature value is then assigned to a specific coordinate to simplify data classification. For this purpose, lines called classifiers are used to divide the data and draw them on a graph.
5. Simple Bayes algorithm
Simple Bayes algorithm works based on probabilistic models. Simple Bayes algorithm assumes that a particular feature in a class is not related to any other feature. Even if these features are related to each other, the Bayes algorithm considers all of these features independently when calculating the probability of a particular outcome. Typically, machine learning engineers use simple Bayes models for large datasets. The simple Bayes algorithm has an explicit and clear performance and in some cases it even performs better than very complex classification methods.
6. K-Nearest Neighbors algorithm
This algorithm can be used for classification and regression problems. Typically, in the world of data science, experts use this algorithm to solve classification problems. The k-nearest-neighbor algorithm has a simple operation, it stores all existing items and classifies each new item with the majority vote. Then, it chooses the class that has the most subscription. This process is done through a distance function which is responsible for measuring the distance. To better understand the performance of the KNN algorithm, it is not bad to refer to an example in the real world. If you want information about someone, it makes sense to talk to their friends and colleagues! If you decide to use the K-nearest neighbor algorithm, pay attention to the following:
KNN is computationally expensive.
Variables must be normalized; Otherwise, higher range variables can bias and linearize the algorithm.
The data still needs to be pre-processed.
7. K-Means
is an unsupervised learning algorithm used to solve clustering problems. The above algorithm works by classifying the data set into a certain number of clusters called K. In such a way that all the data points are placed in a homogeneous and heterogeneous cluster from the data of other clusters. The functional mechanism of the K-means algorithm is as follows:
K-means algorithm selects several center points for each k cluster.
Each data point forms a cluster with the nearest centers, i.e. K clusters.
New hubs are created based on existing cluster members.
Based on the newly constructed centroids, the nearest distance is determined for each data point. This process is repeated until the centers have variable nature.
8. Random forest algorithm
A set of decision trees is called a random forest. This algorithm was invented by two statistics experts named Leo Breiman and Adele Cutler. The function of the random forest algorithm is to combine the output of several decision trees to reach a single result. Decision trees seek to find the best partition for a subset of data and do this through the Classification and Regression Tree algorithm. The quality of the performed segmentation is measured by using methods such as gross Gini, information gain or mean squared error. The function of random forest is as follows:
If the number of items in the training set is N, a sample of N items is randomly selected. This example is basically a training set that is used to grow the tree.
If there are M input variables, a number m<<M is specified, so that at each node, m variables are randomly selected from M and the best split over these m is used to split the node. The value of m remains constant during this process. Since there is no pruning, each tree grows indefinitely.
9. Dimension reduction algorithms
In today's world, companies, government agencies, and research organizations generate and store vast amounts of data for analysis. This raw data contains a lot of information, however the main challenge is to identify important patterns and variables. Dimensionality reduction algorithms such as decision tree, factor analysis, missing value ratio, and random forest can help you find relevant details.
10. Gradient boosting algorithm and AdaBoosting algorithm
Gradient Boosting Algorithm and AdaBoosting Algorithm are reinforcement learning algorithms that are used for high accuracy predictions and when we have to manage a large amount of data. The gradient boosting algorithm is a machine learning method for regression and classification problems that creates a predictive model in the form of a set of weak predictive models. The accuracy of a predictive model can be enhanced in two ways:
Using feature engineering
Direct application of boosting algorithms
AdaBoost creates an additive logistic regression model with stepwise fitting. In AdaBoost, we use an exponential loss function as follows.
L(y, F(x)) = exp(-yF(x))
In short, the above algorithm combines several weak or moderate predictive models to build a strong predictive model. These boosting algorithms perform well in data science projects like Kaggle, AV Hackathon, CrowdAnalytix. It should be explained that the above algorithm is used in programming languages such as Python and R to achieve accurate results.
last words
If you are looking for a job in the field of machine learning, we suggest you start studying the above algorithms. Considering that the field of artificial intelligence and machine learning is expanding rapidly, it is better to master machine learning tools as soon as possible so that you can find a job that suits your interests. If you are experienced in this field and want to advance your career, it is better to focus on learning these algorithms more precisely because these algorithms will play an important role in the world of IT in the future. In addition, we suggest you increase your knowledge in Python, deep learning algorithms with tensors, natural language processing, speech recognition, computer vision, and reinforcement learning.
