# Top 10 Machine Learning Algorithms for Beginners

Machine learning (ML) is rapidly changing the world and is affecting every aspect of our daily life. From voice assistants that use NLP and machine learning to make appointments, check our calendar, play music, to programmatic announcements, it is everywhere. Most of the time, the complexity of the scientific field of machine learning can be overwhelming, making keeping up with “what’s important” a very challenging task. The first step to learning is mastering the essential machine learning concepts. To start with, let’s explore the **best** ** machine learning algorithms** for beginners.

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**Top Machine Learning Algorithms for Beginners**

- KNN Classification Algorithm
- Support Vector Machine (SVMs) Algorithms
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest Algorithms
- Artificial Neural Networks
- Recurrent Neural Networks
- K-means Clustering
- Naive Bayes Theorem

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**KNN Classification Algorithm**

K Nearest Neighbors is a simple algorithm that stores all available cases and separates new use cases and data points into different classes so that we can classify them according to measures of similarity.

In K-Nearest Neighbor, “K” means the number of “neighboring points” that we take into account in the vicinity to classify the “n” groups – which are already known, since it is a supervised algorithm. KNN learns as it moves forward and does not require any specific learning phase. It begins to classify the data points decided by the majority vote of its neighbors.

**How does KNN work?**

- Calculates the distance between the item to be classified and the rest of the items in the training dataset
- Selects the closest “k” elements (with less distance, depending on the function used)
- Carries out a “majority vote” between the k points: those of a class/label that dominates will decide their final ranking

Image – **How KNN works?**

Source – Medium

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**Support Vector Machine (SVMs) Algorithms**

SVMs are among the best and the most popular classifiers in statistical learning and machine learning. SVMs are based on the Maximal Margin Classifier, which in turn, is based on the hyperplane concept. Understanding the fundamentals of SVMs requires a solid knowledge of linear algebra.

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**How SVMs work?**

A set of training examples is fed into the SVM algorithm, and then the algorithm builds a model that begins to assign new data to one of the categories that it learned during the learning phase. SVM constructs a hyperplane or set of hyperplanes in a very high (or even infinite) dimensional space that can be used in classification or regression problems. A good separation between the classes will allow a correct classification.

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**Linear Regression**

Initially developed in statistics to study the relationship between input and output digital variables, it has been adopted by the machine learning community to make predictions based on the linear regression equation.

The mathematical representation of linear regression is a close relationship equation that combines a specific set of input data (x) to predict the output value (y) for this set of input values. The linear equation assigns a factor to each set of input values, which are called coefficients represented by the Greek letter Beta (β). The Linear Regression equation with two sets of input values is as follows –

## y = β0 + β1x1 + β2x2

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**Logistic Regression **

In logistic regression, our goal should be to produce a discrete value, either 1 or 0. This helps us find a definitive answer. The logistic regression model calculates a weighted sum of the input variables similar to linear regression but runs the result through a special nonlinear function, the logistic function, or the sigmoid function to produce the output y.

Logistic regression can be mathematically represented as –

## y = e^(b0 + b1*x) / (1 + e^(b0 + b1*x))

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**Decision Trees **

A decision tree is a map of the possible outcomes of a series of related decisions. It allows an individual or an organization to compare possible actions against each other based on their costs, probabilities, and benefits. They can be used to lead an informal brainstorming or devise an algorithm that mathematically anticipates the best option.

A decision tree usually starts with a single node and then branches out into possible outcomes. Every result creates additional nodes that can lead to other possibilities. This entire process creates a tree-shaped structure.

There are three different types of nodes: probability nodes, decision nodes, and terminal nodes.

Image – **Classification and Regression Analysis with Decision Trees**

**Random Forest Algorithms**

Random Forest Algorithms include decision trees that represent their course of action or statistical probability. These multiple trees are assigned to a single tree called the Classification and Regression Model (CART). It is a learning method in which the number of decision trees is constructed at the time of training and outputs of the modal predicted by the individual trees. Random Forest Algorithms have proved to be very effective to estimate missing data and they maintain accuracy when a large proportion of the data are missing.

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**How do Random Forest Algorithms work?**

- It selects random samples from a given data set
- Random Forest Algorithms build a decision tree for each sample to provide the prediction result for each decision tree
- Voting takes place for each expected result
- The highest voted prediction result is selected as the final prediction result

**Artificial Neural Networks**

The artificial neural networks (ANN) are models of artificial intelligence inspired by the behavior of neurons and brain connections to solve problems. As a computational model, ANNs use graphs and functions made up of process elements (EP or nodes) and connections (links). They process inputs and generate outputs that help solve problems. In some models, local memory is used in the nodes or process elements. The nodes and connections of the neural network are organized in layers.

Image – ANN

**Applications of artificial neural networks **

- Intelligent systems for decision making in business management
- Prediction
- Trend recognition
- Pattern recognition and risk management applied for example in fraud detection
- Smart devices with learning capacity, for example, homepods or smart speakers
- Smart home or home automation
- Computational vision and detection systems
- Autonomous vehicles and renewable energy

**Recurrent Neural Networks **

Did you know that Siri and Google Assistant use RNN in their programming? RNNs are a type of ANNs that has a memory attached to each node, which facilitates the processing of sequential data, that is, one unit of data depends on the previous one.

Recurrent neural networks or Recurrent Neural Networks (RNN) are a class of networks for analyzing time series data, allowing us to treat the dimension of “time”. Unlike other direct neural networks, the RNN can maintain a state of context and even store, learn, and express related information in context windows of any length.

Unlike traditional neural networks, it is not limited to the limit of space but also supports time sequences. In other words, there is a side between the hidden layer of the current moment and the hidden layer of the next moment. RNN is widely used in sequence-related scenarios such as videos consisting of picture frames, audio consisting of clips, and sentences consisting of words.

Image – Type of RNNs

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**K-means Clustering**

K-means is an unsupervised classification algorithm that groups object into k groups based on their characteristics. Grouping is done by minimizing the sum of distances between each object and the centroid of its group or cluster.

The algorithm functions in the below three steps –

**Initialization** – Once the number of groups, k has been chosen, k centroids are established in the data space, for example, choosing them randomly

**Assigning objects to centroids** – Each object in the data is assigned to its closest centroid

**Centroid Update** – The position of the centroid of each group is updated, taking as the new centroid the position of the average of the objects belonging to that group.

Image – Labelled data clusters with K-means algorithms

In this machine learning algorithm, the goal is to label the data points according to their similarity. Therefore, we do not define the groups before the algorithm, but rather the algorithm looks for these groups as it moves forward.

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**Naive Bayes Theorem**

Naive Bayes is a prediction model based on Bayesian probability. The model is very simple, but powerful, in that it is a direct result of the data and its treatment with simple Bayesian statistics of the conditional probability. The Naive Bayes classification algorithm assumes that two events are independent of each other and, therefore, this greatly simplifies the calculations. Initially thought of as nothing more than an academic exercise, Naive Bayes has also been shown to work very well in the real world.

Image – Bayes’ Theorem

Source – Towards Data Science

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These are the top ten most important Machine Learning Algorithms today. As time passes and the requirements become more complex, there will be variants of existing ones and new machine learning algorithms.

Once you are clear about these two points and know the aforementioned Machine Learning Algorithms, you can choose the one that best suits your needs and start your machine learning journey.

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