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Machine Learning

Top 10 Data Science Algorithms You Must Know About

The implementation of Data Science to any problem requires a set of skills. Machine Learning is an integral part of this skill set. For doing Data Science, you must know the various Machine Learning algorithms used for solving different types of problems, as a single algorithm cannot be the best for all types of use cases. These algorithms find an application in various tasks like prediction, classification, clustering, etc from the dataset under consideration. In this article, we will see a brief introduction to the top Data Science algorithms.

Top Data Science Algorithms

The most popular Machine Learning algorithms used by the Data Scientists are:

1. Linear Regressiondata science algorithm - linear regression

Linear regression method is used for predicting the value of the dependent variable by using the values of the independent variable. The linear regression model is suitable for predicting the value of a continuous quantity.

OR

The linear regression model represents the relationship between the input variables (x) and the output variable (y) of a dataset in terms of a line given by the equation,

y = b0 + b1x

Where,

  • y is the dependent variable whose value we want to predict.
  • x is the independent variable whose values are used for predicting the dependent variable.
  • b0 and b1 are constants in which b0 is the Y-intercept and b1 is the slope.

The main aim of this method is to find the value of b0 and b1 to find the best fit line that will be covering or will be nearest to most of the data points.

2. Logistic Regression

Linear Regression is always used for representing the relationship between some continuous values. However, contrary to this Logistic Regression works on discrete values. Logistic regression finds the most common application in solving binary classification problems, that is, when there are only two possibilities of an event, either the event will occur or it will not occur (0 or 1).

Thus, in Logistic Regression, we convert the predicted values into such values that lie in the range of 0 to 1 by using a non-linear transform function which is called a logistic function. The logistic function results in an S-shaped curve and is therefore also called a Sigmoid function given by the equation,

𝝈(x) = 1/1+e^-xdata science algorithm - logistic regressions

The equation of Logistic Regression is,

P(x) = e^(b0+b1x)/1 + e^(b0+b1x)

Where b0 and b1 are coefficients and the goal of Logistic Regression is to find the value of these coefficients.

3. Decision Trees

Decision trees help in solving both classification and prediction problems. It makes it easy to understand the data for better accuracy of the predictions. Each node of the Decision tree represents a feature or an attribute, each link represents a decision and each leaf node holds a class label, that is, the outcome.

The drawback of decision trees is that it suffers from the problem of overfitting. Basically these two Data Science algorithms are most commonly used for implementing the Decision trees.

  • ID3 ( Iterative Dichotomiser 3) Algorithm

This algorithm uses entropy and information gain as the decision metric.

  • Cart ( Classification and Regression Tree) Algorithm

This algorithm uses the Gini index as the decision metric. The below image will help you to understand things better.cart data science algorithms

4. Naive Bayes

The Naive Bayes algorithm helps in building predictive models. We use this Data Science algorithm when we want to calculate the probability of the occurrence of an event in the future. Here, we have prior knowledge that another event has already occurred.

The Naive Bayes algorithm works on the assumption that each feature is independent and has an individual contribution to the final prediction. The Naive Bayes theorem is represented by:

P(A|B) = P(B|A) P(A) / P(B)

Where A and B are two events.

  • P(A|B) is the posterior probability i.e the probability of A given that B has already occurred.
  • P(B|A) is the likelihood i.e the probability of B given that A has already occurred.
  • P(A) is the class prior to probability.
  • P(B) is the predictor prior probability.

5. KNN

KNN stands for K-Nearest Neighbours. This Data Science algorithm employs both classification and regression problems. The KNN algorithm considers the complete dataset as the training dataset. After training the model using the KNN algorithm, when we try to predict the outcome of a new data point, the KNN algorithm searches the entire data set for identifying the k most similar or nearest neighbors of that data point. It then predicts the outcome based on these k instances.

For finding the nearest neighbors of a data instance, we can use various distance measures like Euclidean distance, Hamming distance, etc. To better understand, let us consider the following example.KNN data science algorithms

Here we have represented the two classes A and B by the circle and the square respectively. Let us assume the value of k is 3. Now we will first find three data points that are closest to the new data item and enclose them in a dotted circle. Here the three closest points of the new data item belong to class A. Thus, we can say that the new data point will also belong to class A.

Now you all might be thinking that how we assumed k=3? The selection of the value of k is a very critical task. You should take such a value of k that it is neither too small nor too large. Another simpler approach is to take k = √n where n is the number of data points.

Any doubts in TechVidvan’s Data Science algorithms article till now? Ask in the comment section.

6. Support Vector Machine (SVM)

Support Vector Machine or SVM comes under the category of supervised Machine Learning algorithms and finds an application in both classification and regression problems. It is most commonly used for classification of problems and classifies the data points by using a hyperplane.

The first step of this Data Science algorithm involves plotting all the data items as individual points in an n-dimensional graph. Here, n is the number of features and the value of each individual feature is the value of a specific coordinate. Then we find the hyperplane that best separates the two classes for classifying them. Finding the correct hyperplane plays the most important role in classification. The data points which are closest to the separating hyperplane are the support vectors.svm support vectors

Let us consider the following example to understand how you can identify the right hyperplane. The basic principle for selecting the best hyperplane is that you have to choose the hyperplane that separates the two classes very well.svm hyperlanes

In this case, the hyperplane B is classifying the data points very well. Thus, B will be the right hyperplane.hyperlane classifying data points

All three hyperplanes are separating the two classes properly. In such cases, we have to select the hyperplane with the maximum margin. As we can see in the above image, the hyperplane B has the maximum margin therefore it will be the right hyperplane.svm hyperlane with maximum margins

In this case, the hyperplane B has the maximum margin but it is not classifying the two classes accurately. Thus, A will be the right hyperplane.

7. K-Means Clustering

K-means clustering is a type of unsupervised Machine Learning algorithm. Clustering basically means dividing the data set into groups of similar data items called clusters. K means clustering categorizes the data items into k groups with similar data items. For measuring this similarity, we use Euclidean distance which is given by,

D = √(x1-x2)^2 + (y1-y2)^2

K means clustering is iterative in nature. The basic steps followed by the algorithm are as follows:

  • First, we select the value of k which is equal to the number of clusters into which we want to categorize our data. Then we assign the random center values to each of these k clusters. Now we start searching for the nearest data points to the cluster centers by using the Euclidean distance formula.
  • In the next step, we calculate the mean of the data points assigned to each cluster.
  • Again we search for the nearest data points to the newly created centers and assign them to their closest clusters.
  • We should keep repeating the above steps until there is no change in the data points assigned to the k clusters.

8. Principal Component Analysis (PCA)

PCA is basically a technique for performing dimensionality reduction of the datasets with the least effect on the variance of the datasets. This means removing the redundant features but keeping the important ones. To achieve this, PCA transforms the variables of the dataset into a new set of variables. This new set of variables represents the principal components. The most important features of these principal components are:

  • All the PCs are orthogonal (i.e they are at a right angle to each other).
  • They are created in such a way that with the increasing number of components, the amount of variation that it retains starts decreasing. This means the 1st principal component retains the variation to the maximum extent as compared to the original variables.

PCA is basically used for summarizing data. While dealing with a dataset there might be some features related to each other. Thus PCA helps you to reduce such features and make predictions with less number of features without compromising with the accuracy. For example, consider the following diagram in which we have reduced a 3D space to a 2D space.data science algorithm - PCA

9. Neural Networks

Neural Networks are also known as Artificial Neural Networks. Let us understand this by an example.neural networks data science algorithms

Identifying the digits written in the above image is a very easy task for humans. This is because our brain contains millions of neurons that perform complex calculations for identifying any visual easily in no time. But for machines, this is a very difficult task to do.

Neural networks solve this problem by training the machine with a large number of examples. By this, the machine automatically learns from the data for recognizing various digits. Thus we can say that Neural Networks are the Data Science algorithms that work to make the machine identify the various patterns in the same way as a human brain does.

10. Random Forests

Random Forests overcomes the overfitting problem of decision trees and helps in solving both classification and regression problems. It works on the principle of Ensemble learning. The Ensemble learning methods believe that a large number of weak learners can work together for giving high accuracy predictions.

Random Forests work in a much similar way. It considers the prediction of a large number of individual decision trees for giving the final outcome. It calculates the number of votes of predictions of different decision trees and the prediction with the largest number of votes becomes the prediction of the model. Let us understand this by an example.random forests data science algorithms

In the above image, there are two classes labeled as A and B. In this random forest consisting of 7 decision trees, 3 have voted for class A and 4 voted for class B. As class B has received the maximum votes thus the model’s prediction will be class B.

Summary

In this article, we have gone through a basic introduction of some of the most popular Data Science algorithms among the Data Scientists. Their are various Data Science tools also which help Data Scientists to handle and analyze large amounts of data. These Data Science tools and algorithms help them to solve various Data Science problems for making better strategies.

I hope you liked TechVidvan’s Data Science algorithms article, do give us a rating on Google.

Happy Learning!!

 

Source: Top 10 Data Science Algorithms You Must Know About

Categories
Machine Learning

Trends in Machine Learning in 2020

Many industries realize the potential of Machine Learning and are incorporating it as a core technology. Progress and new applications of these tools are moving quickly in the field, and we discuss expected upcoming trends in Machine Learning for 2020.

By Tanya Singh.

To many, Machine Learning may be a new word, but it was first coined by Arthur Samuel in 1952, and since then, the constant evolution of Machine Learning has made it the go-to technology for many sectors. Right from robotic process automation to technical expertise, Machine Learning technology is extensively used to make predictions and get valuable insight into business operations. It’s considered as the subset of Artificial Intelligence (intelligence demonstrated by machines).

If we go by the books, Machine Learning can be defined as a scientific study of statistical models and complex algorithms that primarily rely on patterns and inference. The technology works independently of any explicit instruction, and that’s its strength.

The impact of Machine Learning is quite engrossing, as it has captured the attention of many companies, irrespective of their industry type. In the name of the game, Machine Learning has truly transformed the fundamentals of industries for better.

The significance of Machine Learning can be caused by the fact that $28.5 billion was allocated in this technology during the first quarter of 2019, as reported by Statista.

Taking the relevance of Machine Learning into account, we have come up with trends that are going to make way into the market in 2020. The following are the much-anticipated Machine Learning trends that will alter the basis of industries across the globe.

 

1) Regulation of Digital Data

In today’s world, data is everything. The emergence of various technologies has propelled the supplement of data. Be it the automotive industry or the manufacturing sector; data is generating at an unprecedented pace. But the question is, ‘is all the data relevant?’

Well, to untangle this mystery, Machine Learning can be deployed, as it can sort any amount of data by setting up cloud solutions and data centers. It simply filters the data as per its significance and brings up the functional data, while leaving behind the scrap. This way, it saves time allows organizations to manage the expenditure, as well.

In 2020, an enormous amount of data will be produced, and industries will require Machine Learning to categorize the relevant data for better efficiency.

 

2) Machine Learning in Voice Assistance

According to the emarketer study in 2019, it was estimated that 111.8 million people in the US would use a voice assistant for various purposes. So it’s quite evident that voice assistants are a considerable part of industries. Siri, Cortana, Google Assistant, and Amazon Alexa are some of the in-demand examples of intelligent personal assistants.

Machine Learning, coupled with Artificial Intelligence, aids in processing operations with the utmost accuracy. Therefore, Machine Learning is going to help industries to perform complicated and significant tasks effortlessly while enhancing productivity.

It’s expected that in 2020, the growing areas of research & investment will mainly focus on churning out custom-designed Machine Learning voice assistance.

 

3) For Effective Marketing

Marketing is a vital factor for every business to survive in the prevailing cut-throat competition. It promotes the presence and visibility of business while driving the intended results. But with the existing multiple marketing platforms, it has become challenging even to prove the business existence.

However, if a business is successful enough to extract the patterns from the existing user data, then the business is very much expected to formulate successful and effective marketing strategies. And to analyze the data, Machine Learning can be deployed to mine data and evaluate research methods for more beneficial results.

Adoption of Machine Learning in defining effective marketing strategies is highly anticipated in the future course of time.

 

4) Advancement of Cyber Security

In recent times, cyberspace has become the talk of the town. As reported by Panda Security, about 230,000 malware samples are created every day by hackers, and the intention to create the malware is always crystal clear. And with the computers, networks, programs, and data centers, it becomes even more problematic to check the malware attacks.

Thankfully, we have Machine Learning technology that aids the multiple layers of protection by automating complex tasks and detecting cyber-attacks on its own. Not only this, but Machine Learning can also be extended to react to cybersecurity breaches and mitigate the damage. It automates responses to cyber-attacks without the need for human intervention.

Going forward, Machine Learning will be used in advanced cyber defense programs to contain and save damage.

 

Faster Computing Power

Industry analysts have started grasping the power of artificial neural networks, and that’s because we all can foresee the algorithmic breakthroughs that will be required for aiding the problem-solving systems. Here, Artificial Intelligence and Machine Learning can address the complex issues that will require explorations and regulating decision-making capacity. And once all of it is deciphered, we can expect to experience ever-blazing computing power.

Enterprises like Intel, Hailo, and Nvidia have already geared up to empower the existing neural network processing via custom hardware chips and explainability of AI algorithms.

Once the businesses figure out the computing capability to run Machine Learning algorithms progressively, we can expect to witness more power centers, who can invest in crafting hardware for data sources along the edge.

 

The Endnote

Without reserve, we can say that Machine Learning is going big day by day, and in 2020, we will be experiencing added applications of this innovative technology. And why not? With Machine Learning, industries can forecast demands and make quick decisions while riding on advanced Machine Learning solutions. Managing complex tasks and maintaining accuracy is the key to business success, and Machine Learning is immaculate in doing the same.

All the trends, as mentioned above of Machine Learning, are quite practical and look promising in imparting unprecedented customer satisfaction. The dynamic dimensions of ever-growing industries further propel the relevance of Machine Learning trends.

Source: Trends in Machine Learning in 2020

Categories
Machine Learning

Review articles of Machine Learning

This is a mostly auto-generated list of review articles on machine learning and artificial intelligence that are on arXiv. Although some of them were written for a specific technical audience or application, the techniques described are nonetheless generally relevant. The list is sorted reverse chronologically by the last updated date of the reviews. It was generated on 2020-02-29. It includes articles mainly from the arXiv categories cs.AI, cs.CL, cs.CV, cs.IR, cs.LG, cs.NE, eess.IV, and stat.ML. A rememberable short link to this page is https://j.mp/ml-reviews. The source code along with raw data for generating this page are linked. Any incorrect or missing entries can be reported as an issue. This page is currently not automatically updated. An auto-updated RSS feed is however available.

Source: Review articles of Machine Learning