Trending February 2024 # Interview Questions On Bagging Algorithms In Machine Learning # Suggested March 2024 # Top 10 Popular

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This article was published as a part of the Data Science Blogathon.

Introduction

Bagging is a famous ensemble technique in the field of machine learning which is widely used for its performance and better results. It is one of the most important and high-performing ensemble techniques, which is easy to use and accurate. Due to the rich performance on even weak machine learning algorithms, It has become a popular ensemble technique and is being compared with other strong machine learning algorithms.

Most machine learning interviews frequently asked interview questions related to bagging algorithms. This article will discuss the top interview questions on bagging, which are mostly asked in machine-learning interviews. Practicing these questions will help one understand the concept of bagging very deeply and help answer the interview questions related to it very efficiently.

1. What is Bagging and How Does it Work? Explain it with Examples.

Bagging stands for Bootstarp Aggregation. Bootstrapping generally means randomly selecting a sample from a dataset, and aggregations stand for the further procedure and preprocessing of the selected samples. So in the bagging, we generally take multiple machine learning models of the same algorithm, meaning that we only take the same machine learning algorithm multiple times.

For example, if we are using SVM as a base algorithm and the count of the models is 5 then all the models will be of SVM only. Once the base model is decided, there will be a bootstrapping process where the random samples from the dataset will be selected and fed to the machine learning model.

The data will be fed to the models by bootstrapping, and there will be separate training for every model. Once all the models are trained, then there will be a prediction phase where all the different models will predict individually, and as a step of aggregation, we can apply any method to the multiple prediction data as there will be 5 different predictions from every model. The common approach is to calculate the mean of the predictions in case of regression or consider the majority count of it in case of classification.

2. How is Bagging Different from the Random Forest Algorithm?

The very basic difference between bagging and the random forest is related to the base models. In bagging, the base model can be any machine learning algorithm, and there is an option of selecting any machine learning algorithm as the base model in bagging by using the base_estimator parameter.

In the random forest, the base estimator or the base models are always decision trees, and other is not any option of selecting any other machine learning algorithms as base estimators in random forest.

Another difference between bagging and the random forest is that in bagging, all the features are selected for the training of the base models, whereas in the random forest, only a subset of the features are selected for the base model training, and out of that only the best performing are chosen as final features.

3. What is the Difference Between Bootstrapping and Pasting in Bagging?

The main difference between bootstrapping and pasting is in the data sampling. As we know, in bagging, there is a sampling of the main dataset, It could be row or column sampling, out of which samples of the dataset are provided to the base models for training.

In bagging or bootstrapping, the samples are taken from the main dataset and fed to the first model, now the same samples can be again used for the training of any other method;, here, the sampling will be with replacement.

In pasting, there is a sample taken from the main dataset, but once the samples are used for training any model, the sawm samples will not be used again for the training of any other model. So here, the sampling is done without replacement.

4. Why does Bagging Performs Well on the Low Bias High Variance Datasets?

In general, low bias high variance datasets are the data that have a very good performance on the training data and poor performance on the testing data, the case of overfitting. The data prone to overfit on any model is preferred for bagging algorithms as bagging reduces the variance of the dataset. Now let’s suppose we have a dataset which is having a very high variance. Suppose we have 10000 rows in our data from which 100 samples have a high variance; now, if this data is fed to any other algorithm, the algorithm will perform poorly as these 100 samples will affect the training, but in the case of bagging, there will be multiple models of the same algorithm, so there will not be a case where all the 100 rows will be fed to the same model due to bootstrapping or sampling of the chúng tôi here now every model will experience the same weightage of the variance in the dataset, and in the end, the high variance of the dataset will not affect the final predictions of the model.

5. What is the Difference between Bagging and Boosting? Which is Better?

In the bagging algorithms, the main dataset is sampled in the parts, and the same multiple base models are used for training with different samples. In the final stage of aggregation, the output from every single base model will be considered, and the final output can be a mean or most frequent term from all models trained. It is also known as parallel learning, as all weak learners learn at the same time. Boosting is generally a stagewise addition method, where multiple weak learners are trained, and all the models are of the same machine learning algorithm. The errors and the mistake from the previously trained weak learner are considered to avoid the same errors in the further training of the next weak learner. It is also known as sequential learning, as the weak learner learns in sequence with each other.

We can not say which algorithm will perform better all the way, but generally, bagging is preferred when there is a low bias and high variance in the dataset (overfitting), whereas boosting is preferred in the case of a high bias and low variance dataset (underfitting).

Conclusion

This article discusses the top 5 interview questions with the core idea and intuition behind them. Reading and preparing these questions will help one understand the bagging algorithm’s core intuition and how it differs from other algorithms.

Some Key Takeaways from this article are:

1. Random forest is a bagging algorithm with decision trees as base models.

2. Bagging uses sampling of the data with replacement, whereas pasting uses sampling of the data without replacement.

3. Bagging performs well on the high variance dataset and boosting performs well on high-bias datasets.

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10 Basic Interview Questions With Answers On Linux Networking

Linux is a widely used operating system, and networking is a crucial aspect of it. The ability to understand and troubleshoot Linux networking is a valuable skill for any IT professional. In this article, we will cover some basic interview questions on Linux networking, along with their answers and examples.

What is the purpose of the ifconfig command, and how is it used?

The ifconfig command is used to configure and manage network interfaces on Linux. It can be used to view the current network configuration, assign IP addresses, configure network interfaces, and set other network-related parameters. Here is an example of how to use ifconfig −

$ ifconfig eth0

This command will display the current configuration of the eth0 interface, including its IP address, netmask, and other details.

How do you check the routing table on a Linux system?

The routing table is used to determine the best path for network traffic to take. To check the routing table on a Linux system, use the following command −

$ netstat -r

This command will display the routing table, including the destination network, netmask, gateway, and other information.

How do you assign a static IP address to a network interface in Linux?

To assign a static IP address to a network interface in Linux, you will need to edit the network configuration file. The location of this file may vary depending on your distribution, but it is usually located in the /etc/network/interfaces directory. Here is an example of how to assign a static IP address to the eth0 interface −

$ sudo vi /etc/network/interfaces # The primary network interface auto eth0 iface eth0 inet static address 192.168.1.100 netmask 255.255.255.0 gateway 192.168.1.1

In this example, we have assigned the IP address 192.168.1.100 to the eth0 interface, along with the netmask and gateway.

How do you configure a Linux system as a router?

To configure a Linux system as a router, you will need to enable IP forwarding and configure NAT (Network Address Translation). IP forwarding allows the Linux system to forward packets between networks, while NAT allows the Linux system to translate private IP addresses to public IP addresses. Here is an example of how to configure a Linux system as a router −

$ sudo sysctl -w net.ipv4.ip_forward=1 $ sudo iptables -t nat -A POSTROUTING -o eth0 -j MASQUERADE

In this example, we have enabled IP forwarding and configured NAT on the eth0 interface.

What is the purpose of the netstat command, and how is it used?

The netstat command is used to display various network-related statistics on a Linux system. It can be used to view active network connections, listening ports, routing tables, and other information. Here is an example of how to use netstat to display active network connections −

This command will display a list of active network connections that are currently in the ESTABLISHED state.

What is the purpose of the route command, and how is it used?

The route command is used to view and modify the kernel’s IP routing table. It can be used to add or delete routes, view the routing table, and set other routing-related parameters. Here is an example of how to use the route command to add a new route −

$ sudo route add -net 192.168.2.0 netmask 255.255.255.0 gw 192.168.1.1

This command will add a new route for the network 192.168.2.0/24 via the gateway 192.168.1.1.

How do you configure a Linux system to use a static IP address for DNS resolution?

To configure a Linux system to use a static IP address for DNS resolution, you will need to edit the chúng tôi file. Here is an example of how to configure a static IP address for DNS resolution −

$ sudo vi /etc/resolv.conf nameserver 192.168.1.1

In this example, we have configured the DNS server to use the IP address 192.168.1.1 for DNS resolution. You can replace this IP address with the IP address of your own DNS server.

How do you configure a Linux system to use a VPN connection?

To configure a Linux system to use a VPN connection, you will need to install a VPN client and configure it with the appropriate settings. OpenVPN is a popular open-source VPN client that can be installed on Linux. Here is an example of how to configure OpenVPN on a Linux system −

$ sudo apt-get install openvpn $ sudo cp chúng tôi /etc/openvpn/ $ sudo vi /etc/openvpn/client.conf

In this example, we have installed the OpenVPN client, copied the sample configuration file to the /etc/openvpn directory, and edited it to include the appropriate settings. You will need to replace the sample configuration settings with your own VPN settings.

How do you configure a Linux system to use a static MAC address for a network interface?

To configure a Linux system to use a static MAC address for a network interface, you will need to edit the network configuration file for that interface. The location of this file may vary depending on your distribution, but it is usually located in the /etc/network/interfaces directory. Here is an example of how to configure a static MAC address for the eth0 interface −

$ sudo vi /etc/network/interfaces auto eth0 iface eth0 inet dhcp hwaddress ether 00:11:22:33:44:55

In this example, we have assigned the MAC address 00:11:22:33:44:55 to the eth0 interface.

How do you configure a Linux system to use a static IP address for a wireless network interface?

To configure a Linux system to use a static IP address for a wireless network interface, you will need to edit the network configuration file for that interface. The location of this file may vary depending on your distribution, but it is usually located in the /etc/network/interfaces directory. Here is an example of how to configure a static IP address for the wlan0 interface −

$ sudo vi /etc/network/interfaces auto wlan0 iface wlan0 inet static address 192.168.1.100 netmask 255.255.255.0 gateway 192.168.1.1 wpa-ssid myssid wpa-psk mypassword

In this example, we have assigned the IP address 192.168.1.100 to the wlan0 interface, along with the netmask, gateway, SSID, and password for the wireless network.

How do you configure a Linux system to use VLAN tagging?

To configure a Linux system to use VLAN tagging, you will need to create a virtual network interface for each VLAN. Here is an example of how to configure VLAN tagging −

$ sudo vconfig add eth0 100 $ sudo vconfig add eth0 200

In this example, we have created two virtual network interfaces for VLAN 100 and 200 on the eth0 physical interface. You will need to configure the virtual network interfaces with the appropriate network settings.

How do you configure a Linux system to use a static route for a specific destination?

To configure a Linux system to use a static route for a specific destination, you will need to use the route command to add the route. Here is an example of how to add a static route −

$ sudo route add -net 192.168.2.0 netmask 255.255.255.0 gw 192.168.1.1

In this example, we have added a static route for the network 192.168.2.0/24 via the gateway 192.168.1.1.

How do you configure a Linux system to use a static DNS server for a specific network interface?

To configure a Linux system to use a static DNS server for a specific network interface, you will need to edit the network configuration file for that interface. The location of this file may vary depending on your distribution, but it is usually located in the /etc/network/interfaces directory. Here is an example of how to configure a static DNS server for the eth0 interface −

$ sudo vi /etc/network/interfaces auto eth0 iface eth0 inet dhcp dns-nameservers 192.168.1.1

In this example, we have configured the eth0 interface to use the DNS server at IP address 192.168.1.1.

A Beginner’s Guide To Deep Learning Algorithms

This article was published as a part of the Data Science Blogathon.

Introduction to Deep Learning Algorithms

The goal of deep learning is to create models that have abstract features. This is accomplished by building models composed of many layers in which higher layers interpret the input while lower layers abstract the details.

As we train these deep learning networks, the high-level information from the input image produces weights that determine how information is interpreted.

These weights are generated by stochastic gradient descent algorithms based on backpropagation for updating the network parameters.

Training large neural networks on big data can take days or weeks, and it may require adjustments for optimal performance, such as adding more memory or computing power.

Sometimes it’s necessary to experiment with multiple architectures such as nonlinear activation functions or different regularization techniques like dropout or batch normalization.

Nearest Neighbor

Clustering algorithms divide a larger set of input into smaller sets so that those sets can be more easily visualized -Nearest Neighbor is one such algorithm because it breaks the input up based on the distance between data points.

For example, if we had an input set containing pictures of animals and cars, the nearest neighbor would break the inputs into two clusters. The nearest cluster would contain images with similar shapes (i.e., animals or cars), and the furthest cluster would contain images with different shapes.

Convolutional Neural Networks (CNN)

Convolutional neural networks are a class of artificial neural networks that employ convolutional layers to extract features from the input. CNNs are frequently used in computer vision because they can process visual data with fewer moving parts, i.e., they’re efficient and run well on computers. In this sense, they fit the problem better than traditional deep learning models. The basic idea is that at each layer, one-dimensionality is dropped out of the input; so for a given pixel, there is a pooling layer for just spatial information, then another for just color channels, then one more for channel-independent filters or higher-level activation functions.

Long Short Term Memory Neural Network (LSTMNN)

Several deep learning algorithms can be combined in many different ways to produce models that satisfy certain properties. Today, we will discuss the Long Short-Term Memory Neural Network (LSTMNN). LSTM networks are great for detecting patterns and have been found to work well in NLP tasks, image recognition, classification, etc. The LSTMNN is a neural network that consists of LSTM cells.

Recurrent Neural Network ( RNN )

An RNN is an artificial neural network that processes data sequentially. In comparison to other neural networks, RNNs can understand arbitrary sequential data better and are better at predicting sequential patterns. The main issue with RNNs is that they require very large amounts of memory, so many are specialized for a single sequence length. They cannot process input sequences in parallel because the hidden state must be saved across time steps. This is because each time step depends on the previous time step, and future time steps cannot be predicted by looking at only one past time step.

Generative Adversarial Networks (GANs) Support Vector Machines (SVM)

One deep learning algorithm is Support Vector Machines (SVM). One of the most famous classification algorithms, SVM, is a numerical technique that uses a set of hyperplanes to separate two or more data classes. In binary classification problems, hyperplanes are generally represented by lines in a two-dimensional plane. Generally, an SVM is trained and used for a particular problem by tuning parameters that govern how much data each support vector will contribute to partitioning the space. The kernel function determines how one feature vector maps into an SVM; it could be linear or nonlinear depending on what is being modeled.

Artificial Neural Networks (ANN)

ANNs are networks that are composed of artificial neurons. The ANN is modeled after the human brain, but there are variations. The type of neuron being used and the type of layers in the network determine the behavior.

ANNs typically involve an input layer, one or more hidden layers, and an output layer. These layers can be stacked on top of each other and side by side. When a new piece of data comes into the input layer, it travels through the next layer, which might be a hidden layer where it does computations before going on to another layer until it reaches the output layer.

The decision-making process involves training an ANN with some set parameters to learn what outputs should come from inputs with various conditions.

Autoencoders Section: Compositional Pattern Producing Networks (CPPN)

Compositional Pattern Producing Networks (CPPN) is a kind of autoencoder, meaning they’re neural networks designed for dimensionality reduction. As their name suggests, CPPNs create patterns from an input set. The patterns created are not just geometric shapes but very creative and organic-looking forms. CPPN Autoencoders can be used in all fields, including image processing, image analysis, and prediction markets.

Conclusion

To summarize, deep learning algorithms are a powerful and complex technology capable of identifying data patterns. They enable us to parse information and recognize trends more efficiently than ever.

Furthermore, they help businesses make more informed decisions with their data. I hope this guide has given you a better understanding of deep learning and why it is important for the future.

There are many deep learning algorithms, but the most popular ones used today are Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN). 

I would recommend taking some time to learn about these two approaches on your own to decide which one might be best for your situation.

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Machine Learning Techniques For Text Representation In Nlp

This article was published as a part of the Data Science Blogathon.

Introduction

Natural Language Processing is a branch of artificial intelligence that deals with human language to make a system able to understand and respond to language. Data being the most important part of any data science project should always be represented in a way that helps easy understanding and modeling, especially when it comes to NLP machine learning. It is said that when we provide very good features to bad models and bad features to well-optimized models then bad models will perform far better than an optimized model. So in this article, we will study how features from text data can be extracted, and used in our NLP machine learning modeling process and why feature extraction from text is a bit difficult compared to other types of data.

Table of Contents

Brief Introduction on Text Representation

Why Feature Extraction from text is difficult?

Common Terms you should know

Techniques for Feature Extraction from text data

One-Hot Encoding

Bag of words Technique

N-Grams

TF-IDF

End Notes

Introduction to Text Representation

The first question arises is what is Feature Extraction from the text?  Feature Extraction is a general term that is also known as a text representation of text vectorization which is a process of converting text into numbers. we call vectorization because when text is converted in numbers it is in vector form.

Now the second question would be Why do we need feature extraction? So we know that machines can only understand numbers and to make machines able to identify language we need to convert it into numeric form.

Why Feature extraction from textual data is difficult? 

If you ask any NLP practitioner or experienced data scientist then the answer will be yes that handling textual data is difficult? Now first let us compare text feature extraction with feature extraction in other types of data. So In an image dataset suppose digit recognition is where you have images of digits and the task is to predict the digit so in this image feature extraction is easy because images are already present in form of numbers(Pixels). If we talk about audio features, suppose emotion prediction from speech recognition so in this we have data in form of waveform signals where features can be extracted over some time Interval. But when I say I have a sentence and want to predict its sentiment How will you represent it in numbers? An image dataset, the speech dataset was the simple case but in a text data case, you have to think a little bit. In this article, we are going to study these techniques only.

Common Terms Used

These are common terms that we will use in further techniques so I want you to be familiar with these four basic terms

Corpus(C) ~ The total number of combinations of words in the whole dataset is known as Corpus. In simple words concatenating all the text records of the dataset forms a corpus.

Vocabulary(V) ~ a total number of distinct words which form your corpus is known as Vocabulary.

Document(D) ~ There are multiple records in a dataset so a single record or review is referred to as a document.

Word(W) ~ Words that are used in a document are known as Word.

Techniques for Feature Extraction 1 One-Hot Encoding

Now to perform all the techniques using python let us get to Jupyter notebook and create a sample dataframe of some sentences.

import numpy as np import pandas as pd df = pd.DataFrame({"text":sentences, "output":[1,1,0]})

Now we can perform one-hot encoding using sklearn pre-built class as well as you can implement it using python. After implementation, each sentence will have a different shape 2-D array as shown in below sample image of one sentence.

1) Sparsity – You can see that only a single sentence creates a vector of n*m size where n is the length of sentence m is a number of unique words in a document and 80 percent of values in a vector is zero.

2) No fixed Size – Each document is of a different length which creates vectors of different sizes and cannot feed to the model.

3) Does not capture semantics – The core idea is we have to convert text into numbers by keeping in mind that the actual meaning of a sentence should be observed in numbers that are not seen in one-hot encoding.

2 Bag Of Words from sklearn.feature_extraction.text import CountVectorizer cv = CountVectorizer() bow = cv.fit_transform(df['text'])

Now to see the vocabulary and the vector it has created you can use the below code as shown in the below results image.

Advantages

1) Simple and intuitive – Only a few lines of code are required to implement the technique.

2) Fix size vector – The problem which we saw in one-hot encoding where we are unable to feed data the data to machine learning model because each sentence forms a different size vector but here It ignores the new words and takes only words which are vocabulary so creates a vector of fix size.

2) Sparsity – when we have a large vocabulary, and the document contains a few repeated terms then it creates a sparse array.

3) Not considering ordering is an issue – It is difficult to estimate the semantics of the document.

3 N-Grams

The technique is similar to Bag of words. All the techniques till now we have read it is made up of a single word and we are not able to use them or utilize them for better understanding. So N-Gram technique solves this problem and constructs vocabulary with multiple words. When we built an N-gram technique we need to define like we want bigram, trigram, etc. So when you define N-gram and if it is not possible then it will throw an error. In our case, we cannot build after a 4 or 5-gram model. Let us try bigram and observe the outputs.

#Bigram model from sklearn.feature_extraction.text import CountVectorizer cv = CountVectorizer(ngram_range=[2,2]) bow = cv.fit_transform(df['text'])

You can try trigram with a range like [3,3] and try with N range so you get more clarification over the technique and try to transform a new document and observe how does it perform.

Advantages

1) Able to capture semantic meaning of the sentence – As we use Bigram or trigram then it takes a sequence of sentences which makes it easy for finding the word relationship.

2) Intuitive and easy to implement – implementation of N-Gram is straightforward with a little bit of modification in Bag of words.

1) As we move from unigram to N-Gram then dimension of vector formation or vocabulary increases due to which it takes a little bit more time in computation and prediction

2) no solution for out of vocabulary terms – we do not have a way another than ignoring the new words in a new sentence.

4 TF-IDF (Term Frequency and Inverse Document Frequency)

Now the technique which we will study does not work in the same way as the above techniques. This technique gives different values(weightage) to each word in a document. The core idea of assigning weightage is the word that appears multiple time in a document but has a rare appearance in corpus then it is very important for that document so it gives more weightage to that word. This weightage is calculated by two terms known as TF and IDF. So for finding the weightage of any word we find TF and IDF and multiply both the terms.

Term Frequency(TF) – The number of occurrences of a word in a document divided by a total number of terms in a document is referred to as Term Frequency. For example, I have to find the Term frequency of people in the below sentence then it will be 1/5. It says how frequently a particular word occurs in a particular document.

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Inverse Document Frequency – Total number of documents in corpus divided by the total number of documents with term T in them and taking the log of a complete fraction is inverse document frequency. If we have a word that comes in all documents then the resultant output of the log is zero But in implementation sklearn uses a little bit different implementation because if it becomes zero then the contribution of the word is ignored so they add one in the resultant and because of which you can observe the values of TFIDF a bit high. If a word comes only a single time then IDF will be higher.

from sklearn.feature_extraction.text import TfidfVectorizer tfidf = TfidfVectorizer() tfidf.fit_transform(df['text']).toarray()

So one term keeps track of how frequently the term occurs while the other keeps track of how rarely the term occurs.

End Notes

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Data Scientist Vs Machine Learning

Differences Between Data Scientist vs Machine Learning

Hadoop, Data Science, Statistics & others

Data Scientist

Standard tasks:

Allocate, aggregate, and synthesize data from various structured and unstructured sources.

Explore, develop, and apply intelligent learning to real-world data and provide essential findings and successful actions based on them.

Analyze and provide data collected in the organization.

Design and build new processes for modeling, data mining, and implementation.

Develop prototypes, algorithms, predictive models, and prototypes.

Carry out requests for data analysis and communicate their findings and decisions.

In addition, there are more specific tasks depending on the domain in which the employer works, or the project is being implemented.

Machine Learning

The Machine Learning Engineer position is more “technical.” ML Engineer has more in common with classical Software Engineering than Data Scientists. It helps you learn the objective function, which plots the inputs to the target variable and independent variables to the dependent variables.

The standard tasks of ML Engineers are generally like Data scientists. You also need to be able to work with data, experiment with various Machine Learning algorithms that will solve the task, and create prototypes and ready-made solutions.

Strong programming skills in one or more popular languages (usually Python and Java) and databases.

Less emphasis on the ability to work in data analysis environments but more emphasis on Machine Learning algorithms.

R and Python for modeling are preferable to Matlab, SPSS, and SAS.

Ability to use ready-made libraries for various stacks in the application, for example, Mahout, Lucene for Java, and NumPy / SciPy for Python.

Ability to create distributed applications using Hadoop and other solutions.

As you can see, the position of ML Engineer (or narrower) requires more knowledge in Software Engineering and, accordingly, is well suited for experienced developers. The case often works when the usual developer must solve the ML task for his duty, and he starts to understand the necessary algorithms and libraries.

Head-to-Head Comparison Between Data Scientist and Machine Learning (Infographics)

Below are the top 5 differences between Data scientists and Machine Learning:

Key Difference Between Data Scientist and Machine Learning

Below are the lists of points that describe the key Differences Between Data Scientist and Machine Learning:

Machine learning and statistics are part of data science. The word learning in machine learning means that the algorithms depend on data used as a training set to fine-tune some model or algorithm parameters. This encompasses many techniques, such as regression, naive Bayes, or supervised clustering. But not all styles fit in this category. For instance, unsupervised clustering – a statistical and data science technique – aims at detecting clusters and cluster structures without any a-prior knowledge or training set to help the classification algorithm. A human being is needed to label the clusters found. Some techniques are hybrid, such as semi-supervised classification. Some pattern detection or density estimation techniques fit into this category.

Data science is much more than machine learning, though. Data in data science may or may not come from a machine or mechanical process (survey data could be manually collected, and clinical trials involve a specific type of small data), and it might have nothing to do with learning, as I have just discussed. But the main difference is that data science covers the whole spectrum of data processing, not just the algorithmic or statistical aspects. Data science also covers data integration, distributed architecture, automated machine learning, data visualization, dashboards, and Big data engineering.

Data Scientist and Machine Learning Comparison Table

Feature Data Scientist Machine Learning

Data It mainly focuses on extracting details of data in tabular or images. It mainly focuses on algorithms, polynomial structures, and word adding.

Complexity It handles unstructured data, and it works with a scheduler. It uses Algorithms and mathematical concepts, statistics, and spatial analysis.

Hardware Requirement Systems are Horizontally scalable and have High Disk and RAM storage. It requires Graphic processors and Tensor Processors, that is very high-level hardware.

Skills Data Profiling, ETL, NoSQL, Reporting. Python, R, Maths, Stats, SQL Model.

Focus Focuses on abilities to handle the data. Algorithms are used to gain knowledge from huge amounts of data.

Conclusion

Machine learning helps you learn the objective function, which plots the inputs to the target variable and independent variables to the dependent variables.

A Data scientist does a lot of data exploration and arrives at a broad strategy for tackling it. He is responsible for asking questions about the data and finding what answers one can reasonably draw from the data. Feature engineering belongs to the realm of Data scientists. Creativity also plays a role here, and An Machine Learning engineer knows more tools and can build models given a set of features and data – as per directions from the Data Scientist. The realm of Data preprocessing and feature extraction belongs to ML engineers.

Data science and examination utilize machine learning for this archetypal validation and creation. It is vital to note that all the algorithms in this model creation may not come from machine learning. They can arrive from numerous other fields. The model desires to be kept relevant always. If the situations change, the model we created earlier may become immaterial. The model must be checked for certainty at different times and adapted if its confidence reduces.

Data science is a whole extensive domain. If we try to put it in a pipeline, it would have data acquisition, data storage, data preprocessing or cleaning, learning patterns in data (via machine learning), and using knowledge for predictions. This is one way to understand how machine learning fits into data science.

Recommended Articles

This is a guide to Data Scientist vs Machine Learning. Here we have discussed Data Scientist vs Machine Learning head-to-head comparison, key differences, infographics, and comparison table. You may also look at the following articles to learn more –

How Machine Learning Improves Cybersecurity?

Here is how machine learning improves cybersecurity

Today, deploying robust cybersecurity solutions is unfeasible without significantly depending on machine learning. Simultaneously, without a thorough, rich, and full approach to the data set, it is difficult to properly use machine learning. MI can be used by cybersecurity systems to recognise patterns and learn from them in order to detect and prevent repeated attacks and adjust to different behaviour. It can assist cybersecurity teams in being more proactive in preventing dangers and responding to live attacks. It can help businesses use their assets more strategically by reducing the amount of time invested in mundane tasks.  

Machine Learning in Cyber Security

ML may be used in different areas within Cyber Security to improve security procedures and make it simpler for security analysts to swiftly discover, prioritise, cope with, and remediate new threats in order to better comprehend previous cyber-attacks and build appropriate defence measures.  

Automating Tasks

The potential of machine learning in cyber security to simplify repetitive and time-consuming processes like triaging intelligence, malware detection, network log analysis, and vulnerability analysis is a significant benefit. By adding machine learning into the security workflow, businesses may complete activities quicker and respond to and remediate risks at a rate that would be impossible to do with only manual human capabilities. By automating repetitive operations, customers may simply scale up or down without changing the number of people required, lowering expenses. AutoML is a term used to describe the process of using machine learning to automate activities. When repetitive processes in development are automated to help analysts, data scientists, and developers be more productive, this is referred to as AutoML.  

Threat Detection and Classification

In order to identify and respond to threats, machine learning techniques are employed in applications. This may be accomplished by analysing large data sets of security events and finding harmful behaviour patterns. When comparable occurrences are recognised, ML works to autonomously deal with them using the trained ML model. For example, utilising Indicators of Compromise, a database to feed a machine learning model may be constructed (IOCs). These can aid in real-time monitoring, identification, and response to threats. Malware activity may be classified using ML classification algorithms and IOC data sets. A study by Darktrace, a Machine Learning based Enterprise Immune Solution, alleges to have stopped assaults during the WannaCry ransomware outbreak as an example of such an application.  

Phishing

Traditional phishing detection algorithms aren’t fast enough or accurate enough to identify and distinguish between innocent and malicious URLs. Predictive URL categorization methods based on the latest machine learning algorithms can detect trends that signal fraudulent emails. To accomplish so, the models are trained on characteristics such as email headers, body data, punctuation patterns, and more in order to categorise and distinguish the harmful from the benign.  

WebShell

WebShell is a malicious block of software that is put into a website and allows users to make changes to the server’s web root folder. As a result, attackers have access to the database. As a result, the bad actor is able to acquire personal details. A regular shopping cart behaviour may be recognised using machine learning, and the system can be programmed to distinguish between normal and malicious behaviour.  

Network Risk Scoring

Quantitative methods for assigning risk rankings to network segments aid organisations in prioritising resources. ML may be used to examine prior cyber-attack datasets and discover which network regions were more frequently targeted in certain assaults. With regard to a specific network region, this score can assist assess the chance and effect of an attack. As a result, organisations are less likely to be targets of future assaults. When doing company profiling, you must determine which areas, if compromised, can ruin your company. It might be a CRM system, accounting software, or a sales system. It’s all about determining which areas of your business are the most vulnerable. If, for example, HR suffers a setback, your firm may have a low-risk rating. However, if your oil trading system goes down, your entire industry may go down with it. Every business has its own approach to security. And once you grasp the intricacies of a company, you’ll know what to safeguard. And if a hack occurs, you’ll know what to prioritise.  

Human Interaction

Computers, as we all know, are excellent at solving complex problems and automating things that people might accomplish, but which PCs excel at. Although AI is primarily concerned with computers, people are required to make educated judgments and receive orders. As a result, we may conclude that people cannot be replaced by machines. Machine learning algorithms are excellent at interpreting spoken language and recognising faces, but they still require people in the end.  

Conclusion

Machine learning is a powerful technology. However, it is not a magic bullet. It’s crucial to remember that, while technology is improving and AI and machine learning are progressing at a rapid pace, technology is only as powerful as the brains of the analysts who manage and use it.

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