Friday, April 23, 2021

7 Trending WordPress Design and Development Tactics in 2021

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WordPress Design Tactics


It’s no secret that WordPress is the most popular content management system (CMS) right now. As of 2021, WordPress powers nearly 75 million websites on the Internet, which is around 34% of the entire web. There are plenty of reasons why developers choose WordPress to build websites. 

 

The CMS offers thousands of built-in themes, plugins, and add-ons that make the entire development process a hassle-free task. Not to mention, WordPress has a user-friendly interface, which means even non-developers can use it to set up their website with minimum effort. 

 

But, despite all these cool features, even WordPress is vulnerable to unexpected errors and failures. Since the platform is open-source, the websites are always exposed to potential hackers. Anyone can release a theme or plugin and run their custom scripts to steal data from your website. 

 

That’s the reason why it’s always a good strategy to hire a professional WordPress website design company. Such agencies have experienced WordPress developers who have years of experience in the field. They’ll implement the best security encryption and designing practices to ensure that your website can engage the users while protecting their personal information at the same time. 

 

And, in case if you’re planning to execute DIY development, it would be better to utilize the right practices to build an engaging website. In this guide, we are going to share some of the most effective design & development tactics that’ll help you build a feature-rich website for your customers. 

 

Install Up-to-Date Themes, Plugins, and Add-Ons

Every WordPress website incorporates different plugins, add-ons, and a general theme to enhance usability. However, if you use outdated themes or plugins, they’ll most likely hinder your website’s performance. 

 

Outdated themes usually contain malware that will not only put your website’s security at risk but also damage the overall functionality. This will damage customer engagement and you’ll struggle to retain the website visitors. So, if you already have a WordPress website, make sure to update its theme, plugins, and all the add-ons to the latest version. 

 

And, in case you’re yet to design the website, try to choose a theme with positive reviews and ensure that it receives regular updates. These initial efforts will help you avoid any major consequences in the future. 

 

Optimize Your Website for Mobile Users

Mobile phones have become an essential tool of one’s everyday life. More and more users have started to rely on mobile to access different websites. This is a major reason why a large portion of Internet traffic comes from mobile itself. If you want to reach your potential buyers in 2021, it’ll be imperative to make your website mobile-friendly. 

 

When it comes to WordPress, designing a mobile-friendly website is easier than you think. All you need to do is choose a responsive theme from the WordPress Theme Store. For people who don’t know, responsive themes allow a website to automatically adjust the layout as per the user’s screen size. This will ensure that your website delivers a consistent user experience across all devices. 

 

Implement SSL Certification

This is probably the most crucial WordPress development tactic that’ll help you secure your website from hackers. Even if you’re going to design a single-page website, it would be a wise decision to add an SSL certificate. Moreover, users have become pretty smart these days and they are always going to choose secure websites over random domains on the Internet. 

 

Adding an SSL certificate is a cakewalk, especially if you’re working with a team of professional web developers in Miami. You can also choose to add the SSL certification while choosing a hosting plan. The majority of hosting providers, including Bluehost and Hostgator, offer free SSL certificates with even the basic hosting plans. 

 

Utilize Test Environment

Test environments are designed to test a website under different real-life scenarios to check how it’ll perform in the real world. However, these test environments can be a great tool to test new changes to your website without damaging the performance at all. 

 

With a test environment, you can test different updates before making them live. This will help you evaluate all the potential bugs beforehand and avoid downtime in the future.  

 

Try to Use Child Themes As Much As Possible

If you’re an experienced developer, you’ll most likely make changes to the core theme and plugins to offer a personalized experience to visitors. While this is a great practice to enhance a website’s usability, it’s also one of the biggest mistakes you can make while managing a WordPress website. Why? Because any changes that you make to a theme will be removed automatically as soon as you update it. 

 

To avoid such scenarios, it’s always advised to create child themes for a core theme. This will help you preserve all the changes that you make, even if you decide to update themes to the latest version. 

 

Use Caching to Optimize Website Speed

Website’s loading speed is one of the most critical factors Google uses to rank different websites in its search results. Basically, if your website takes too long to load, you won’t be able to get ahead of your competitors and gain the expected online exposure. 

 

You can use WordPress caching to ensure that your website loads quickly and delivers a remarkable performance. If you’re not familiar with the concept, WordPress caching is the practice to reuse database requests to speed up the entire website. With some of the most popular hosting providers, you’ll get a default feature to automatically enable WordPress caching. 

 

So, check your hosting plan and see if it supports WordPress caching or not. In case it doesn’t, you can outsource the task to a professional WordPress website design company. 

 

Use Limited Plugins

As tempting as the idea of installing different plugins sounds, keep in mind that adding more plugins will only make your website bulkier. Even though the WordPress store is filled with different plugins, you don’t need them all. 

 

Simply analyze your website requirements and then choose the most appropriate plugins accordingly. This will help you bridge the gap between better functionality and fast loading speed and you’ll be able to deliver a consistent browsing experience to the customers. 

 

Conclusion

So, these are some of the most helpful WordPress design & development tactics that’ll help you design fully functional websites in 2021. Implement these tips and your website will perform remarkably in terms of user engagement. However, if you’re new to the entire WordPress community, consult professional WordPress developers before making any changes to the website. 


About Author Bio

You’ve made it! Welcome to Unique Web Designer. We are extremely passionate about what we do and have always put our clients first. We have a lust for life and a passion for creation. We look forward to the opportunity to work with you.



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How to Use LinkedIn Advertising for Small Businesses?

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LinkedIn Advertising


Everyone is now going social. If there’s one thing that many people can’t do without even for a day, it would be engaging in various social media activities. Social media networks used to be a platform where people connect with their friends and loved ones. These days, however, social media is no longer just a platform for socialization but also a great venue for entrepreneurs to market their business, boost their reach, improve brand engagement, increase sales, and eventually grow their business. One of the social media networks that are gaining popularity among businesses nowadays is LinkedIn. 


LinkedIn is generally geared toward professionals, career-driven individuals, and renowned businesses. This makes it an amazing platform for networking, connecting with joint venture partners, growing an email marketing list, improving brand visibility, and cultivating small businesses. 


If you haven’t started using LinkedIn for your business yet, it is never too late for you to take action. Sign up now and experience what LinkedIn has on the table for you. However, if you are already using LinkedIn but have not utilized all of its exciting features yet, this blog is perfect for you. Today, we will talk about how you can gain exposure and inexpensively grow your small business. 


Just like Facebook, you can also market your business on LinkedIn through LinkedIn advertising. I will walk you through the important steps you need to take when using LinkedIn Advertising for your small business. 

Set up your LinkedIn Ads account

Before you can use the all-in-one advertising platform of Linkedin, you need to set up your LinkedIn Ads account first. We will break down this process into 8 main steps.


Create your company’s LinkedIn page: Start by creating your company’s LinkedIn page. Creating a LinkedIn page is quite simple, quick, and straightforward. You just have to provide the necessary information and follow the instructions on the screen. If you already have an existing LinkedIn page for your business, you may use it and proceed to the next step. You will need a LinkedIn account before you can build your company page on the platform.


Be an admin to your company’s LinkedIn page: Becoming an admin to your company’s LinkedIn page allows you access to all admin features and privileges. This includes permission to create, edit, and delete ads. You can also opt to use the “Direct Sponsored Content Poster” access feature. This feature won’t grant you admin access but it will allow you to create, edit, and delete ads.


Create a LinkedIn Ads account: The next thing you need to do is to create a LinkedIn Ads account. This will give you access to the LinkedIn Ads feature of LinkedIn. After creating a LinkedIn Ads account, you will be logged into an ad platform called “Campaign Manager”. You will need a LinkedIn account to run and manage ad campaigns and control your Ad budget. 


Customize your LinkedIn Ads account: To maximize the potential of LinkedIn Ads features, you have to customize your account and ensure that everything is in line with your business goals, targets, and values. Customizing a LinkedIn Ads account typically includes setting up conversion tracking, integrating Lead Gen forms, creating retargeting audiences, and adding company information like email and company lists. 

Install the LinkedIn insight tag

Installing the LinkedIn insight tag on your website and web pages will allow you to keep track of the success of your ad campaign. The LinkedIn insight tag will provide you access to features like retargeting, conversion tracking, and free web demographics. It enables you to see critical information like who is viewing your offer and clicking through your ad campaign.

Determine your company’s objectives

Having a clear set of objectives is critical to the success of your ad campaigns. Do you want to boost your online presence, promote brand awareness, or improve engagements? Perhaps, you want to focus on lead generation. Regardless of what you intend to achieve you have to clearly determine your objectives. This will help you streamline and customize a successful ad campaign. 

Pick self-service or managed campaigns

LinkedIn offers small businesses a quick and easy way to utilize a self-service campaign. Simply get into the Campaign Manager hub to configure and run ad campaigns. You can also use the Campaign Manager hub to check your ads’ performance, monitor their progress, and find detailed information on other advertising activities.

Decide what Ad format you will use

LinkedIn allows you to choose either Sponsored Content, Text Ads, or both. Before creating an ad, you will have to decide first what format you will use.


Sponsored Content feels and looks native to the platform and appears naturally in the member’s feeds. Ads in Sponsored Content format look like a typical post with a headline, image, and sponsor’s link. They are displayed in the company’s own feed as “boosted” posts. If you opt to choose this format, it is best to use your company page’s top-performing posts as they are most likely to attract more audience.

 

Text Ads appear on the top, bottom, or side of the member’s feed. This format may be simple but when created wisely, it can be very effective. For best results, use dynamic visuals and add a short yet concise and compelling copy.  

Create and run your ad campaigns

Now that you have everything ready, you can now start creating and running a successful ad campaign. You have to set your target audience, though, to make sure that your ads are shown only to your prospective customers. You also have to set your ad campaign budget so you won’t end up spending too little or too much of you are willing to invest for your ads. 


And lastly, optimize your LinkedIn ad campaign and promote it across various social media platforms for better results.


ABOUT THE AUTHOR


Ankit Patel is a Sales/Marketing Manager at XongoLab Technologies LLP & PeppyOcean. As a hobby, He loves to write articles about technology, business, and marketing. His articles featured on Datafloq, JaxEnter, TechTarget, eLearninggAdobe, DesignWebKit, InstantShift and many more.

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Sunday, April 11, 2021

3 Common Problems with the Kindle and How to Fix Them

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Kindle Fire

The Kindle from Amazon is quite a success when it comes to the world of tablets and smartphones. When the original kindle made its debut, there were numerous upgrades coming on various models of Kindle. The kindles are in various shapes in sizes for various kinds of audiences. 

 

Sometimes when you are using the kindle, there are some unexpected errors that you can face. Among those errors, there are many such as the Kindle Won’t connect to Wi-Fi, the kindle is frozen, the kindle is not working, and so on. 

 

These are some of the common issues that you can face and it happens from time to time. Today we are going to discuss some of the potential solutions for these common problems.

3 common problems with the Kindle and how to fix them

1. Kindle won’t charge

Sometimes there are many battery-related issues that you will face in your kindle. There are some issues where there will be a complete resistance to charge to the partial charge to a very slow charge. There are a few things that you must double-check before you are trying to contact amazon experts. 

 

Make sure that you are charging the kindle device with the original amazon cable. There is a charger that is the option that will take 4 hours, but if you are charging and using the kindle, then it is going to take more than 10 hours. 

 

There is more than you can read in Amazon’s forum. Well, in order to resolve these issues, there are some steps that you can follow. One of the most common reasons for this issue is a loose or broken charging port. 

 

One of the ways through which you will be able to get the micro USB cable. This will come with a magnetic detachable connector that has been designed to stay in the port. This is going to prevent the repeated wear and tear. 

 

You can easily buy such cables online. This might help you resolve the Kindle won't charge. 

 

If you see that the charger is still not working, then you need to press the power button for at least 20 seconds in order to power off. After that, you can try to plug in the charger. You need to make sure that you are using the cable that you got with the kindle fire

2. Kindle Won’t connect to Wi-Fi

 

There are some reasons which are why the Kindle Fire HD or HDX is not going to connect to the network. If you see that you are not connecting to the network, then there are a couple of things that you can try. 

 

You need to make sure that your kindle device is not in Airplane mode. Make sure that the Wi-Fi is on in the notifications bar. 

                                                                                               

Let us see some of the possible solutions to this issue:

  • The first thing that you can try is to hold the power button for at least a second and when you see the shutdown option, then you need to press it down. Then turn the kindle as well as the router on. 
  • Make sure that you also try to reboot the device by holding the power button for at least 30 seconds. Then press the power button in order to turn it on. You can even hold the power button for some time until you see that your device is rebooting. 
  • Before you are going to start, make sure that your kindle fire HD is fully charged. make sure that you have backed-up all your precious files as well so that you do not lose anything important. 
  • Make sure that you also down the Wi-Fi analyzer app so that the busy channels are not your problem. These are also going to reveal the signal strength issues. 

Follow these steps that you can try to resolve the kindle won’t connect to the WiFi issue. 

3. Kindle is frozen

 

You will see that a kindle might freeze up due to a variety of reasons. This can happen with any device that has a memory. Well, when you face this issue, then you can try to restart the kindle. It is just going to take a matter of minutes and this is going to easily resolve the issue.

 

Make sure that you are keeping your kindle from running on a low battery and make sure that you update your device regularly. Sometimes charging the kindle or resetting it is also going to resolve the issue of Kindle frozen.

 

Well, these are the 3 common problems with the Kindle and how to fix them. Well, there are at times when you will not be able to resolve the issues. At that time, you can get in touch with the official experts. Who can help you resolve the issue? 


Note: Please reach out to the official support here: amzn.to/3ds1OPu, for assistance.







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Friday, April 9, 2021

The Ultimate Guide to Machine Learning Algorithms

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Machine learning

As we can see, in the present world, almost all manual tasks are being done automatically, and so the formal definition of manual doing has changed. We live in an era of experiencing enormous technological progress, and now we can easily predict the future days with how computing has advanced over the years.

 

One of the best scenarios where automatic tasks come into play is implementing machine learning algorithms that help computers play chess/ludo. Perform robotic surgeries, predict prices for the future, and get smarter in many other ways on behalf of us. The results have been astonishing.

In this particular article, we will have an overview of the below-mentioned topics:

  • The definition of what machine learning algorithms
  • Few Pros and Cons of Machine Learning Algorithms
  • The three types of Machine Learning Algorithms
  • Top 10 Machine Learning Algorithms
  • When to use which Machine Learning Algorithms and their various Use Cases
  • The conclusion

Definition

The precise definition of machine learning states that machine learning is a computer program that keeps on learning from some experience 'E' concerning some task 'T' and some performance measure 'P,' if its performance on T as measured by P, and it improves with 'E.' 


It is in the field of search engines, detection of credit card fraud, stock market prediction, data mining, image prediction, computer vision, sentiment analysis, natural language processing, biometrics, robotic surgeries, medical diagnostics, securities market analysis, DNA sequence sequencing, music generation, speech and handwriting recognition, strategy games and a lot to add on the list.


So, what do we mean by Machine Learning Algorithms?


Machine Learning Algorithms


In simple words, we can say Machine learning algorithms are fundamental programs that include both mathematics and logic. They tend to adjust themselves to perform better when they get exposed to huge volumes of data. The machines learn as they process more and more data over time improve from experience without being explicitly programmed.

These algorithms use a definite procedure and have both advantages and disadvantages in their implementation for model building. Let us check a few among those:

Pros and Cons

Advantages of Machine Learning Algorithms

Advantages of Machine Learning
  • Machine Learning algorithms are best suited when handling large volumes of data is needed as they are multidimensional and multi-variety as they can do that in dynamic or uncertain environments.
  • Machine Learning Algorithms can work with large volumes of datasets and discover specific trends and patterns that are not apparent to humans. 

For instance, websites like Amazon understand the browsing behavior of customers. And start recommending the right products, deals, and reminders relevant to them.

  • As we have already discussed, ML algorithms do not involve any human intervention. It gives the machines the ability to learn, make predictions, and improve on their own. An example of this is the antivirus software, which learns by itself to filter out new threats, the moment they get recognized. Moreover, ML algorithms are good at identifying spam.

Disadvantages of Machine Learning Algorithms

Disadvantages of Machine Learning Algorithms

  • One of ML algorithms' prime disadvantages is that the algorithms need enough time to learn from themselves. And develop enough logic to fulfill their purpose with a considerable amount of accuracy and relevancy and the requirement of massive resources to function.
  • No doubt, these algorithms are autonomous, but they are highly susceptible to errors when the algorithm gets trained with small data sets. We end up with biased predictions that come from the slanted training set. And it finally leads to irrelevant results getting portrayed to customers.
  • Another major drawback occurs when we cannot choose the proper algorithm for our purpose. The ability to accurately interpret the results generated by the algorithms is crucial.

By now, we got an idea of what a machine learning algorithm is and its pros and cons. Now, let us try to understand its types and their subcategories.

Classification of Machine Learning Algorithms

Classification of Machine Learning Algorithms


Broadly, Machine Learning Algorithms classified into three categories which described briefly below:

  1. Supervised Learning:
Supervised Learning

The first algorithm is known as supervised learning which mainly consists of a target/outcome variable. And it has to be predicted from a given set of predictors or independent variables. It is the task where the machine learning algorithm learns a function that maps an input to output and infers a process from labeled training data based on input-output pairs. 

Some examples of Supervised Learning Algorithms include the KNN algorithm, Decision Tree, Bayesian Classification, Logistic Regression, Random Forest, etc. One real-life example of supervised learning is predicting house prices or maybe image classification.


We have seen the main idea in supervised learning is to learn under supervision. But, now we will move into an unsupervised learning algorithm where we lack this kind of signal.

  1. Unsupervised Learning:

Unsupervised Learning

We do not have any data points label, but we are not totally in the black clouds. We do have the actual data points, and it helps us draw references from observations in the input data to find out meaningful structure and pattern in the remarks. It is used for clustering populations in different segments for specific interventions. 

 

Real-world examples of Unsupervised Learning Algorithms include K-means clustering, Component Analysis, Apriori algorithm. Feature Selection, Dimensionality reduction, or finding customer segments commonly use unsupervised learning techniques. 

  1. Reinforcement Learning:


In this type of algorithm, the machine is trained in such a way that it makes specific decisions. Its working principle is for the device to get exposed to an environment where it continuously trains itself using trial and error methods. And the machine keeps learning from its experience and tries to capture the best possible knowledge to form accurate business decisions. An example of reinforcement learning is Markov Decision Process.

Machine Learning Algorithms

Based on the above three techniques, the classification of ML Algorithms is in the following manner:

Regression algorithm

  • Linear Regression
  • Logistic Regression
  • Multiple Adaptive Regression
  • Local scatter smoothing estimate

Decision tree algorithm

  • Classification and Regression Tree
  • ID3 algorithm
  • C4.5 and C5.0
  • CHAID
  • Random Forest
  • Multivariate Adaptive Regression Spline
  • Gradient Boosting Machine

Bayesian algorithm

  • Naive Bayes
  • Gaussian Bayes
  • Polynomial naive Bayes

Instance-based learning algorithm

  • K- proximity algorithm
  • Self-Organizing Mapping Algorithm
  • Learning vectorization
  • Local Weighted Learning Algorithm

 Regularization algorithm

  • Ridge Regression
  • LASSO Regression
  • Elastic Net
  • Minimum Angle Regression

Integrated algorithm

  • Boosting
  • Bagging
  • AdaBoost
  • Stack generalization (mixed)
  • GBM algorithm
  • GBRT algorithm
  • Random forest

Kernel-based algorithm

  • Support vector machine (SVM)
  • Linear Discriminant Analysis (LDA)
  • Radial Basis Function (RBF)

Clustering Algorithm

  • K - mean
  • K - medium number
  • EM algorithm
  • Hierarchical clustering

Dimensionality reduction algorithm

  • Principal Component Analysis (PCA)
  • Principal component regression (PCR)
  • Partial least squares regression (PLSR)
  • Salmon map
  • Multidimensional scaling analysis (MDS)
  • Projection pursuit method (PP)
  • Linear Discriminant Analysis (LDA)
  • Mixed Discriminant Analysis (MDA)
  • Quadratic Discriminant Analysis (QDA)
  • Flexible Discriminant Analysis (FDA)


Now, among all these algorithms which gave rise to Machine Learning Algorithms, we shall be discussing the ten most commonly used algorithms in details below:

 

  1. Linear Regression

Linear Regression

Francis Galton is responsible for discovering the linear regression model. 

 

Firstly, he analyzed the heights of father and son; and came out with the best fit line or using regression line techniques to find all people's mean size.

 

In technical terms, ML engineers define linear regression as the modeling approach to find relationships between one or more independent variables (predictor.) And denoted it as X, and the dependent variable (target) indicates as Y. By fitting them to a line which is known as the regression line.

 

By representing as a linear equation Y= a * X + b where Y – Dependent Variable, a – Slope, X – Independent variable, b – Intercept. a and b are the coefficients which they derived by minimizing the sum of the squared difference of distance between the data points and the regression line.

 

For instance, predicting the sales of Ice cream based on temperature is done with helping Linear Regression.

 

Some use cases of linear regression include:

 

  • Price predicting, performance, and risk parameters based on the sales of a product.
  • Generating insights on various consumer profitability, behavior, or some other business factors
  • Evaluating the present trends to make estimates and forecasts
  • Determining the price and promotions on sales of a product for marketing effectiveness
  • Assessing risk in the financial services and insurance domain
  • Widely used for astronomical data analysis

 

  1. Logistic Regression

Logistic Regression



Logistic regression is a classification algorithm that data ML engineers use to predict categorical values. It is usually in binary forms as 0/1, true/false, right/wrong) within the set of independent variables. It is also called a sigmoid curve or logit regression. 

 

It helps to improve logistic regression models such as: eliminate features, regularizing techniques. It includes interaction terms and finally using a non-linear model.

 

An example is predicting if a person will buy an SUV based on their age and estimated salary.

Some use cases of Logistic Regression includes:

  • Making predictive models for credit scoring
  • Used for claiming claim about a text fragment in the text editing
  • Speed is one of the best advantages of logistic regression, and so this feature is quite beneficial in the gaming industry.

 

  1. Decision Trees


Decision Trees

Decision Trees are the most popular used machine learning algorithms widely used for classification and regression problems. It represents a tree where each node represents a feature or attribute, each branch represents a decision, and each leaf represents an outcome. It utilizes the if-then rules, which are both exhaustive and exclusive in classification.

 

Considering an example is where the person should accept a new job offer or not. Or does Kyphosis exist in surgery?

 

Some use cases of the Decision Tree Algorithm includes:

 

  • Building knowledge management platforms for customer service by improving resolution, customer satisfaction rules, and average handling time
  • Forecasting the future outcomes and assigning probabilities in finance sectors
  • Loan approval decision-making
  • Price prediction and real-time options analysis

 

  1. SVM (Support Vector Machine)


SVM (Support Vector Machine)

Support Vector Machine, shortly SVM, is a classification method that represents the training data as points. That we plot in an n-dimensional space (n denoting the number of features we have)separated into categories by a niche as broad as possible. Classifiers are those lines that we use to split the data and plot those on a graph. New points then get added to space by predicting which category they fall under and which room they belong in the algorithms.

 

Some everyday use cases of SVM includes-

 

  • SVM can classify parts of the image as a face and create a definite boundary around it.
  • SVM allows the categorization of both text and hypertext for inductive and transductive models.
  • SVM mainly focuses on images as it provides better search accuracy for image classification by comparing the traditional searching techniques.
  • The best applications of SVM are identifying the classification of genes of patients and other biological problems.

 

  1. Naive Bayes

Naive Bayes


Naive Bayes classifier works on the contingent probability principle as given by Bayes Theorem. It provides the conditional chance of an event 'A' given another event B has occurred.  

 

This classifier assumes a specific feature during a class is unrelated to the other feature's existence. Therefore, it is one of the simplest algorithms and outperforms even highly sophisticated classification methods. Some real-life examples include face recognition, weather prediction, news classification, and medical diagnosis.

 

Some of the real-world use cases of Naive Bayes are as given below:

  • To check whether an email is spam or not spam?
  • To classify and predict a news article, whether it is technology, politics, or sports?
  • To check a piece of text fragment and understand its sentiment as positive emotions or negative emotions?

 

  1. KNN (K- Nearest Neighbors)

KNN (K- Nearest Neighbors)

KNN algorithm predicts absolute value and applies to both classification and regression problems. It is a supervised learning algorithm that classifies a new data point in the target class according to its neighboring data points' characteristics. 

 

The new data point and nearest data point get measured in two ways; Euclidean Distance or Manhattan Distance. It is easy to implement and non-parametric, i.e., no assumption about the dataset is required. For instance, we can consider multiple cases like book recommendations or product recommendations by the machine that involves this algorithm.

 

Some use cases of these algorithms include:

 

  • KNN is often highly used in the banking system to predict whether an individual is fit for loan approval or not? Or does that individual have characteristics similar to the defaulter one?

 

  • KNN algorithms can also get used to finding an individual credit rating by comparing it with persons having similar traits.

 

  1. K-Means Algorithm

K-Means Algorithm3.

It is a form of unsupervised algorithm which helps to solve clustering problems and guarantees convergence. Here, the data sets get classified into some specific clusters that the data points belonging to the same groups are homogeneous and heterogeneous from the data present in other collections. This algorithm is quite popular and used in a variety of applications. It includes image segmentation and compression, market segmentation, document clustering, etc.

 

Some classic use cases of the K-Means Algorithm includes:

 

  • Clustering documents in multiple categories using tags, topics, and document content having similar traits. It is a very standard classification problem, and k-means is a highly suitable algorithm for this purpose.

 

  • By optimizing the process of good delivery by truck drones and using a combination of k-means clustering to find the optimal number of launch locations. And a genetic algorithm to solve the truck route as a traveling salesman problem.

 

  1. Random Forest Classifier

Random Forest Classifier
A collective of decision trees providing higher accuracy through cross-validations is called a Random Forest. These classifiers can easily handle the missing values and maintain the accuracy of a massive proportion of data. 

 

Some of the prime applications of random forest in different sectors are credit card fraud detection and loan prediction in Banking Industry, Cardiovascular Disease Prediction and diabetes prediction in the healthcare sector, Stock Market Prediction, and Stock Market Sentiment Analysis.

 

  1. Dimensionality Reduction Algorithms

 

Dimensionality Reduction Algorithms

In present days, vast amounts of data are getting generated and stored by data scientists. It may include data collected from Facebook on what we like, share, comment, or post, our smartphone apps collecting a lot of our personal information, or Amazon collecting data of what we buy, view, click, etc. On their site, this is where the dimensionality reduction algorithm comes into play, where only the relevant variables from the datasets get extracted. Some of the techniques involve missing value ratio, low variance filter, high correlation filter,

 

  1. Gradient Boosting & AdaBoost

Gradient Boosting & AdaBoost



These are the boosting algorithms that we will use when mass loads of data manage and make predictions with great accuracy. An additive model where previous models' shortcomings get identified by the gradient is known as Gradient Boosting Algorithm. Whereas the earlier model's shortcomings get identified by high-weight data points is known as AdaBoost Algorithm.

 

The Ending Lines

 

Yes, we have understood the basic concepts of Machine Learning Algorithms, but the main question arises how we can choose the correct algorithm for our model. A few features for considerations such as the size of the training data, speed or training time, number of irrelevant features, accuracy or interpretability of the data. Having a quick overview of our article, we can summarize what we have learned are formal definition of machine learning and machine learning algorithms, their advantages and disadvantages, classification, and the top 10 commonly used algorithms.

 

The next crucial thing is to start learning and practicing each machine learning technique on our own. The best way to begin studying Machine Learning and its Algorithm as practiced will eventually turn us into a Machine Learning expert.

 

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