4 WordPress Plugins to Make Your Blog More Powerful

Published on: 02 April 2016 Last Updated on: 11 June 2021

Starting a blog is one thing, but making it successful requires a lot of creativity and dedication. The blog is considered as an integral part of the lead generation process because it not only converts a random person to a potential lead but also helps him make better decisions. It is due to the reason famous B2B companies Cloudways and Design Mantic believes in doing effective blogging.

These companies are not only writing blogs to promote their services. But, they are producing remarkable content to quench the thirst of their prospects. And, this is where WordPress serves as a tremendous CMS.

If you are running a WordPress-hosted blog and looking for results-driven plugins, then you have reached the right place. Here, I will reveal some of the most amazing WP plugins that will take your blog to the sky in no time.

So, here you go…

All in One SEO Pack

If you are eager to make your blog successful, make sure to satisfy search engines. However, it is only possible if you are using an appropriate SEO friendly plugin for your WP hosted blog. Despite of hundreds of plugins, I prefer All in One SEP Pack. The reason is quite obvious. It is easy to use, extremely effective and helps in optimizing the content through brilliant use of targeted keywords.

Therefore, if you haven’t been optimizing your blog for search engines, then it is a perfect time for you to download this plugin for better outcomes.

Akismet

When running a blog, it is extremely essential for you to keep it protected against spammers. Because once you become the victim, you will have to go through some serious problems as far as the rankings and traffic is a concern.

To help resolve this issue, Akismet is a highly recommended anti-spam plugin for WP users. The best thing about this plugin is that it picks the spammers quite early and stops them make any unwanted activity within the blog. It is because of this reason I would suggest you install Akismet to your blog right now.

SumoMe

Running a blog without social sharing buttons is like writing a book and keeping it at home. Remember, attracting thousands of visitors to a blog won’t maximize the effect until you install a social sharing plugin. For this, I would strongly advise you to use SumoMe.

Industry influencer and a great human being Jeff Bullas also use SumoMe to help his readers share valuable content across all the gigantic social platforms quite easily. So, what are you waiting for?

Hello Bar

I believe that there is no other plugin better than Hello Bar for generating leads. I have been using this plugin for quite some time and it really helped me improved the figures quite drastically. There are two factors that make Hello Bar a legit choice for lead generation. The first one is its compatibility with WP and the second one is its attractive interface.

If you are desperately looking to attract more potential customers, then I would suggest you use Hello Bar as a vital source.

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Sharmita Shee

Sharmita is a Senior Developer with over 12 years of experience in the industry. She is skilled in several programming languages including Java, C++, and Python. Sharmita has a deep understanding of software development methodologies and is committed to delivering high-quality code. She enjoys collaborating with team members and finding innovative solutions to complex problems.

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Software Development Team

The development of any intellectual product is initially aimed at success in its implementation. In this regard, it is extremely important to ensure the proper quality of the software, application, or another result of the work. A properly selected Software development team is several professionals who can organize the work of creating software in a short time and at minimal cost to the investor, and also guarantee the relevance of the product. How to choose the correct development team? The level of professional training and work experience of specialists involved in the development of an intellectual product depends on the complexity of the work, as well as on the budget of the investor. Among all specialists actively offering their services on the market, development companies choose the following team members: 1. Universal specialists In the labor and services market, you can meet craftsmen who position themselves as highly qualified specialists who can solve any issues. As a rule, these people have extensive experience and knowledge in the matter under consideration. They can easily handle the development of a simple application. At the same time, when creating complex software, you will need deeper knowledge that generalists don’t possess. 2. Profile specialists These team members can be designers, programmers, content managers, or just engineers. They have the appropriate education and have extensive knowledge and experience in some competitive fields. They require the expert opinion, as well as full management of a specific part of the project, well-coordinated work in a team, and timely transfer of data and development results to related specialists. 3. Combined team Recruited to create the most complex applications or software. The team includes both generalists and specialized specialists. When developing an intellectual product, a horizontal control system is used. Each member of the team reports directly to the top management. When developing software, the worthwhile ideas of each employee are taken into account, and cross-checking is also provided. This project management technique is considered the most effective, as it allows you to minimize costs and achieve the expected results. Market experts and representatives of large IT companies recommend that when recruiting a team for a development team, ensure an approximately equal balance of generalists and specialized specialists. Moreover, each team must have either an official or an informal leader, who will be able to coordinate all tasks between other members of the collective. How to determine the optimal number of team members? There should be exactly enough team members to ensure the implementation of the project and the fulfillment of the technical task from the customer with the utmost accuracy, strictly on time. The following important factors influence the team size: The complexity of the task. The uniqueness of the development. Deadlines, defined by the customer and technical restrictions. The allocated budget for the implementation of the project by the investor. Provided resources to complete the task. In practice, to develop primitive software, no more than 2 – 4 people will be required. if it is necessary to implement a project of increased complexity, 7 or more team members may be required. What are the functions of team members? Most often, members of a pre-selected team perform the following roles in the development of a smart product: The key link is the investor or the customer of software development. It is he who provides information about what the final product should look like. Managing person – a team member who has experience in project management. He coordinates all specialists, combines ideas, draws up calendar schedules, conducts meetings, and issues final or intermediate results to the customer. The software architect is the most important specialized or generalist specialist. It provides the primary implementation of the general idea in strict accordance with the terms of reference. Subsequently, such a person is an assistant to the head, and he is sent for verification, as well as the assembly of all the developments of the work of the entire team. Engineers and ordinary developers – there may be several such members in the team. They are responsible for product development and software uptime. Designers – the area of responsibility of these specialists includes the design of attractive templates for users, as well as interfaces. Their work is related to how the product will be perceived by the end consumer. The main task of the designer is to create an easy-to-use and attractive product that will attract the interest of as many users as possible. The controller is a quality control engineer or an audit specialist. It checks the operability of the product, the compliance of the developments with the technical task, and also ensures the elimination of errors, which helps to improve the quality of the software. Analyst – a person who works in a team as an expert. He owns the needs of the market, as well as the industry for which the application is being developed. The demand for the software depends on this person, as well as its competitiveness, in comparison with other similar programs. Regardless of the assigned functions, the main degree of responsibility of the leader is to maintain a healthy spirit and friendly relations in the team. All team members should periodically come together for an online or face-to-face briefing, share results and problems. Upon discovering the first difficulties, each of the developers must notify all other team members about this. A focus on results is the main key to success. Requirements for the team in software development To achieve positive results, each team member must obey the internal regulations and follow the following important rules: Communication is carried out only in real-time, without delay in responding to a question or a task. All issues should be put up for public discussion – the principle of business transparency. All team members must trust each other. Each team member performs only their function. The composition of the team remains unchanged until the completion of development. In the collegiate discussion of questions, all of them are reflected on the board. Despite the above principles and examples, there is no single formula for creating an effective team in practice. To create a team, it is recommended to contact experts or recruitment agencies, and if the company already has the personnel, it is recommended to select developers not only according to the criteria of knowledge and experience but also according to the principle of psychological compatibility. An effective result is achieved only with a healthy spirit in the team and a competent distribution of tasks. Read Also: The Benefits Of Custom Software Development Ecommerce Software Development Trends Industrial Automation Software Development Big data software development services

How Have Python Made Machine Learning Convenient?

In the world of software development, Machine Learning also known as ML and Python are the two most popular terms that are in the current craze. Python is a high-level software programming language that has become the underlying base of many famous applications like Nextdoor, Instagram, etc. Machine Learning is a very important part of Artificial Intelligence (AI). Both of them target towards improving the many aspects of computer applications in their own different ways. Python is a programming language that is extremely easy to understand because of its simple syntax structure. As a result, developing the applications becomes a quite easy and simple yet strong framework. Machine Learning, on the other hand, helps an application to self-improvement without any prior programming. Integration of Python with Machine Learning has offered a number of benefits to the candidates because of which, candidates are able to work in this field more conveniently and efficiently. In order to understand how Python has made things convenient for the individuals, it is important to understand the various facilities of machine learning with Python. How Have Python Made Machine Learning Convenient? Ease of Understanding: As discussed above Python is one of the simplest forms of programming applications. Since, Machine Learning consists of complex algorithms, having one easy language to form the structure increases the code readability and decreases its maintenance. Vast Libraries: The huge volume of libraries that are available in Python for Machine Learning’s disposal is simply amazing. Some of the famous libraries are Numpy and SciPy for respectively for scientific computation and advanced ones. Data Analysis and Mining heavily rely on SciKit- learn. One framework named PyTorch is specially developed for Machine Learning. Other popular frameworks include Apache Spark, CNTK, TensorFlow, etc. Better quality output: Python is easy to understand and develop leaves the developers with plenty of time to increase the quality of Machine Learning application. The trial and error time on the complex algorithms is much less thereby providing plenty of space for improvement. The result of such an effort is usually very satisfying providing the end user with a pleasant experience. Extensive Support: The community of Python and Machine Learning Developers is ever increasing. In case, one stumbles in any area for their development, there is a horde of solutions available that are provided by the members of this huge community. Help is just a click away for any issues that one encounters during the course of development Flexibility: The flexibility the Python provides in developing frameworks for Machine Language is just amazing. The numerous approaches that can be used in development are lucrative for the designers and the developers. Linking data is altogether quite different becomes very easy for Python based Machine Learning frameworks. Name and Fame: The popularity of Machine Learning with Python base is on high demand. As a result, more and more people are inclining to learn and use the combinations. Therefore, it is easy to get such trained individuals in the market and get the job done. Higher Career Opportunities: In the hindsight of the previous discussion, more and more companies and organization are changing their base towards Python based Machine Learning frameworks. Therefore, job opportunities are increasing. This results in getting better jobs where the pay is extremely good and rewarding. Fewer Trials and Tribulations: Since Python has such a huge number of libraries, there is no practical need to compile the language in the instructions of the Machine Learning beforehand. It can be directly used in the program. Needless to mention, it saves time and effort which the two essential factors in any development and delivery. Cost Saving: It is a known fact that Python is an open source language that can be used freely in the system without any licenses. This aspect saves a huge amount of money that is otherwise spent on licensing fees across numerous platforms. The cost saving is immense which increases the budget of the project thereby increasing the quality in general. Dynamic Support: Machine Learning is a fast-paced framework that needs dynamic support. There are very less number of high-level programming languages in the current market that can provide such support, except that of python. Platform That Is Quite Independent: The flexibility that Python provides urges the developer to use it in any Machine Learning platform. This independence of platform that can be seen in the case of Python is making it extremely handy for a Machine Learning project that leads to successful development. Statistical Modeling: The model that can be used to develop a Machine Learning framework can be many. Python helps to create statistical modeling for the Machine Learning that is easy to comprehend and maintain. Data Set Maintenance: The first step towards developing a successful Machine Learning framework is to maintain a strong data set. Data set can be defined as a collection of information that the Machine Learning uses to enrich its algorithms. Using Python to set up the data set for Machine Learning is extremely easy and hassle-free. No Learning Hassles: As previously mentioned Python is easy to understand. This causes no learning hassles for individuals. Therefore, creating able developers with Python knowledge for Machine Learning frameworks becomes quite easy and less time-consuming. Easy Transition in Research: The machine learning frameworks that are primarily developed for the research projects rely heavily on Python as their base. Research projects related to pattern recognition or data analysis do not have enough time or budget for complex development languages. In such scenarios, Python provides the best solution with its ease of understanding and easy comprehensiveness. Customization Is Quite Convenient: Python’s flexibility with its easy to understand programming syntax and platform independence actually helps it to be customized to any need. A solution fits in all the needs and requirements without much hassle. Highly Adaptable In Nature: Python is a programming language that is quite easy to comprehend. The range of its comprehension is on a global scale. Even a fourteen-year-old kid would be able to understand and code in Python. This aspect increases its adaptability in the numerous Machine Learning platforms that are widely used across various areas. No Space Crunch: Machine Learning is all about efficient data handling and the way an application with the use of available data can do a better performance without any human intervention. This requires a lot of server space to load various kinds of data to feed into the respective algorithms. Python’s easy code requires much less space as compared to others. Therefore, it saves precious server space to accommodate more data thereby increasing the Machine Learning performance. Conclusion: The rising popularity wave of Python and Machine Learning will not go away that easily. Therefore, it is high time that both these aspects should be learned and implemented. Learning them would open different doors for numerous opportunities. Python because of its easy comprehensiveness will be the only weapon to code Machine Learning in the near future. It is always advised to future ready with an enhanced skill set. Machine Learning will slowly have a huge impact on the world we live in. Understanding and controlling machine learning would be the only way to survive in the cutthroat competition. Using the weapon of python one should start conquering the area of Machine Learning. It is the best buddy that developers want to be friends. The benefits would be extremely rewarding when one integrates the programming language of Python with the various aspects of Machine Learning framework. Read Also: How Developers Can Get More Out Of Hadoop? Kids Will Get These Benefits If They Learn Coding New Platforms To Explore In Advertising This Year 5 Things To Take Into Account To Hire Custom Software Development Services Coming Soon: A New Tech Challenge For Lenders – UMDP Data Standardization To Aid Closing

Tech Challenge for Lenders – UMDP Data Standardization to Aid Closing

The mortgage industry overhauled its processes and technologies with the TILA/RESPA Integrated Disclosure (TRID) Act rolling out. Technology initiatives that created competitiveness between banks soon became the norm. Compliance needed to move north. Now, automation has made paperless mortgage a reality with loan origination software, retail point of sale, customer-direct solutions, and electronic documents providing complete end-to-end support. Regulations are constantly changing and lenders, still in their TRID hangover, need to do more. What is UMDP? In the pre-recession era (before 2008), borrower and loan information was sourced and stored by different agencies. Siloed information hindered different players from accessing true data and it was usually shared by email. This resulted in errors and insufficient access to data in the banking system. Uniform Mortgage Data Program (UMDP) is the last leg to the realization of the electronic mortgage. Just as TRID brought in enhanced data quality in the origination, UMDP will improve data standards in closing disclosures and data interpretation. Government-sponsored enterprises (GSEs) have indicated, come 2017, they will refuse to buy loans that fail to conform to the Uniform Closing Dataset (UCD) standard. The Federal Housing Finance Agency (FHFA) is directing government-sponsored agencies (GSEs) to create a common approach, protocol, and data set for mortgage data. The common data set, which is understood by all, will improve data accuracy. Definition ambiguities of loans purchasable by GSEs can be avoided. The lenders can capture granular data of a particular standard. The Uniform Collateral Data Portal (UCDP) will be one electronic portal through which all lenders can submit appraisal data. The Uniform Appraisal Dataset (UAD)will provide common definitions and requirements of appraisal data. The Uniform Mortgage Servicing Dataset (UMSD) will define the data set with standardized definitions, formats, and values. The Uniform Closing Dataset (UCD) will provide standard data for the closing disclosure form. UMDP may have bestowed the entire industry with mortgage data becoming uniform and accurate, but the upgrades rigmarole still worry lenders. However, GSEs have chosen to be capable of verifying underwriting data and audit data anytime. Customer preference lies with a seamless closing process and the impetus for lenders will be to transition into e-mortgage. Fannie and Freddie have already announced that they will purchase loans if the appraisals were submitted electronically and met the UAD standard. Technology vendors are already ensuring that their solutions transform appraisal information into data compliant with the Mortgage Industry Standards Maintenance Organization (MISMO). Manual processes are slowly fading and lenders are going to cloud-based mortgage origination and servicing software. The entire process needs to be seamless to ensure compliance from origination to closing. Today, the challenge for loan origination software is compliance, but tomorrow the focus will be imminently on collaboration. It will be important to have data which can be shared and understood by all. Standardization helps loan originators and customers to access data from diverse sources and interpret them effectively. Data standardization is the key driver for e-mortgage and paperless origination and closing. UMDP will be the “new normal” and help the industry to standardize their processes for better visibility and faith in the system. Preethi vagadia is a business architect worked in Mortgage and Finance software department with top notch companies and has over 8 years of experience in Mortgage Lending Technology,Mortgage Technology software, mortgage management software, etc. She has also worked on several process improvement projects involving multi-national teams for global customers in warranty management and mortgage.

4 WordPress Plugins to Make Your Blog More Powerful

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Sharmita Shee

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Software Development Team

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Tech Challenge for Lenders – UMDP Data Standardization to Aid Closing

Patient Portal Development Company Addresses Technology

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