Posts tagged "machine learning"

Top 3 Web Development Trends That Will Continue in 2022

No business can establish a solid market reach with a bad online presence, as audiences nowadays choose sites connected with the latest technologies.

Some of the top web development trends we have noticed in 2021 might assist you in making your website more advanced. Let’s move a step further and look into top-level web technologies for your website.

 Machine Learning

Personalization of information has evolved into a significant aspect of the user experience. Machine Learning (ML), also known as predictive personalization, use artificial intelligence (AI) to deliver the most relevant information to each individual depending on what they are searching for or interested in at any given moment.

It was previously only possible with human intervention but is now possible automatically thanks to machine learning algorithms that analyze incoming data about users’ preferences over time before making adjustments accordingly so that they do not repeat themselves when showing them similar items!

The way we consume material has evolved as a result of ML technology. Rather than tailoring a whole section, you may target each user individually and cater to their preferences with customized feeds depending on what they want to view during that period or week-long vacation overseas! Airbnb and Netflix, for example, use this technology by comparing search queries made from screenprints entered into their websites to stored preference profiles corresponding to those queries – allowing customers to get tailored results while exploring either website and cataloging new movies/shows.

Businesses recognize this value since it ensures consistency in information delivery while catering directly to client wants/needs without wasting money by addressing too many users at once – which equals more significant ROI (returns on investment).

In addition, ML is being used with chatbots to improve consumer engagement. You may learn more about it by contacting an ML and chatbot developer.

The Advantages of Content Personalization Using Machine Learning:

  • Examine the site’s navigation and view durations.
  • A/B testing and enhanced customer understanding will provide you with additional insights.
  • Web pages may be easily optimized.


Data Security

Data security is one of the most critical factors in today’s corporate environment. There are several ways to keep your information secure in today’s society. One method is to use a solid firewall program and ensure that all connections from outside sources are appropriately filtered for possible risks before entering the network.

This will guarantee that no one ever gets through without being thoroughly scrutinized beforehand!

However, protecting your company’s intellectual property can be difficult, mainly if it contains sensitive information such as customer names or financial records that could financially jeopardize them by revealing their identity online, as well as putting other businesses at risk for cyberattacks.

It would be a significant setback in terms of both reputation and money! Artificial intelligence will become much more helpful when researching how to effectively deploy technologies such as biometric logins, scanning fingerprints/retinas, and so on. Still, there are always hazards associated because you don’t know who else has access.

With advancements in data security, web developers now have a higher possibility of providing safe information to their clients.

Advantages of Data Security:

  • The content management system is safe and secure due to plugins and extensions.
  • Choose more secure hosting and servers.
  • Use an excellent firewall application.


Artificial Intelligence

Chatbots are one of the most prevalent methods for serving people in the current day, and they are quickly becoming an essential component of digital growth. Many well-known corporations, like Facebook, Whatsapp, and Microsoft, are already using Artificial Intelligence and bots.

Artificial intelligence (AI) has the potential to increase company efficiency by 40% or more. (Accenture)

By using AI in web development benefits, you can forecast client preferences, allowing you to shape the entire design and quality of your website. AI technology will enable you to learn:

  • What are your customers planning to buy next?
  • What items aren’t popular among customers?
  • What types of items are most popular among customers?

Websites that use Artificial Intelligence and bot technology have a better level of client engagement. The essential aspect of AI-based bots is their ability to solve problems 24/7, comprehensive consumer behavior analytics, and human-like experience.


All You Need to Know About Recruiting Machine Learning Talent

In recent years, AI technology has become a necessary part of many businesses and in the years to come, it’s only going to become more popular. Regardless of the size of the company, more and more are now in need of an AI specialist.

Recruiting such a specialist is difficult when you have to compete with top brand tech giants such as Google and Microsoft. It’s impossible for smaller or even medium-sized companies to offer the same salaries and opportunities as AI specialists working for Amazon, for example. Amazon invests the most in AI and machine learning hiring, an average of $227.8 million per year.  Google spends considerably less, however, the average is still $130.1 million.

There is no need to despair. Even if your budget is a fraction of Amazon’s there are still some things you can do to increase your chances of finding the perfect AI specialist.

Emphasis On Experience When Recruiting

AI and ML (machine learning) specialists will have various levels of experience. You need your hiring strategy to include a specialist with the level of experience that meets your project’s needs. On top of that, you need to look in the right place for this type of experience.

If you are looking for a junior AI candidate, you should focus your search on universities, training programs, hackathons, and meetups. Those with more experience ready to fill a senior role can be found by networking and researching academic papers.

Understand What You Need

As with any position you need to fill, you need to have a clear understanding of what you are looking for. Machine learning roles will require a strong background in math and statistics. If you are recruiting for a senior role, you may want someone with a Ph.D. in these fields. These skills are fundamental in problem-solving and identifying the best algorithms for a certain problem.

Another good skill your candidate should have is curiosity, a need to investigate things. This will go a long way when it comes to facing unclear problems. A strong candidate will be able to understand data and quickly catch on to the new concepts of AI and ML, as this field advances very quickly. Many also find it’s beneficial to have someone who s genuinely enthusiastic about their job and the company.

Show That You Have Opportunities Their Current Job Doesn’t Have

Nowadays, people’s priorities are changing and it’s not always about the highest salary. If you want to attract a high-end ML candidate, you are going to have to provide intellectual challenges, a range of problems, expertise in the team, the quality of your brand, and a competitive salary.

If you can’t match the salary of your competitors, try offering other incentives. Flexibility and the ability to work remotely are very attractive to a lot of people.

Offering things that people don’t have but want is an ideal way to attract people who are currently employed and therefore not actively searching for a new job.

Team Up With Universities

This is a case of going straight to the source of machine learning talent. Keeping close contact with local universities allows you to make contact with new recruits before they start looking elsewhere. You could even consider funding school projects.

This method has become very popular and you may find other companies doing the same thing to recruit AI and ML talent. For you to stand out from the rest, make sure your projects, or even competitions, are interesting and engaging.

Contact A Staffing Agency

Recruiting in the IT industry is cut-throat, more so for high-end, experienced AI and ML specialists. Recruiting takes time and money, two things nobody wants to waste.

Recruitment agencies have access to a large talent pool with candidates ready to start straight away. They save you a lot of time by doing much of the paperwork themselves and there is often more flexibility than when you high a candidate.

While recruiting AI and machine learning specialist is certainly a challenge, it’s not impossible. These strategies are some ideas to put into place in order to increase your chances of finding the right candidate for your needs.

How to Successfully Hire a Data Scientist

Data scientists are in high demand. Studies carried out by show that strong candidates can have up to three offers and the success rate of hiring is usually below 50%. With such a difficult task, what is the best way to hire a data scientist? One way is to make sure the process is kept short. But this will only work if you have the right set of objectives laid out before you begin the process.

How to use a Data Scientist

It is crucial to set the appropriate expectations for the data scientist and for the needs of your company.  Some of the most common uses of data science are.

  • Resolving problems with data analysis- you might receive a lot of data but not use it analysis it correctly to achieve the goals of the business.
  • Receive recommendations- data science can be used to predict models for businesses. One particular use is to gain more insight into target clients.
  • Provide business intelligence- this refers to data management, how the information is arranged and produced via dashboards. It helps in the decision making process.

It is more than possible to combine two or all three of the uses to gain more of an overall view of the business situation and make more informed decisions.

The Life Cycle of Data Science

The first and probably most important step is to understand the problem. You will then be able to identify the data needs before selecting the right methodology to use. Following this, there is proof of concept before another crucial stage, the validation, and experimentation. This is where you will see if the methodology chosen works. The final stages include the release of the product and the maintenance.

You may not enjoy evaluating the candidate’s skills for each stage, especially if the wrong platform is used. Generally speaking, hiring managers are happy if they achieve an evaluation accuracy of 50%. This ongoing task can occupy 20% of the data science team’s time. So a technical recruitment platform is worth considering.

The Required Skills of a Data Scientist

There is an array of skills often depending on the needs of a company, but here are some of the key things a data scientist should have:

  • Statistics and linear algebra- your candidate should have excellent decision-making skills and be skilled at collecting, analyzing, and making inferences from the received data.
  • Machine learning- making predictions after classifying or grouping data. Ideally, they should be able to use big data technologies to build pipelines that will feed machine learning algorithms.
  • Data mining- the ability to visualize and mine raw data with the objective of producing meaningful insights.
  • Optimization- the data should be used to produce the maximum outcomes possible.
  • Technical skills-data scientist candidates should be confident in a number of programming languages (Python, JavaScript, SQL, C, and C++), libraries (OpenCV, pandas, and NumPy, and structures and algorithms (Excel, Hadoop, and SAS).

The different types of data Scientists

Broadly speaking, data scientists can be classified as researchers or engineers. It is recommended to have a combination of the two.

Data researchers are highly confident in math and/or statistics. They must be able to develop custom algorithms to extract the most from data and find solutions. Technical skills should include R, Python, SQL, and NoSQL is an advantage.

A data engineer candidate should have sufficient experience in coding, structuring, and prototyping. They must also be confident using Python, Scala, Java, and MATLAB. It’s also necessary for them to be able to visualize and build machine learning models

How to Assess the skills of a Data Scientist

Data science requires skills in three fields that often overlap:

  • Math and statistics- linear algebra, probability, differential calculus, and descriptive and inferential statistics.
  • Machine learning and programming- the most commonly used algorithms, data structures like trees and graphs, coding in Python or R. Also:
    • Classification and regression
    • Supervised and unsupervised learning
    • Clustering algorithms
    • Decision treees7random forest classifiers
    • Naïve Bayes algorithm
    • Boosting and bagging
    • Bias-Variance Trade-off
    • Binary, multiclass, and multi-label classification
    • Neural networks
    • Understating of various networks
  • Business/domain knowledge- to effectively use the data, a candidate must understand the field the business is in, understanding the specific problems and presenting the solutions.

Data Scientist Salaries

Bases on the statistics taken from Glassdoor, the national average salary for a data scientist in the US is $117,345.  An intern data scientist can earn an average salary of $67,000, while a senior data scientist can earn approximately $137,000 per year.

As with most jobs, experience, qualifications and the size of the company will play a part in the potential salaries.

Big names hiring Data Scientist

  • Twitter
  • Reddit
  • NBC
  • Nielsen
  • Square
  • MTV
  • Microsoft
  • Facebook
  • Fitbit
  • LinkedIn
  • Amazon

Obviously, there are many, many more.

How to Assess Skills in the recruitment Process

Solving a real-world Machine learning problem might take too long and therefore unsuitable for an interview. Developer assessment platforms take real-world problems and break them down into smaller tasks that allow a developer to show their skills. Many platforms have real-world Machine learning problems too. This could be useful further along in the interview process to assess skills in more detail.

Interview Questions

There are literally hundreds of questions you could ask. Here are some examples:

  • What is a confusion matrix?
  • Explain SVM machine learning algorithm in detail.
  • What is pruning in a decision tree?
  • What is selection bias?
  • How would you turn ‘X’ business problem into an experiment?
  • Ask to see examples of previous projects and code.





Get a Peek into the Future of Hiring and the Role of Machine Learning

When you talk about the future of any aspect of daily life, you are actually talking tech. Because we are moving towards everything digital, machine learning will have a major role. Let’s talk about what machine learning is, how it will impact future recruitment processes and why it is important to get up to date.

Q1: What is machine learning?

Machine learning is based on data science, but being a subject with many ramifications, giving a proper definition is not possible. But in order to simplify it to our understanding, machine learning is a computer’s ability to learn through patterns of data. The more patterns it processes, the more it learns to improve its recognition ability. By going over these patterns or samples of data, the machine is able to predict or decide on different matters.

Another way of explaining it is this: “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.” – Tom Mitchell, Carnegie Mellon University

Task T: predict shopping patterns in the Tesco store

Experience E: run an algorithm on past patterns in the store

Performance measure P: improves predictions for future shopping

If your machine successfully learned from running past patterns, it should make better future predictions.

Q2: How does Machine Learning influence recruitment processes?

First of all, recruitment relies on large amounts of data. This alone creates a strong connection with machine learning. If we look closely, we will easily infer that Machine Learning will have a major role in predicting job trends, demand on skills, shortages or over-demand based on location, human resource spread across areas and so on.

This gets the work cut out for recruiters who now need to source out prospective candidates, adapt to changing trends in the industry and analyze working patterns of candidates and employees. ML will also provide info on a company’s’ hiring habits, the lifespan of employees inside the company, satisfaction with jobs and migration tendencies.

The ability to “see into the future” and prepare for a shift in industries is invaluable information for recruiters when putting up a plan and sourcing out people.

With data analysis out of the way for the humans in HR departments, you get to focus more on quality of relationships, building valuable connections and nurturing long-term collaborations. Let the machine do the number crunching and you, the recruiter can develop your soft skills: intuition, empathy, approach, coping with changes and better supporting the people you meet in the process. Machines can do an assessment of skills and place in suitable positions. This will eliminate missing potential candidates because there was a mismatch in term use.

Futuristic as this sounds, you need to gear up for this change in approaches to make your recruitment successful.