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Tag: best artificial intelligence and machine learning certification course

How AI certification can revolutionize recruitment?

Posted on by Imarticus Learning

How AI certification can revolutionize recruitment?

As artificial intelligence evolves and becomes sophisticated, its impact on various industries will inevitably be profound. One area where AI is already beginning to impact significantly is recruitment.

With the help of AI certification, recruiters can now efficiently and effectively screen candidates for jobs by analyzing their resumes and identifying the best candidates for the position. This blog post will explore how AI certification can revolutionize recruitment.

What is AI certification, and how does it work?

 AI certification is a process by which individuals can prove their proficiency in artificial intelligence. The certification exam tests candidates’ understanding of AI concepts and their ability to apply these concepts to solve real-world problems.

AI certification can be a valuable asset for job seekers as it can help them stand out from the competition and demonstrate their skills to potential employers. Additionally, AI certification can help employers identify candidates with the necessary skills to fill open positions.

Overall, AI certification is beneficial for both job seekers and employers alike. It can help job seekers showcase their skills and knowledge while assisting employers in identifying the best candidates for vacant positions.

Here are some ways in which AI certification can revolutionize recruitment.

First and foremost, AI certification can help identify the most suitable candidates for a role. By analyzing a candidate’s qualifications and experience, an AI system can shortlist the best matches for the job. It means that recruiters can spend less time sifting through CVs and more time interviewing the most promising candidates.

Secondly, AI certification can help to streamline the recruitment process. By automating repetitive tasks such as CV screening and candidate assessment, an AI system can free up recruiters’ time to focus on strategic tasks. It can result in a faster, more efficient recruitment process that saves time and money.

Finally, AI certification can help to improve the quality of hires. An AI system can identify the key characteristics that make a successful employee by using data and analytics. This information can then screen candidates and select those most likely to succeed in the role. As a result, AI-certified recruitment systems can help reduce turnover and improve the quality of the workforce.

AI certification is thus a valuable tool for recruiters. By helping to identify the best candidates, streamline the recruitment process, and improve the quality of hires, AI certification can revolutionize the way businesses recruit employees. In today’s competitive market, those who embrace AI-powered recruitment will have a significant advantage over those who do not.

Discover the Artificial intelligence and Machine Learning course by Imarticus Learning:

The Artificial Intelligence and Machine Learning certification from the E & ICT Academy, IIT Guwahati, and the best industry experts is a great combination. This certification will teach students to become data scientists, data analysts, machine learning engineers, and AI engineers.

Course Benefits for Learners:

  • Prepare for a fascinating data science job by completing 25 real-world projects that provide you with industry experience.
  • Impress employers and demonstrate your Artificial intelligence certification with an E & ICT Academy, IIT Guwahati, and an Imarticus Learning-endorsed certificate.
  • Students can now participate in live online seminars and discussions with one of the finest instructors in India.

4 key differences between AI courses and robotics

Posted on by Imarticus Learning

4 key differences between AI courses and robotics

Artificial Intelligence and robotics are branches of science that people often get confused with. People think that they are related to each other, that one is a branch of the other. The fact is, though they serve some similar purposes of automation of some systems, they are entirely different.

Those who want to pursue a career in either of these must understand this difference. One should not enrol for an AI certification thinking that it deals with making robots. Let’s find out what are the main differences between AI and robotics. 

Artificial Intelligence vs Robotics

An artificially intelligent robot is what is making the public confused about the relationship between AI and robotics. The main differences between them are mentioned here.

  1. Robotics is a branch of engineering while Artificial Intelligence is a part of computer science.
  2. Robotics creates robots that have a physical form, while AI creates smart machines that can solve problems for humans or make their tasks simpler.
  3.  Robots are programmed to do different tasks which enables them to be autonomous or semi-autonomous. They cannot make changes on their own. On the other hand, algorithms can make modifications based on the input they get, using machine learning or similar tools.
  4. Artificial intelligence is widely used in the daily lives of the public while robots are used in selected areas, which is very low at the moment. 

At the same time, it should be noted that not all robots that we know about have a physical form. The term ‘bots’ refers to robots but the one that most of us see in our day-to-day lives refers to the chatbots, search engine bots, etc are software and are only part of the digital world so they may not be called real robots. 

Where do AI and Robotics meet?

AI and robotics meet where Artificial Intelligence is used in the programming of the robots to make them intelligent. They are known as intelligent robots where the robots make the physical part and the AI forms its ‘brain’. Such robots can move, pick up things, and can also keep them at their specific places.

What helps the robot here are AI algorithms and a camera that helps determine the usual places. AI here almost works as a ‘sixth sense’ where the robot can be sensitized to use its various ‘senses’. 

Future of AI in robotics

AI and robotics are here to stay for a long time. This combination could perform several tasks very well, regardless of how complex they seem to be. People could own a robot and even be able to train them to do customized jobs for them. Intelligent robots could be useful in various industries such as delivery systems, agriculture, etc. 

One can find several AI and Robotics courses. Those who want to pursue that career could start with the Artificial intelligence and Machine Learning course that introduces them to all the latest technologies and tools in this field. The AIML course at Imarticus is conducted by the IIT Guwahati. Participants for this course will be getting lectures directly from the academic professors as well as from the experts in the industry.  

Conclusion

One of the cons of the future of AI and robotics is the lack of enough creativity to practically use them. To counter this, we need more interested people in the AI industry. The primary step towards this should be the Artificial Intelligence certification, both entry-level and intermediate. At Imarticus, one can find one of the finest courses that will provide expertise, experience, and proper guidance. Enroll now!

Unsupervised V/S Supervised Learning: The Ultimate Tech Battle 

Posted on by Imarticus Learning

Unsupervised V/S Supervised Learning: The Ultimate Tech Battle

To understand machine learning, it is crucial to understand the type of data and how to utilize it to the best of our efforts to solve real-world problems. This is where AI ML courses provide the proper guidance needed to get started. Within artificial intelligence and machine learning, supervised and unsupervised learning are the two basic approaches to handling data; these patterns can help you predict future behaviour or outcomes. 

Supervised Learning

Supervised learning is a machine learning technique that allows computers to learn from examples. In supervised learning, the computer is provided with a set of training data (a list of inputs and corresponding outputs) to learn how to map input data into output data. The most common application of supervised learning is for classification—the computer learns to recognize patterns in the data and make predictions based on those patterns.

The most common supervised learning applications are prediction, classification, and regression. Classification is used when you want to assign an object or a piece of information into one or more categories. In contrast, regression is used when you want to predict the values of some variable(s) based on other variables.

Supervised learning has many applications in the real world, including

–Prediction: Predicting what will happen next.

–Classification: Identifying categories and subcategories of items.

-Regression: Finding trends based on historical data. 

Unsupervised Learning

Unsupervised machine learning is a type of AI that enables a computer to learn and make predictions without being given any specific examples of correct answers or any input data that has been marked as correct.

In this type of learning, the algorithm is provided with unlabeled data and must be able to figure out how to group it into meaningful groups. One example would be grouping similar images into categories like “cats” or “not cats.” Another example would be identifying objects within an image, like a dog or a cat.

Unsupervised learning is a type of machine learning that involves analyzing data without any prior knowledge about the structure or patterns in it. In unsupervised learning, an algorithm will attempt to find patterns in the data and use them to make predictions.

Unsupervised learning is used in natural language processing, computer vision, and other fields. Unsupervised learning can be used for many different purposes, but there are some common applications including: 

  • Discovering hidden variables
  • Identifying relationships between variables
  • Identifying anomalies in data, predicting values
  • Finding clusters or groups within a data set.Here’s what both of them have to offer against the other-
Unsupervised Supervised
Unsupervised learning, also known as self-organized learning or unguided learning, means that you do not give any specific training examples to your model. You just provide it with a set of data and let it learn from it. This type of learning is usually used for clustering and dimensionality reduction. It is also called inductive inference because we use training data to learn how to generalize from our observations about some phenomenon in order to make good predictions about future events involving that same phenomenon.
The algorithm is given a set of data and must identify patterns within it. For example, if you have a list of songs and their genres, then the goal would be to determine the genre of each song without any other information. Here, an algorithm is given a dataset along with metadata—information about how it should be interpreted—and then learns to categorize new examples according to what it has learned from its training data. For example, if you have training data consisting of images labeled as “cat” or “dog,” then the goal would be for your algorithm to learn what category each image belongs in so that when presented with new images, it can correctly identify them as well.
This type of machine learning has many applications in fields such as medicine, where it can be used to identify cancerous cells in medical images; finance, where it can be used to predict stock prices; and retail, where it can help identify products likely to be purchased by customers. Supervised learning can be used to build predictive models that can be used in many different business applications, such as fraud detection and customer churn prediction. It can also be used for modeling time series data, recommendation systems, and classification problems.

Unsupervised learning is ideal for finding hidden patterns in your data. These patterns can help you predict future behavior or outcomes. You can use unsupervised learning to find clusters of similar customers, for example, and then use those clusters to predict what products they’ll buy next. Or you can use them to find correlations between different variables—like age and income—and then use those correlations as a starting point for further research into specific groups of people who share those characteristics.

Supervised learning is also useful because it helps you build classifiers—a fancy word for “systems that classify things.” You might want to create a classifier that identifies whether or not someone has cancer-based on their medical records (which would be an example of supervised learning) or one that identifies if someone is behaving differently based on their social media activity.

To learn more about the prediction techniques and the integrated course offered by Imarticus learning, get in touch through our website or our offline learning centers near you. This course is led by a prestigious IIT Guwahati college. Get you the best coaching needed to boost your career growth; get in touch with our experts today!

An insight into self-supervised learning

Posted on by Imarticus Learning

A subtype of machine learning and artificial intelligence is supervised learning. It is characterized by its reliance on labeled datasets to train algorithms capable of reliably classifying data or forecasting events.

An approach known as self-supervised learning uses unlabeled input data to produce a supervised learning method.

There is plenty of unlabelled data to choose from. Self-supervised learning is motivated by the desire to first acquire usable data representations from an unlabelled sea of information, and then tune those representations by labeling them for a supervised learning method.

Principle of Working

Self-supervised learning relies on the structure of the data as a source of supervisory signals. With self-supervised learning, the goal is to make predictions about inputs that are either unobserved or concealed, based on the inputs that are both visible and invisible.

Importance of Self-supervised Learning

To predict the consequences of unknown data, supervised learning needs labeled data. Large datasets, on the other hand, maybe required in order to construct proper models and arrive at accurate predictions. It may be difficult to manually identify huge training datasets. When dealing with large volumes of data, self-supervised learning can manage it all.

Computer vision tasks that use OpenCV and Convolutional Neural Networks are often performed via self-supervised learning. Self-supervised learning may enhance computer vision and voice recognition systems by reducing the need for example instances, which are necessary for building correct models.

Human supervision is required for supervised models to function properly. There are exceptions to this rule, though. Reinforcement learning may then be used to encourage machines to start from scratch in situations where they can get instant feedback without causing any harm. However, this may not apply to all situations in the actual world. 

Prior to making decisions, human beings may consider the repercussions of their actions, and they don’t need to experience every possible outcome to make a decision. Even machines have the ability to function in the same manner. Self-supervised learning takes over now. It creates labels without human participation and allows robots to come up with a resolution on their own.

Applications of Self-supervised Learning

Computer vision and Natural Language Processing (NLP) are the primary areas of application of self-supervised learning systems. There are other areas where self-supervised learning is applied. Most of them are mentioned below:

  • It is used for coloring images in grayscale
  • It is used for filling up missing gaps in pictures, audio clips, or text
  • It is used in surgeries to predict the depth of cut in the healthcare industry. It also provides better vision in medical visualization by colourisation using computer vision
  • It is used in self-driving cars. The self-supervised learning technique allows the car to calculate the terrain on which it is and also the distance between other cars
  • It is used in ChatBots as well

Conclusion:

Using self-supervised learning for voice recognition has shown encouraging results in recent years and is now being employed by companies like Meta and others. Self-supervised learning’s main selling point is that training may be conducted with data of lesser quality while still boosting final results. Using self-supervised learning mimics the way people learn to identify items better. 

Learn machine learning & AI with Imarticus’ AI & machine learning certification. This is an all-inclusive program that covers all the tools widely used in the domain of data analytics and machine learning in just 9 months.

To assist candidates in developing into skilled data scientists, the curriculum includes real-world business projects, case studies, and mentoring from relevant industry leaders. Secure your AI & Machine Learning Certification now by clicking here.

Hello new world of “Supervised Learning”

Posted on by Imarticus Learning

We have entered an era of machine learning called “supervised learning.” In this world, computers can learn from data that humans have labeled. It is a considerable shift from the old world of machine learning, where computers were only able to learn from data pre-programmed by humans.

This new world of machine learning is opening up many possibilities for businesses and organizations that want to harness the power of artificial intelligence. This blog post will discuss supervised learning and how you can improve your business!

A report from Forrester Research predicts that the market for artificial intelligence will grow $37 billion globally by 2025. Advancements are driving this growth in supervised learning algorithms and the increasing amounts of data available to train machines.

An overview of supervised learning

Supervised learning allows computers to learn from data that humans have labeled. Supervised learning aims to accurately train the computer to predict the desired results for new input data.

Supervised learning algorithms operate by finding a mathematical function that best fits the training data. Using this function to predict the desired outputs for new input data. The process of finding this mathematical function is known as “training the model.”

There are many different supervised learning algorithms, each with advantages and disadvantages. The most popular supervised learning algorithms include:

  • Linear regression
  • Logistic regression
  • Support for vector machines
  • Decision trees
  • Neural networks

How Can Supervised Learning Be Used to Improve Business?

Supervised learning can improve business in many different ways. Some of the most popular applications of supervised learning include:

  • Improving customer support: It builds chatbots that provide automated customer support.
  • Enhancing marketing campaigns: Supervised learning can improve marketing campaigns’ targeting by building models that predict which customers are most likely to respond positively to a given offer.
  • Optimizing supply chains: Supervised learning can build models that predict product demand and optimize supply chains accordingly.
  • Improving fraud detection: Supervised learning builds models that detect fraudulent activity such as credit card fraud or insurance fraud.
  • Enhancing security: Supervised learning can build models that identify unusual behavior patterns that indicate security threats.
  • Predicting consumer behavior: Supervised learning builds models that predict how consumers are likely to behave in the future. You can use it to optimize product offerings and marketing campaigns.

The possibilities for using supervised learning to improve business are endless!

Discover AIML certification with Imarticus Learning

This Machine Learning certification course provides students a solid foundation in data science’s day-to-day applications by teaching them how to apply these skills to real-world issues. This training is for graduates and early career professionals who want to advance their fields in Data Science and Analytics, one of the most in-demand skill sets. 

best artificial intelligence courses by E&ICT Academy, IIT GuwahatiCourse Benefit For Learner: 

  • Students can now learn Machine Learning by participating in 25 real-world projects and case studies with industry partners to learn practical machine learning skills. 
  • Students learn how to apply machine learning to solve data-intensive problems. This course will teach students about data analytics and basic machine learning concepts, as well as some of today’s most popular tools. 
  •  Impress employers and showcase skills with the IIT AI course recognized by India’s prestigious academic collaborations.

The fourth industrial revolution: The primer on Artificial Intelligence and Machine Learning courses

Posted on by Imarticus Learning

We are living in the fourth industrial revolution. It is a time when technology is rapidly changing and evolving. One of the most critical aspects of this revolution is artificial intelligence (AI) and machine learning (ML). These technologies impact every industry and will continue to do so in the future. If you want to stay ahead of the curve, it’s essential to understand these technologies and learn how to use them.

This blog will provide a primer on AI and ML courses. We will also discuss why it’s vital for you to learn about these technologies. 

The fourth industrial revolution is a period of rapid technological progress and digital transformation. The fourth industrial revolution brings in a new age of automation, data-driven decision-making, and intelligent machines due to innovative technologies such as artificial intelligence (AI) and machine learning (ML). 

What are the key drivers of the fourth industrial revolution?

The key drivers of the fourth industrial revolution include:

  • Rapid technological advancement and digital transformation
  • The emergence of new technologies such as AI and ML
  • Increased connectivity and interdependence among countries, businesses, and individuals
  • Shifting demographics (e.g., an aging population)
  • Increased volatility, uncertainty, and risk
  • Rising income inequality

As we stand on the brink of a fourth industrial revolution, it’s more important than ever to understand the technologies driving it. One of the transformative technologies of our time is artificial intelligence (AI). From how we work and communicate to how we travel and entertain, AI has the potential to change almost every aspect of our lives.

What is the future of AI and machine learning?

The future looks bright for Artificial Intelligence (AI) and Machine Learning (ML), with both technologies experiencing exponential growth in recent years. The applications of AI and ML are limitless, spanning across industries such as finance, healthcare, manufacturing, retail, transportation, and logistics. Businesses need to gain a strong understanding of AI and ML to stay ahead of the curve and seize the opportunities brought about by these technologies,

Fortunately, there are many courses available that can provide this essential knowledge. Here is a primer on the best AI and ML courses, perfect for those looking to get started in this fascinating field.

Discover the AIML course by Imarticus Learning:

This Artificial Intelligence and Machine Learning certification combine the E & ICT Academy, IIT Guwahati, and the best industry leaders. This 9-month course will help students prepare for data scientists, data analysts, machine learning engineers, and AI engineers.

This course will help students strengthen their fundamental AI competencies. Students can now use the Expert Mentorship service to build a practical understanding of artificial intelligence and machine learning. Take advantage of real-world projects from a variety of sectors. This course has a long way to go towards assisting you in seizing lucrative job possibilities in the hot fields of artificial intelligence and machine learning.

Course Benefits for Learners:

  • Prepare for a fascinating data science profession by acquiring in-demand data science and AI abilities with 25 real-world projects that give employers diverse industry exposure.

  • Impress employers and display your AI skills with an E & ICT Academy, IIT Guwahati, and an Imarticus Learning-endorsed certificate.

  • Students can now participate in live online seminars and discussions with one of the finest instructors in India.