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Category: Management

Covid-19 Data Analysis Using Tableau

Posted on by Imarticus Learning

Covid-19 Data Analysis Using Tableau

Tableau is a fast-growing data visualization and data intelligence software used worldwide to drive better data-driven decisions. Dynamic software manipulates updated data as and when it is introduced into Tableau. A simplistic description of Tableau is a pivot table on steroids. But Tableau is so much more than Excel and other software available in the market for analyzing data. 

Today Tableau is at the forefront of data analysis with millions of users globally. Apart from its advanced features, interactive dashboards have the facility to import data from different data systems like Python and R. 

To be up-to-date on data analysis, get a Tableau certification by doing a Tableau course or a data science course in India at Imarticus Learning.

The Covid-19 epidemic has devastated the whole world and resulted in many deaths. It transcended national borders and attacked citizens of all countries without discrimination. 

Many countries maintained a daily tracker of Covid-19 patient statistics to take appropriate action regarding lockdowns, curfew, treatment plans, ensuring availability of medical and health care facilities, availability of oxygen, vaccine production, emission, etc. Tableau software has been handy for all the data at a global level and facilitated decision-making by the governments of the respective countries.

Tableau helped government health organizations and companies in data analysis during the Covid-19 epidemic. Given below is the use of a Tableau to track the progress of the Covid-19 epidemic in the U.S.

Tableau workday data file was used.

The Tableau workday data file was used with Tableau Prep Builder and data from John Hopkins to represent a dynamic data representation. The above is a static view of the Tableau Dynamic Prep Builder. A survey of work from home employees, which was administered on the Salesforce platform, is represented below:

Filters were used to enable analysis by employee’s region, quota responsibility, and previous work experience from home.

Tableau launched the Datahub to make sense of the Covid-19 data.

In India, Covid-19 data was analyzed using Tableau. The analysis of the data focused on the following key data points:

  1. The timeline of the spread in the country
  2. The primary reasons for the spread and the various government responses to it.
  3. Analysis of research and development.
  4. The number of Covid-19 tests carried out and analysis of the same. 

To be up-to-date on data analysis, get a Tableau certification by doing a Tableau course or a data science course in India at Imarticus Learning.

Timelines were plotted, charting the return of tourists from Wuhan and the return of tourists from Italy. The various events which led to the congregation of people were all charted.

Most of the reasons for the spread were the various religious congregations in the country belonging to different faiths. This led to the peaking of the Covid-19 cases, as shown in the above chart.

Government responses can be classified as under:-

  • Thermal screening of patients returning from abroad. This was gradually extended to the various airports in India.
  • By Mid-March, containment measures were introduced to prevent spread through contact.
  • By 22nd March, lockdown measures were introduced in 22 states, followed by inter-state movement restrictions. The gradual nationwide lockdown was also introduced. The data graphs in Tableau show rapidly increasing numbers of Covid-19 patients resulting in further extensions of the lockdown.
  • Research and treatment: India fared better compared to the rest of the world, as shown in the Tableau graph below:
  • Many government facilities such as the DRDO and ISRO started producing protective equipment, PPE suits, and ventilators to fight against Covid-19. Gradually other Public and private sector enterprises were also drawn into the production of equipment to protect against the Covid-19 epidemic.
  • Testing: Covid-19 testing was gradually extended to cover significant population proportions. All pneumonia cases were included in the ambit of testing.

 The testing ambit was gradually widened to include broader testing in all the hotspot areas. The graph below shows the confirmed cases contrasted against the number of deaths in India.

Some of the important takeaways from the above exercise were:

  • Data from Tableau graphs and data visualization models were used for public decisions.
  • The data visualization models were used to communicate the immense scale of the tragedy in a way that mere numbers could not.
  • The summary statistics mask inequalities between regions, states, and groups of people.
  • The data collection itself was a very complex exercise.
  • One measure from the data charts or visualization exercises does not tell the complete story. Many measures had to be taken together to tell the complete story of the progress of the Covid-19 epidemic.
  • Rapid developments of Covid-19 charts happened at the expense of accessibility. The Tableau statistics and graphs were not accessible to those working on the ground.  

 To be up-to-date on data analysis, get a Tableau certification by doing a Tableau course or a data science course in India at Imarticus Learning.

Visit Imarticus Learning to learn more. Contact us through chat support, or drive to our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, and Gurgaon. 

6 Trends Shaping the Future of Data Science

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6 Trends Shaping the Future of Data Science

Introduction

The data science industry is rapidly evolving. The field is changing from the types of data collected to the tools and techniques used to analyze it. More and more companies are using these insights as part of their business strategies. As the world becomes more digitally adept, data scientists are in high demand to help businesses make sense of the information they collect.

At Imarticus, we offer data science courses as we are always on the lookout for what’s next in this rapidly changing future of data science. 

Here are six predictions for trends shaping the future of data science:

1. Data Collection Becomes More Ubiquitous

As companies become more comfortable with data to improve their business performance, they will likely collect more data about their customers and employees. In particular, we expect to see an increase in the amount of location-based information that companies collect about their customers’ movements (and even their emotions).

We are still in the early stages of understanding how to use data to make better decisions, but we are beginning to understand which best practices are most effective. For example, there’s a growing consensus that it’s essential to train your models on as much data as possible—not just large datasets but a variety of datasets representing different data types and problem areas.

2. Data Scientists Become More Valuable

As companies start collecting more data types, they’ll need to hire people who can help them make sense of it all. They will be willing to pay top dollar for those people because they know how important it is to access insights from every corner of their organization. There will also be an increased demand for people training in applied statistics or machine learning to apply those skills broadly across all areas. 

Data democratization: Data scientists are not just going to be working in corporations anymore—anyone with an internet connection can harness the power of data science.

3. The Internet of Things 

IoT is already changing/defining how we interact with our environment, and it will continue to change how we interact with data. As our physical world becomes increasingly connected, we can analyze our surroundings better and understand what they mean.

4. Machine learning

ML is becoming more accessible than ever before. Thanks to cloud computing and powerful open-source tools like TensorFlow and Keras, even non-coders can create powerful models without needing a Ph.D. in mathematics or computer science.

Additionally, there is a growing awareness regarding the importance of machine learning algorithms that can handle complex tasks with no human-defined solution. It means creating systems that can learn from their users’ behavior over time and use this information to solve new problems. It is similar to how Google Search knows what you want when you type in “tacos” or “puppies” while providing recommendations based on your previous searches.

5. Deep learning

Deep learning helps us understand language at a deeper level than ever before. By analyzing a text at various levels—from individual words up to sentences, paragraphs, and entire documents—we can extract information that would otherwise be impossible to find using traditional keyword search or keyword matching algorithms.

6. The growth of Big Data

As more people start using personal data to make discoveries, we’re going to see a lot more information about human behavior emerging—and as it becomes easier for people everywhere to collect this information and share it with others, we’ll see even more discoveries made through crowdsourcing efforts than ever before.

The future of data science will also be shaped by developments in automation technology, including AI assistants like Siri or Alexa. These technologies allow us to interact with computers in new ways. For example, they can understand natural language input like commands or questions and provide answers quickly without requiring us to learn programming languages.

Conclusion

The future of data science is an exciting one. We’ve already seen some incredible advancements and more to come. Now is the best time ever to enrol in data science courses and build a career for a digital future.

Imarticus learning offers a Certificate Program in Data Science and Machine Learning to guide and train you with the best resources to prepare you for this data journey.

Get in touch with us and find a detailed analysis of how this program can potentially revamp your career. Contact us through chat support or drive to our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, and Gurgaon for more information.

Learn Python Online: Things that non-programmers must know

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Learn Python Online: Things that non-programmers must know

The developer of Python, Guido V Rossum, created it as a hobby project back in 1989. Today, Python has become one of the few official programming languages of Google due to its ease of use paired with efficiency. Python won the position of most popular programming language in 2020, as put forward by a joint survey of Google Trends and GitHub. In terms of programming language usage by global developers, Python has secured the third position in 2021, following JavaScript and HTML/CSS. Are you a non-programmer? Then Python can be your ideal starting point. And, here are a few things that you should know about this programming language.

Python: What is it?

It is an object-oriented, high-level programming language. It is a general-purpose, interactive programming language, the name of which is based on Rossum’s favorite TV series, “Monty Python’s Flying Circus.” The language is known for its versatility and simplicity, making it ideal for beginners and non-programmers.

What are the features of Python? 

Do you want to learn Python? Here are a few features to know about.

It can be integrated: 

Python can be integrated with an array of programming languages like C, C++, and Java.

Python is interpreted, and interactive:

Python can be processed by interpreters while they run it and a learner does not need to compile the program before execution. It is interactive because you can interact with the interpreter directly.

It is object-oriented:

Python is an object-oriented programming language and has code reusability. Designing an application with Python is easier with minimal coding.

Ideal beginner’s language

Python is characterized by the simplicity of structure, clear-cut syntaxes, and less number of keywords, thus making it simple to learn for any non-programmer. Furthermore, it is an open-source software freely accessible to all.

Why learn Python?

Tons of career opportunities:

The popularity of Python has contributed to the rising demand for Python programmers. Certification in data analytics can promise you an array of career choices.

Active community:

Program learning is not a solo sport. A massive community for providing the required learning support is vital to proper learning. Python has a considerable community backup which helps non-programmers in their learning.

Highly versatile:

It is a highly adaptable programming language that is used in multiple fields like web development, data science, software designing, and gaming. Python’s versatility is further enhanced by its ability to sync with a variety of other programming languages. Thus, it is a ‘cross-platform’ language.

Which fields use Python?

Python’s simplicity and ease of use have granted it access to a variety of fields like:

  • Web applications
  • 3D CAD-based applications
  • Software development
  • Auditory applications
  • Machine learning
  • Visual applications
  • Artificial intelligence

 Tips to keep in mind

Now that you know what Python is and what its features are, here are a few tips to keep in mind before opting for certification in data analytics:

  • Being consistent in code-making always helps to learn better. Are you making mistakes in coding? They are the stepping stones to your proper learning. So, code every day!
  • An interactive Python shell can be your ideal learning tool.
  • Opt for the pair programming technique, where you will pair with another developer. This way, you both will switch between writing codes and reviewing and problem-solving. Now, you can learn both sides of Python.
  • Opt for data analytics certification courses online, which can offer you the best possible resource to learn Python faster.

Conclusion

Are you looking for data analytics certification courses online? If yes, then you must check out the one offered by Imarticus Learning. With a fully online learning mode, this course is ideal for future data science experts and is backed by case studies, real-life business projects, and mentorship sessions.

Beyond The Hype: Learn Computer Vision

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Beyond The Hype: Learn Computer Vision

Computer Vision is a subfield of artificial intelligence. One day it’ll be our go-to method for identifying objects and faces in the world around us—one way or another, if you’ve ever tried Google Photos’ facial recognition feature, it’s where computer vision work. It uses mathematical models to identify people’s faces in images (and tag them automatically). This blog post will explain what computer vision is and why you should learn it.

Why should you learn computer vision?`

It’s a highly sought-after skill, and it’s easy to understand because computer vision is a practical skill that you can use in real life. Not only that, but it’s also an interesting subject matter with lots of potential for growth and development. 

Some apps already use computer vision we use every day—like Snapchat filters, which use face-tracking technology to make you look like a cat or dog when taking photos with friends.

What is the application of computer vision?

You can use computer vision in countless applications, including:

  • Image recognition involves the ability to identify objects or scenes within an image, as well as the ability to track things over time. In the coming years, you could use these technologies in retail stores where customers can scan their items at a self-checkout station and then be billed immediately by their mobile phones—no need for cashiers!

  • Image classification involves categorizing images into different groups according to what they depict (e.g., dog vs. cat) or what part of the human body they represent (e.g., face vs. hand). For example, Amazon uses image classification algorithms on its website to automatically understand which products are shown in each image so that when you search for something similar online. It can give you suggestions based on previous searches made by other customers who clicked through from Google Images links before coming back later with those same results still ready for consumption! 

It also helps retailers build better recommendation engines based on user feedback after making purchases or browsing various pages on eCommerce sites like Amazon’s own interface. But these systems aren’t perfect yet because they require millions upon millions of training data points before making accurate predictions about new ones that may contain unusual patterns not seen before. 

How can you start learning computer vision as a beginner?

There are many resources for learning about computer vision. The best way to start is by understanding the basics of computer vision and then learning about the different algorithms used in computer vision. You can also learn how these algorithms work.

You can use many resources to learn more about computer vision, including books and online courses.

Learn AI and Machine Learning courses with Imarticus Learning.

This deep learning Artificial Intelligence credential brings together the E & ICT Academy, IIT Guwahati, and the most prominent industry specialists. This AI course will help students prepare for careers as data scientists, data analysts, machine learning engineers, and AI engineers.

Course Benefits For Learners:

  • Students can now participate in 25 real-world projects to get industry experience and prepare themselves for a fascinating future in data science.
  • Impress employers and demonstrate your talents with an E & ICT Academy, IIT Guwahati, and an Imarticus Learning-endorsed certificate.
  • This IIT artificial intelligence course will help students land lucrative jobs in the disciplines of artificial intelligence and machine learning. 

Contact us through chat support, or drive to our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, and Gurgaon. 

Moving beyond handcrafted architectures in supervised learning

Posted on by Imarticus Learning

Moving beyond handcrafted architectures in supervised learning

There was a time when handcrafted architectures were the norm in supervised learning. But those days are behind us now, and we are moving towards more automated methods. This blog will discuss why handcrafting is no longer the best approach for supervised learning tasks.

What is a handcrafted architecture in supervised learning, and why is it used?

A handcrafted architecture is a predefined structure used to guide the learning process in supervised learning. It simplifies the learning process and improves generalization. While handcrafted architectures are effective, they can also be limiting.

Recent advances have led to the development of more flexible and powerful architectures that can be learned automatically from data. These architectures are often more effective than handcrafted ones, as they can learn to exploit the regularities in data that are most relevant for the task at hand.

Some of the reasons why handcrafting are no longer the best approach for the supervised learning task.

  • One reason is that handcrafted features are often low-level and do not capture high-level abstractions necessary for many tasks. 
  • Another reason is that the handcrafted feature space is often limited and does not allow for the use of more powerful learning models such as deep neural networks. 
  • Finally, handcrafted architectures are often designed for a specific task and do not generalize well to other tasks. 

Thus, it is clear that handcrafted features and architectures are no longer the best approaches for supervised learning tasks.

How does the move to artificial intelligence impact supervised learning?

There has been a shift away from traditionally handcrafted architectures in supervised learning towards more automated machine learning approaches in recent years. It is the increasing availability of data and computing power that has allowed for the development of more complex models.

There are numerous benefits to using machine learning for supervised learning tasks. Machine learning models can automatically learn features from data, improving performance. In addition, machine learning models are often more robust to changes in data than handcrafted architectures.

There has been a significant shift in building supervised learning models in the past decade. We’ve moved from primarily using handcrafted architectures to a more data-driven approach. It is mainly due to the success of deep learning in various tasks such as image classification, object detection, and natural language processing.

However, deep learning is not the only machine learning approach to achieving state-of-the-art results. Many other methods, such as support vector machines, decision trees, and random forests, can also be very effective. AIML is a course that will teach you about these other methods and how to apply them to different tasks.

Discover AI and Machine Learning courses by Imarticus Learning

The E & ICT Academy, IIT Guwahati, and the most significant industry experts come together in this Artificial Intelligence and Machine Learning certificate. This artificial intelligence course will assist students in preparing for data scientists, data analysts, machine learning engineers, and AI engineers.

Course Benefits for Learners:

  • Prepare for a fascinating career in data science by completing 25 real-world projects that give you industry expertise.
  •  Impress employers and display your skills with an E & ICT Academy, IIT Guwahati, and an Imarticus Learning-endorsed certificate.
  •  This machine learning and artificial intelligence course will assist students in seizing lucrative job possibilities in the hot fields of artificial intelligence and machine learning.

Guide To Computer Vision: Why Does It Matter?

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Guide To Computer Vision: Why Does It Matter?

The use of computer vision in the actual world is expanding, challenging, and redefining its meaning. Computer vision is becoming an increasingly prevalent word in conversation across the globe and industries: computer vision systems, software, computer vision hardware, computer vision development, computer vision pipelines, and computer vision technologies. This blog post will teach you about computer vision in detail. 

What is Computer Vision?

Computer vision is a field that utilizes images to analyze and interpret objects, scenes, and motions. The difference between computer and machine vision is that the latter does not rely on visual information alone but instead processes signals from other sensors such as infrared or ultrasonic sensors.  

You can use computer vision in a variety of applications, including:

  • Robotics: A robot equipped with computer vision technology will interpret its environment better than before, allowing it to avoid obstacles while navigating around objects.
  • Autonomous Vehicles: Advanced driver assistance systems (ADAS) use computer vision algorithms to identify road signs and alert drivers if they need to take action based on what they see (e.g., a stop sign). ADAS can also detect pedestrians or cyclists who may cross their path while driving at high speeds.
  • Security Systems: Modern cameras can detect intruders by recognizing faces or body shapes through infrared cameras; even though these systems aren’t perfect yet—they’re still pretty helpful in preventing crimes from happening nearby businesses.

How Does It Work?

Computer vision is a subsection of machine learning and artificial intelligence. The distinction between these three terms can be confusing at first, but they’re not that complicated once you understand each one. Let’s start by looking at how computer vision differs from machine learning and deep learning.

Machine learning is how computers learn to perform tasks based on data provided by humans or other sources. Deep learning refers to the use of artificial neural networks (ANN) to solve problems in visual recognition, speech recognition, natural language processing, and reinforcement learning—and it’s responsible for making recent breakthroughs in AI possible!

Why Does Computer Vision Matter?

Computer vision is the future, and it will change how we interact with machines. It will change the way we interact with each other. 

Computer vision is important because it’s an essential component of autonomous vehicles, which will be critical for reducing traffic-related deaths on roads. Deep learning is a subset of artificial intelligence, a subfield of computer science.

The future looks bright for computer vision. It’s a fast-growing field with significant potential to change the world, and it will have a considerable impact on our lives and society.

Explore AI and Machine Learning courses with Imarticus Learning.

This deep learning Artificial Intelligence credential brings together the E & ICT Academy, IIT Guwahati, and the most prominent industry specialists. This AI course will help students prepare for careers as data scientists, data analysts, machine learning engineers, and AI engineers.

Course Benefits For Learners:

  • Students participate in 25 real-world projects to get industry experience and prepare for a fascinating future in data science.
  • Impress employers and demonstrate your talents with an E & ICT Academy, IIT Guwahati, and an Imarticus Learning-endorsed certificate.
  • This IIT artificial intelligence course will help students land lucrative jobs in artificial intelligence and machine learning disciplines.

Contact us through chat support, or drive to our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, and Gurgaon. 

It’s time to improve your procurement skills with a supply chain analytics certification

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It’s time to improve your procurement skills with a supply chain analytics certification

The supply chain is a field that comprises a massive amount of data to be handled. In today’s world, leveraging big data in the supply chain is next to impossible without the help of data analytics. Analytical methods use data analytics to collect the data and process it to improve operational efficiency and effectiveness. Taking up a supply chain analytics course plays a vital role in providing decision-making skills to the management at operational, tactical, and strategic levels.

Analytics cover sourcing resources, manufacturing the products, distributing, and ensuring logistics. Therefore, supply chain management careers and posts such as supply and operations planner, data scientist, and demand planner are in high demand. If you have a basic understanding of operations management and are interested in supply chain management, this course is for you.

Why choose us for this Supply Chain Analytics course?

At Imarticus, the Professional Certification in Supply Chain Management and Analytics is a supply chain analytics course exclusively designed by subject experts who have meticulously drafted the curriculum to meet the rising demands of the industry.

The curriculum has been approved by the most prestigious IIT Roorkee, which arms the candidate with the potential to pursue a supply chain management career. We ensure that you get the right mentor to guide you in your education. The course also offers you practical exposure with seminars, workshops, and industrial visits to help you gain experience.

What does the course exactly offer?

This supply chain management course imparts the critical skills needed to attain mastery in the field. Extra credits provided in the course provide flexibility to increase the weightage of the certificate. The course at Imarticus provides the students with supplemental readings, practical problems, and opportunities to create an interactive and collaborative learning environment.

Taking up this supply chain analytics course provides the candidate with immense knowledge of the latest data analytics approaches to support an organization in making data-based decisions. The course is for people who would like to integrate their supply chain management knowledge with the current data analytics system. 

What makes this Supply Chain Analytics course such an in-demand course?

A supply chain analytics course throws light on the ways to collect, evaluate, and improve the quality of supplier-to-consumer metrics and KPIs and support strategic organizational objectives and decision-making.

Supply chain analytics also plays a role in conducting the functions mentioned below, which makes it one of the most in-demand courses in the market:

  • Enhance risk management by detecting current known risks and forecasting future risks
  • Improve planning accuracy by examining data to identify factors that cause a rise or fall in demand
  • Refine order management by collecting data sources to predict demand, assess inventory levels, and detect fulfilment issues
  • Streamline and organize procurement by allocating budgets and analysing expenditures across several departments
  • Increase working capital by making robust models – this helps determine the inventory levels needed to accomplish service goals with minimum capital investment

Students can build data-driven projects in various domains, including competitive analysis, demand forecasting, distribution, and logistics.  

After the supply chain analytics course, the student will receive a certificate validating their expertise in the subjects covered in the curriculum, enhancing their resume and attracting prospective employers’ attention. The supply chain would require data analytics since customer demand increased with each passing day. To meet the diverse demand of the customers and function effectively, it is essential to increase the production and supply chain with a focus on analytics. 

What would you be able to achieve from the Supply Chain Analytics course?

  • Understand and apply business analytics tools to optimize the company’s performance 
  • Gain practical experience to conduct efficient procurement functions
  • Pick the right supply chain strategy to create a competitive advantage in the market
  • Become well-versed with descriptive analytics, predictive analysis, and prescriptive analysis

This course has a lot more to offer, including computer languages such as Python and Tora. Our alumni currently work in the supply chain and logistics departments of Amazon, FedEx, SAP, Nestle, Johnson & Johnson, etc. 

Learn more about the Professional Certification in Supply Chain Management and Analytics from IIT Roorkee 

Unsupervised V/S Supervised Learning: The Ultimate Tech Battle 

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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!

How NLP Becomes Suitable For The Mass-Market

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How NLP Becomes Suitable For The Mass-Market

Modernization has changed our lives significantly, leading to changing needs and demands. It impacts businesses as they need to update and upgrade their products and services based on market trends. It is challenging to keep track of changing consumer demands and continue launching suitable products and services. Thus, businesses keep looking for technologies, tools, methodologies, and strategies to enhance customer and market understanding and grow their business successfully.

The industrial revolution has brought remarkable transformations in our everyday life and work. Emerging technologies are enhancing our capabilities for efficient and effective business performance. These technologies work on the data collected from various sources to extract meaningful, hidden, helpful information from structured and unstructured data. Natural Language Processing, Artificial Intelligence, and Machine Learning are some of the trending technologies in the digital world. 

Natural Language Processing (NLP) is computer-aided human language processing, including written and spoken language. It is used to either understand the language or generate the language. Artificial Intelligence and Machine Learning are used to analyze and extract meaning from the vast amount of collected data. Businesses collect data for various aspects of the company, like processing, operation, product design, technology, and marketing. NLP is suitable for companies targeting mass-market if it has:

  • Access to vast amounts of market data
  • Experts to develop cutting-edge technologies
  • Computing capacity to process the collected data

How is NLP Suitable for the Mass Market?

NLP is used to translate, generate, and understand language and emotions used in various formats like speech, text, audio, and video. Marketing has been one of the most important criteria for a successful business. Hence the suitability of NLP for marketing can be understood by its applications for business marketing, as discussed below:

  • Sequence classification: It assigns the text sequence to one of multiple previously defined classes of information. For example, information may provide emotions like joy, anger, sadness, sorrow, etc. The marketing team can determine the emotions behind feedback, review, and discussions about the product/ services on social media. Based on this information, the team can create strategies and plans to improve its sales and make the product satisfy consumer needs and demands.
  • Question-Answer models: It gives a content-correct answer to questions based on information available from the text corpora. The marketing team can use it to automate responses on chatbots or over customer service calls.
  • Text generation: It matches the word based on given text to predict another word or lengthy text accurately. It enables text generation for multiple languages based on the target group and their linguistic dialect.
  • Phrase recognition assigns one or more words in a sentence to a class, also called Named Entity Recognition (NER). 

Future of NLP Technology

NLP is the most researched domain in the artificial intelligence field. It will have advancements with more profound understanding and developments. In addition, there will be general-purpose and specialized models for language processing helpful for a wide range of businesses. The field will evolve rapidly, and thus demand for experts, professionals, and NLP technology career aspirants will increase drastically.

The latest developments in NLP technology are fast and impressive, and universities worldwide include this technology in syllabi. In addition, various training and education institutes such as Imarticus Learning Pvt. Ltd. are providing certification programs in this field to generate next-generation professionals.

Artificial Intelligence and Machine Learning Programs

If you are looking for machine learning certification courses, Imarticus Learning Pvt. Ltd. has partnered with leading institutions and corporations to help enthusiasts learn AI. Choose from among nine certification courses in Artificial Intelligence and Machine Learning, among others.

All of these courses are designed with the help of the E&ICT Academy, industry leaders, and IIT Guwahati to train aspiring machine learning certification program learners. It will help you to become Data Scientist, Machine Learning Engineer, Data Analyst, Machine Learning Architect, Data Science/ Machine Learning Consultant, and AI Engineer.    

This course develops fundamental skills and a practical understanding of diverse industries’ theoretical backgrounds and real-world projects. You can learn job-relevant skills with 25 in-class real-world projects under the guidance of world-class academic professors. The course includes:

  • Live instructor-led sessions,
  • Online discussions with mentors & peers,
  • In-class projects & exercises,
  • Assignments & assessments,
  • Capstone projects,
  • Hackathons, and
  • Career support.

It helps you unlock your career path with lucrative Artificial Intelligence and Machine Learning opportunities. Therefore, if you would like to get details, contact Imarticus Learning Pvt. Ltd. through chat support or visit training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, and Gurgaon. 

Learn to tackle supply chain issues with tools like digital inventory management with an IIT supply chain management course

Posted on by Imarticus Learning

Learn to tackle supply chain issues with tools like digital inventory management with an IIT supply chain management course

It’s essential to understand supply chain management in the business world. This is especially true if you want to start or expand your business. Even if you already have a grasp of the concept, it’s crucial to stay up-to-date on the latest tools and technologies that can help improve your supply chain. That’s where an IIT supply chain management course comes in! In 2022, you will be able to take an online course that will teach you everything you need to know about managing your company’s supply chain.

What is digital inventory management, and how does it work?

Digital inventory management is a system that helps businesses keep track of their inventory using computerized methods. This can include barcodes, RFID tags, and other tracking technologies. By keeping track of inventory digitally, businesses can save time and money while reducing errors.

There are many benefits to using digital inventory management, including:

  • Reduced time spent on inventory management
  • Increased accuracy of inventory data
  • Easier to track and manage inventory levels
  • Reduced costs associated with inventory management

If you’re interested in learning more about digital inventory management, consider taking an IIT supply chain management course. This course can help you understand the skills you need to manage inventory using digital tools effectively. You can help your business run smoothly and efficiently with the proper training. 

How do we tackle common supply chain issues with the help of digital inventory management?

Supply chain management can be a complex and daunting task for many businesses. There are many moving parts involved, and if any one of them goes wrong, it can have a domino effect that disrupts the entire system.

One of the most critical aspects of supply chain management is inventory management. This ensures that enough raw materials and finished products are available to meet customer demand.

Digital inventory management is a tool that can help businesses keep track of their inventory levels in real-time. You can then use this information to make decisions about production, purchasing, and other aspects of the supply chain.

IIT’s Supply Chain Management course will teach you how to use digital inventory management to tackle common supply chain issues. The course is for business professionals who want to learn more about this vital aspect of the business.

If you wish to learn how to use digital inventory management to improve your business, sign up for IIT’s Supply Chain Management course today!

Discover Professional certification in supply chain management with Imarticus Learning

The online supply chain management course provides cutting-edge information to those interested in taking the operations or supply chain analytics course. Students who enroll in this online supply chain management program will learn about Demand Planner, Data Scientist job titles.

Course Benefits For Learners:

  • We immerse students in relevant technologies and projects, such as six real-world projects that will give them a solid understanding of the sector to prepare them for a successful future as certified Supply Chain analysts. 
  • Students who finish a supply chain management online course can prepare for highly sought-after professions such as Demand Planner or Data Scientist, which are in high demand among businesses today! 
  • Students will learn to become data-centric and improve Supply Chain decision-making by utilizing the Python programming language.
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