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10+ Mind-Boggling Facts You Can Learn In Artificial Intelligence As A Fresher

Posted on by Imarticus Learning

10+ Mind-Boggling Facts You Can Learn In Artificial Intelligence As A Fresher

With the rapid development in the technology sector, artificial intelligence has already become a part of our everyday life.  A lot of big tech companies have become involved in this development and have already created their unique assistance system. 

For example, Amazon has Alexa, Apple has Siri and Microsoft has Cortana. Although we have some ideas on what AI can do, you will also become surprised if you find out about some specific skills of AI. You can learn AI by enrolling in a machine learning and artificial intelligence course.

In this article, we will discuss the mind-boggling facts about artificial intelligence that you need to know:

Play games

Artificial intelligence can learn and play games like poker, chess, and Go (which is an amazing feat in itself). Moreover, AI can not only play these games smoothly but they can also compete with fellow human beings as well as defeat them in games.

Debate

Through the development of IBM’s Project Debater, we can see that artificial intelligence also can participate in complex debates with human beings and become successful at it. Furthermore, it can conduct research on different topics, craft counter-agreements against human opponents, and produce an engaging point of view.

Create

Different creative processes can also be mastered by artificial intelligence which includes writing poetry, taking photographs, creating visual arts, composing music, etc. The AI of Google was also able to create an AI child of its own that has surpassed its human-constructed counterparts.

Read human minds

Another mind-boggling fact about AI is that it can truly read your mind. Artificial intelligence can interpret your brain signals and then create a speech using those signals. This is a truly impressive feat of AI that can help differently-abled people. For example, this ability of AI can be life-changing for those who have a speech impairment (because of the mind-reading aspect of the skill). To capitalize on the mind-reading potential of AI, big tech giants such as Meta and Elon Musk have created projects that can harness this skill.

Understand emotion

Currently, AI tools that can read people’s emotions are being used for market research. These AI tools can track and gather data from people’s emotions. The tools use a person’s body language, voice, facial expression, etc., and evaluate it against the emotion database to find out what kind of emotion the individual is expressing. Based on their expression, AI can also find out what their action will be.

Listen and understand

Artificial intelligence can analyze and detect the sound of gunshots and warn the relevant agencies about the same. This is one of the most fascinating things AI can do, it can hear and evaluate different types of sounds. People also like the response of digital voice assistants when it comes to asking for a weather report or managing minute things. The convenience, accuracy, and efficiency provided by digital voice assistants are phenomenal.

Speak

Artificial intelligence also can speak and interact with human beings. Oftentimes, it is also helpful and fun when Google Maps or Alexa answers your queries and assists you by providing you with directions. Google Duplex uses artificial intelligence to schedule appointments and finishes every task over the phone using a conversation tone or language. It also can answer accurately to the human it is talking to.

Vision

Artificial intelligence can also see and analyze visual data using machine vision and it also can make proper decisions. There are multiple ways machine vision is being utilized today, for example, payment portals, self-driving cars, facial recognition, etc. Machine vision also helps out in the manufacturing process which is by enabling product quality control process and predictive maintenance.

Read

There is artificial intelligence that can find out the salient features from any sources and summarize them for your usage. Be it news articles, emails, legal documents, web links, books, images, audio files, etc. can be summarized with the help of artificial intelligence and the specific points can be reported back in the form of essential information. 

Currently, this particular feature of artificial intelligence is being used in slack or Facebook messenger. This particular feature of AI is dependent on machine learning, blockchain technology, and natural language processing.

Write

Nowadays professional news organizations and the journalism industry such as Reuters, Washington Post, The New York Times, etc, are utilizing artificial intelligence for writing. The creating ability of AI is being utilized to create different formulaic pieces depending on ‘who, what, when, how, and where. 

A lot of marketers are using artificial intelligence to create artistic social media posts. Not to mention, even a novel produced by artificial intelligence has been selected for an academic award.

Move

Artificial intelligence is being used in different robots and drones for autonomous movement. For example, Tokyo’s national theatre has a robot that can generate autonomous movement.

Artificial intelligence will probably surpass our abilities in a lot of different fields and if you are interested to learn AI and becoming a part of this emerging industry then you should opt for an artificial intelligence course today.

5 must learn programming language for data science and machine learning professionals

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5 must learn programming language for data science and machine learning professionals

Learning programming languages is the first step in becoming a data scientist or machine learning expert. You should be familiar with several programming languages for your practical work and self-learning. This post briefly overviews the top must-know programming languages for data science and machine learning professionals.

R Programming Language

R is open source software, free of charge, released under the GNU General Public License. The latest stable version is R 3.4, with minor updates released every six months. There are also many packages on CRAN (Comprehensive R Archive Network), which provide additional functionality when working with data sets in R itself. It is used extensively in academic environments to teach statistical methods and to develop statistical software.

Python

Python is a programming language for web development, data analysis, and machine learning. It’s also one of the most popular languages to learn as a beginner, thanks to its simple syntax and readability.

It is a high-level programming language with dynamic typing that makes it easier to write programs using fewer lines of code than other languages like C++ or Java. The syntax is not complicated, so you can learn how things work without getting lost in technical jargon or complex grammar rules that don’t apply in real-life situations.

SQL

It is a structured query language used to create, read, update and delete data in a database. The SQL statements are written in English sentences or commands and separated by semicolons (;).

SQL has been for many years, and several variations exist across different databases, including MySQL, PostgreSQL, Oracle, etc. 

Scala Programming

Scala is a modern general-purpose programming language designed to express common programming patterns in a concise, elegant, and type-safe way. It is an immaculate language with an expressive syntax that makes it easy for developers to work with large amounts of data.

Java Programming

Java is a general-purpose, concurrent, class-based, object-oriented computer programming language designed to have as few implementation dependencies as possible.

This language is among the most popular in the Data Science industry. It has many advantages over other languages. It’s easy to learn and still provides excellent performance when solving complex problems with Machine Learning algorithms.

Discover Data Science and Machine Learning Career with Imarticus Learning.

With this certificate program in data science and machine learning, students may begin their careers in data science. Through this curriculum, students will grasp the principles of data science and machine learning and get the knowledge and skills they need to apply these ideas in the real world.

Course Benefits For Learners:

  • This five-month program, developed by IIT faculty members, will instruct learners in using Python to comprehend data mining and machine learning methodologies.
  • This data science certification course will be live via online sessions with India’s best educators.
  • Students will build a strong foundation in data science with the aid of our data science online program.

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

Data Analytics Course for Beginners

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What is Data Analytics?

Data Analytics is defined as the process of analysing data sets to find new trends and draw conclusions about the information they contain. 

Initiatives including data analytics can support a company’s attempts to boost customer service, optimise marketing campaigns, and generate revenue. Analytics also allows companies to respond quickly to changing market trends and gain an advantage over competitors. But improving corporate performance is data analytics’ ultimate objective. 

Depending on the particular application, data may be analysed from new information that was processed for real-time analytics or from historical records. It might also originate from internal systems and from outside sources.

Data analytics analyses data sets to identify emerging trends and make inferences about the information they contain.

Initiatives including data analytics can support a company’s attempts to boost customer service, optimise marketing campaigns, and generate revenue. Analytics also gives companies the ability to respond quickly to changing market trends and gain an advantage over competitors. But improving corporate performance is data analytics’ ultimate objective. 

Why Learn the Basics of Data Analytics with a Data Analytics Course?

  • Demand has increased by 400%

The need for Data Scientists has increased dramatically due to every organisation placing significant bets on data analytics to boost business value.

  • Lucrative salary

The average salary for Data Science roles is 10LPA+, according to Glassdoor.

  • Love for math and programming

Data Analytics course is a heady mix of math, statistics, and programming – it can’t get more cutting edge than this.

How can you pursue further information on data analytics?

You can work in one of the fastest-growing industries and one that is constantly evolving and seeking out fresh insights if you have a strong foundation in data analytics with a data analytics course.

If you are interested in studying data analytics, you can learn online and balance work and study.

Opportunities aren’t simply restricted to working for data science organisations. Jobs are now accessible in various sectors, including health, transportation, finance, entertainment, and construction, as demand for data science specialists has skyrocketed.

Why Imarticus?

You may have found yourself in uncharted territory because of how work changes. You might be expected to perform more tasks. A faster pace of work may be required of you. As a result, you may worry about your outdated skills. We can help you refresh current skills and embrace new ones, so you stay in demand.

Imarticus Learning is an expert in online training. We are constantly updating our programs to stay current with the latest trends and technologies so that you can learn at your own pace with the help of our expert trainers. 

Over a decade, we have impacted over 10,00,000 careers through leading-edge curriculums, highly experienced faculty, and over 500 global partnerships with leading institutions and corporations. Imarticus Learning seeks to upskill existing and future workers to fulfil various industries’ current and upcoming job market demands.

Imarticus Learning has successfully helped thousands of students get into leading multinational companies and start-ups and has helped in the career transition of more than 45,000 students across the globe.

In the financial year 2021-2022, we have placed a record of 1841 students, which means “1 student was placed every 4.75 hours“.

8 out of 10 students of Imarticus Learning get placed in industry-leading firms like JP Morgan, KPMG, Morgan Stanley, Goldman Sachs, HSBC, BNP Paribas, etc.

We are associated with over 480 companies, including most of the Fortune 500 companies.

Start your learning journey in analytics with Imarticus. Our premier data analytics course will teach you about the latest developments in the data science industry and equip you with the practical and theoretical knowledge that an expert data scientist must possess.

Deep learning vs machine learning

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Deep learning vs machine learning

Machine Learning and Deep Learning are two approaches to building AI that have generated a lot of buzz recently, both among tech companies and on university campuses worldwide. But which kind of AI should you focus on? Should you opt for a Deep Learning course or a Machine Learning certification? The answer lies in your career aspirations and the type of projects you want to work on. In this article, we’ll break down the differences between machine learning and Deep Learning and discuss when you should use each type of technology based on your career goals and interests.

What is Deep Learning?

Deep Learning is a branch of machine learning that models high-level abstractions in data and understands complex data with multiple levels of representation. Deep neural networks have been successfully applied to supervised and unsupervised problems and can be used as feature detectors or classifiers. Thus they are capable of performing inference in higher layer neural areas. They have been used on large-scale problems for information retrieval, speech recognition, and computer vision, producing results comparable to humans.

What is Machine Learning?

Machine Learning makes computer software more accurate in predicting outcomes without being explicitly programmed. Instead, ML relies on statistical techniques, including regression and classification. It allows computers to learn from past data and predict future events based on those learnings. The basic idea behind machine learning is that you use algorithms to train your system to recognise patterns in your data. Once you’ve trained your system, you can use it for prediction tasks such as forecasting demand for products, recommending products or services, identifying potential customers, and detecting credit card fraud. 

The difference between Deep Learning and Machine Learning

Deep Learning refers to a subset of Machine Learning algorithms. Multiple layers of nonlinear processing units characterize it for feature extraction and transformation. In contrast, Machine Learning refers to any form of Artificial Intelligence in which a program ingests data and learns from it. There are two types of machine learning algorithms – Supervised and Unsupervised. Supervised learning requires input features and their desired output values, whereas unsupervised learning doesn’t require any desired output values but uses input features only. 

Challenges in learning Deep Learning and Machine Learning

Deep Learning is rapidly evolving, with breakthroughs in neural networks being published frequently. But at its core, it is just another type of Machine Learning, albeit one that has proven to work very well on many problems. So why use Deep Learning vs other forms of Machine Learning? If a problem can be solved using linear methods and the output benefits from taking advantage of an entire nonlinear pipeline, it would probably be best to stick with that method. However, many interesting problems—like detecting complex patterns in images or text—are hard to express as equations but are easy for humans to make intuitive sense.

Deep learning allows us to take advantage of our intuition about how we want these problems solved. Deep Learning also provides a way to learn representations for data automatically, which is especially useful when there isn’t a precise mapping between input and output. Instead of building those mappings manually, we let our model figure out what features matter most, making Deep Learning ideal for applications like computer vision, where feature engineering is difficult or time-consuming. The critical question here is – is lots of labelled data needed? 

Challenges in the future

Artificial intelligence is taking off, with new developments advancing every day. It’s no longer a matter of if AI will be a part of our lives but rather when. Machine Learning is currently happening—and fast. The advancement of Deep Learning research has brought us close to computers that can learn how to learn, such as AlphaGo and IBM Watson. Deep Learning is challenging many professions, raising concerns about robots and machines replacing jobs. As with any technological advance, we need to consider many pros and cons before diving into a future powered by artificial intelligence. So let’s look at some areas where Deep Learning is transforming business today. 

Applications of Deep Learning

Deep Learning has a broad range of applications, including information processing, various forms of data mining, and knowledge discovery. It is also used for fundamental studies on understanding natural language processing and further help in semantic parsing. Self-driving cars and robots are other fields where Deep Learning plays an important role. It enables computers to master many complex problems without being explicitly programmed to solve them—and sometimes even without being told what they are supposed to accomplish. Applications of Deep Learning include image recognition systems like Google’s image search; speech recognition systems like Apple’s Siri; natural language processing systems like Facebook’s automatic tagging system; recommendation engines like Amazon’s product recommendations; autonomous vehicle control systems like Tesla’s autopilot mode; medical diagnosis systems like IBM Watson etc.

Conclusion

Deep Learning and Machine Learning seek to help computers think and make decisions differently. Deep Learning seeks to replicate human brain function, whereas Machine Learning emphasizes efficiency. Deep Learning and Machine Learning differences are subtle yet essential in determining when you should use each. In addition, while they may seem similar on paper, some critical distinctions between these fields can affect their implementation in real-world applications. 

If you want to build an enriching and fulfilling career around Deep Learning and Machine learning, the best way is to learn artificial intelligence from experts. CERTIFICATION IN ARTIFICIAL INTELLIGENCE & MACHINE LEARNING offered by Imarticus Learning. It is an advanced industry-approved program designed by E&ICT Academy, IIT Guwahati, for future data scientists and machine learning engineers. For any queries or guidance, contact us through chat support, or visit our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, or Ahmedabad. 

10 Reasons Why Data Analysts Should Learn A Hadoop Course

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10 Reasons Why Data Analysts Should Learn A Hadoop Course

If you’re planning to become a data analyst or already working as one, then learning Hadoop can be very fruitful for your career. Keep reading to know why you should learn Hadoop online! 

It is 2022, where we are surrounded by revolutionizing technologies and gigantic quantities of data from all directions. Almost all industries of this modern world are receiving massive quantities of unstructured data from sources like emails, social media platforms like Facebook and Instagram, etc. which eventually results in Big Data. When it comes to analyzing this data most efficiently and cost-effectively, nothing can beat Hadoop. This is the reason why data analysts and Hadoop make the most powerful combination in the industry of data science. 

10 Reasons Why Every Data Analyst Should Learn Hadoop  

Technologies like Hadoop and Big Data are taking the world by a huge storm. They are slowly becoming the most revolutionizing technologies in the world. So, if you want to keep up with the trends and build a successful career in data analytics, you must learn Hadoop. 

Hadoop is the Entrypass for Big Data Technologies 

Hadoop is the most efficient technology for solving any minor or major Big Data problem. Companies from all over the world are spending a significant amount on building the best data analytics team. The deal is that Hadoop has a large ecosystem that offers powerful data analytics tools like Zookeeper, Pig, Hive, Sqoop, HBase, MapReduce, etc. 

Note that each of these tools solves a wide spectrum of Big Data problems. It doesn’t matter what new technology comes in the future, Hadoop is a strong pillar of strength that will not lose its importance over many years to come. 

Increasing Demand for Hadoop Professionals 

Believe it or not, there is a serious scarcity of Hadoop professionals in the world. Adding to it, the demand for such talents is increasing faster than ever. This is the single biggest reason to learn Hadoop. But the good thing is, that you can easily learn Hadoop online to eliminate this gap in the industry. You can even join a separate Hadoop training program alongside the best data analytics certification course. 

High Salaries 

With the increasing demands for skilful Hadoop professionals, their salaries are also increasing. In other words, the data analysts with a strong hold on Hadoop are earning much higher than their colleagues with no Hadoop certification. 

Better Career Scope

No doubt, Hadoop offers a much-elevated career scope. If you don’t already know, Hadoop has a wide ecosystem of tools that help in Machine Learning, Batch Processing, Stream Processing, etc. which help in landing the below-mentioned job profiles:

  • Hadoop Developer 
  • Data Scientist 
  • Data Analyst 
  • Big Data Architect
  • Hadoop Admin
  • Hadoop Administrator

Another good thing about Hadoop is that it provides job opportunities for both experts and freshers. So, whether you are already a data analyst or building a career in data analytics, this technology can be a game changer for your career. 

Say Bye to Complexities

Dealing with huge batches of data can be very complex. Thanks to technologies like Hadoop and Python as they make data handling very easy and convenient. 

Disruptive Technology

Hadoop is a very flexible and versatile technology. This simply means it can easily process all kinds of structured, semi-structured, and unstructured data (for ex: MySQL, XML, Images/Videos, etc. respectively). Moreover, Hadoop also provides much better data warehousing resources in terms of scalability, cost, storage, as well as performance. 

Better Employment Opportunities

If you want to accelerate your career as a data analyst, then it is very helpful to get Hadoop certified from a reputed institute. Because more and more companies are looking for Hadoop professionals, the quality of training and certification has also increased. 

Wide Range of Domains 

Hadoop is not limited to any one domain. Instead, it is widely used and adapted by a huge spectrum of industries. Be it healthcare, transportation, retail, or media, almost every industry is leveraging the powerful capabilities of Hadoop. When you’re a certified Hadoop professional, you can build a career in any industry you want. 

Lucrative Freelancing Opportunities

Another perk of learning Hadoop is, that it opens doors for lucrative freelancing opportunities. You can always have a high-paying side job as a Hadoop developer or administrator along with any other full-time job. You can even become a complete freelancer after mastering this skill. 

Widely Adopted by Top Organizations

Hadoop is widely adopted by the top organizations in the world. It is mainly used to determine market trends, correlations, customer preferences, and other useful information. This is another reason behind the increasing demand for Hadoop professionals in companies. 

Conclusion

Hadoop is a game-changer when it comes to data processing. It single-handedly makes the data processing environment efficient, productive, and cost-efficient. Looking at the increasing demand for Hadoop professionals, learning this technology can greatly support your career in data science. So, along with the best data analytics certification course, consider taking a Hadoop certification as well. It will be the best thing you can do as a successful data analyst. 

Why data science certification courses are gaining popularity

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Why data science certification courses are gaining popularity

Date Science was coined in the 1960s to interpret data and make statistical sense of it. Back then, there was also almost no focus on using the branch of study for predicting future trends. 

However, the field of data science has seen massive advancements over time, making it one of the most promising domains in terms of job prospects and career growth. Businesses generate huge volumes of data daily that must be analyzed and interpreted. This highlights the importance of having high-quality data and understanding how to analyze it to make data-driven business decisions.

So, how did this happen? Why are so many people interested in learning data science today? Why is there such a buzz around data science certification courses? Let’s find out the answers to all these questions!

Who is a data scientist?

A data scientist is somebody who makes use of statistical techniques and programming languages along with industry knowledge to translate datasets into meaningful information. This information derived from huge volumes of data lays the foundation for various critical business decisions which can be used for workforce planning, production capacity, marketing strategies, etc. It’s not just about analytical skills; data scientists also combine the best social skills to uncover trends. Additionally, they must have excellent communication skills to communicate their data-driven insights to the organisation accurately.


Why are data science certification courses gaining popularity?

Several factors contribute to the increasing popularity of data science courses not only in India but also around the world. The following are the main reasons why a majority of people are subscribing to them:

Lucrative salaries 

Data science job roles are highly lucrative. Even entry-level data science positions pay nearly Rs. 7 Lakh per annum. In contrast, the country’s median annual salary for data science professionals stands at Rs. 16.8 Lakh. According to a recent Analytics India Magazine report, around 1,400 individuals employed in data science roles in the country earn more than Rs. 1 Crore annually!

Helps develop meaningful skills 

Not everything you learn for a profession applies in real life; however, this is not the case with data science. The analytical and decision-making skills you develop to become a data scientist are pretty practical and helpful in day-to-day real-life scenarios.

High job security 

With the advent of emerging technologies, many professionals risk being replaced by advanced technologies and software. However, the nature of the job of data science professionals is such that they cannot be easily replaced with technology even after the significant evolution of Artificial Intelligence as they serve as the fundamental link between human interactions and technology.

Surging demand 

There is no denying that data is the new oil for the world. This is highly evident in the growing demand for data scientists. Alone in India, the demand for professionals in the data science field has seen a yearly increase of 30.1% in April 2022. 

Steep Career Growth 

Data science knowledge not only helps you grasp data insights but also equips you with decision-making skills, which prepares you for critical leadership roles in the organisation in the future and adds a steep upward trajectory to your career graph.

 

How does Imarticus Learning’s Certificate in Data Science Program help you?

Imarticus Learning’s Certificate Program in Data Science and Machine Learning is a 5-month introductory course meant to equip you with the basics of the field. Under this course designed by the IIT faculty, you learn how to mine data along with mastering Python programming for using ML tools. It also introduces you to the world of SQL and Tableau. 

The course has been scheduled on weekends in an online mode. Therefore, it is conveniently accessible to individuals from all walks of life, be they students or working professionals looking to enhance their skill sets.

Wrapping up

Students, recent graduates, working professionals, or retired individuals who wish to learn data science and make a career out of it can enrol in our weekend-special fundamental course and upskill themselves. Data science has immense opportunities to offer both in terms of monetary and professional growth. You can pursue it as a full-time working professional or even take it as a highly-rewarding freelancing gig.

You can even combine it with your existing skill set in your current role, as no field today is untouched by data and its impact. By doing this, you will not only earn a handsome salary, but you will also remain in demand when nearly all operations become tech-savvy in the future.

Are you looking for professional advice? Feel free to contact us through chat support or visit our nearest training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, or Ahmedabad.

How supply chain careers are changing over time

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How supply chain careers are changing over time

Supply Chain Management refers to the management of the entire life journey of a product or service, from selecting its raw materials, transporting them for manufacturing, to delivering it to the final customer.

It is an essential part of every business, serving as a vital link for the intersection of the demand and supply forces. An efficient supply chain not only helps keep the business’ operational costs in check but also minimises the wastage of resources, time, and efforts of the involved human resource and technologies.

If you like planning and organising from the beginning to the end, consider doing a supply chain management and analytics course to build a successful career in the supply management field. It equips you with supply chain planning and introduces you to the world of supply chain analytics, which helps you make critical strategic decisions.

There are various supply chain management career options to choose from per your interest and skill set. Popular profiles include Purchase Managers, Material Analysts, Strategic Planners, Warehouse Managers, Logistics Analysts, and others. Over the last decade, all supply chain planning roles have significantly evolved on account of technological improvements and the digitisation shift.

The Future of Supply Chain Management Careers 

Following are supply chain management careers, including the changes expected to further evolve in the future – 

Production Managers

A production manager plans, coordinates, and supervises all production-related activities of a supply chain. Over the years, they have been responsible for ensuring production according to demand and their delivery to the next stage in the supply loop. 

However, with technological advancements, their role has evolved drastically. In many technology-driven companies, a production manager works with collaborative robots instead of human labour, for example, in automobile production. And to work efficiently with these robots, it is required that the production manager possess the programming skills necessary, as, when needed, they must reprogram robots to meet dynamic demand requirements. 

Unlike before, production managers nowadays also work with IoT-equipped appliances, requiring them to understand augmented analytics. All these changes are modeling production managers to become technicians of the future.

Logistics Manager 

Logistics management plays a vital role in a supply chain. Over the years, logistics managers have been responsible for setting up and managing a network of suppliers and retailers, so the business can optimally serve its target audience. They have also been actively involved in inventory management and arranging goods transportation. 

However, today, the role of a logistics manager is no longer limited to these activities. The role has become more meaningful with the emergence of analytics and automation. Today, a logistics manager can automate low-value work like inventory management and packaging of goods to focus on more attention-requiring areas like customer satisfaction. 

Using advanced analytical tools, a logistics manager can customise delivery channels for different customers, exceed their delivery expectations by delivering goods via drones, and leave an ever-lasting impression on customers, beneficially impacting the business.

Strategic Planners 

Planning is the first step in setting up a supply chain circuit. For years, the supply chain planning role has revolved around charting and experimenting with different supply chain planning options to accurately and timely meet the demand for goods and services. 

However, today, a strategic planner uses advanced technologies like Machine Learning to plan optimal consumption of resources, aligning them cost-effectively with the demand. 

This change has brought a shift in the business areas impacted by the role of strategic planners. They are no longer limited to only sourcing but have gained the power to impact finances and resource utilisation significantly, unlike before.

Final Words

The field of supply chain management is evolving and will continue to grow with the emergence of new and improved technologies. We understand the importance of analytics in this emerging set-up and thus offer a Professional Certification in Supply Chain Management and Analytics, which provides the cutting-edge in the field with skills for making data-backed decisions for managing supply chains of all sizes and complexities.

Want to know more about the supply chain management and analytics course and other options in a supply chain management career? Contact us through chat support or visit our nearest training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, or Ahmedabad.

Why Joining A Data Analytics Certification Course Should Be Your Top Priority?

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Why Joining A Data Analytics Certification Course Should Be Your Top Priority?

Today the world is driven by data. From small to large businesses, data rules the terms everywhere. Although it may sound cliche, the truth is that data is an asset. Why is that so? Why has data gotten so much exposure lately? Particularly in the light of AI and machine learning, data has become unquestionably significant. Let us take an example to understand the matter.

Why is data significant?

Assume ABC is a private company that sells pharmaceutical goods. During the Covid-19 lockdown, ABC sold around 5000 masks in a quarter. From 2020 March onwards, it has seen a fall in the number of masks sold. However, it has witnessed a rise in the number of hand sanitisers sold. 

Now, this is a generalised statement. You can say this as the inference drawn from overall business. What ABC would be interested in is getting a detailed report of the sales. If there is a fall in the number of masks sold, then which are the areas where it dipped the most? In the case of hand sanitisers, which are the regions with high demands for the product. These aspects can only be answered by data. Proper data maintenance and analysis will enable ABC to prepare a detailed business report. 

So you can see that data allows a business to run on numbers and figures and not on assumptions and predictions. This makes business more profitable. A thorough analysis furnishes the areas of opportunity clearly to the management.

This is a reference case study. In reality, big businesses depend entirely on data analysis. And not only business, but other fields are reliant on data massively. This, in turn, creates an ocean of opportunities for data analytics. Let’s see now why a data analytics certification course should be your top priority.

Learn Data Analytics

We are often advised to take up a course on data analytics online training. Have you ever wondered why people are so keen to learn data analytics? What are the key benefits of mastering data analytics? 

Any and all aspects of data analysis fall under this broad category of data analytics. A data analyst assists a company in building a large database for regular use, from management to storage. This sector is becoming even more dynamic with the introduction of emerging technologies. The following are the highlights of joining the best data analytics certification course. 

Data Analytics as a career

We have already seen how data analytics helps business firms and organisations with proper data structuring, data management, data storing, and data prediction. A business always requires accurate data and therefore the demands for sound data analysts are always high in the market. Other avenues are dependent on data as well. Thus, if you learn data analytics today, you will have the best job opportunities lined up for you tomorrow.

Job opportunities outside India 

Western countries adapted to data analytics much earlier. Today, they are working with big data. Big data, to put it simply, is a collection of data with varying levels of complexity, volume, and mobility. This requires in-depth knowledge of data analytics. Companies such as Apple, Google, Facebook, etc. employ the best data analysts from different parts of the world. Thus, you will be rewarded with the most lucrative job opportunities in foreign countries if you enrol in the best data analytics certification course.

Data analytics as the backbone of IT and non-IT hubs

Data analysis gives you opportunities you never would have thought about. Big IT hubs across the world are highly dependent on data analytics. On the other hand, if you think of a non-IT field, say, for instance, banking is also counting on data. For example, a bank would go through rigorous data analysis before it rolls out a new scheme in a particular area. What was conventionally referred to as a ‘survey’ in non-IT fields has become data analysis today. You can say it is the most scientific approach to perform an extensive survey based on available information.

New technologies and data analytics

One of the major advantages of data analytics is that it embraces new inventions with open arms. The intervention of new tools and new processes enriches the field and take it forward to the next phase. Many fields have become stagnant due to rigidity and lack of flexibility. However, data analytics is an agile and adapting field. This is what makes data analytics a super-sustaining field. 

Data Analytics Online Training 

If you join a data analytics certification course, you will acquire an in-depth knowledge of the subject. You will get to work with the tools of the trade, You will learn new programming languages and techniques that will polish you as a professional. Think of it as the phase when you keep collecting and storing miscellaneous weapons in your armoury. This will be handy at the time of job interviews. The more you are well-versed with contemporary tools and techniques, the higher will be your demand in the job market. 

Therefore, start your data analytics course today for a secure and prosperous future. 

Best tips and hacks to Learn machine learning with python

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Best Tips and Hacks to Learn Machine Learning with Python

Learning ML (Machine Learning) is one of the most exciting things you can do with Python. It is one of the most challenging because there are many different types of problems and algorithms to learn. Still, with a bit of effort and practice, you’ll be able to get started with machine learning in no time! This blog post will cover easy ways to get started—from coding basics like variables and loops through linear algebra and probability theory to data visualization techniques. 

Learn to code in Python

As the title says, learn to code in Python. This is an essential step for any programmer and should be the first thing you do when learning a new language. Learning to code in Python will give you an understanding of what goes into software development and how everything works together, including variables, loops, functions, classes & objects, etc.

Keep your machine learning libraries updated.

When you’re learning machine learning, it can be tempting to try out a new library and see what it can do for you. But if your library isn’t up-to-date with the latest releases and fixes, that might cause problems later on when trying to troubleshoot issues in production environments or even on your computer. You should keep your machine learning libraries updated so they work as expected and don’t cause any errors when trying out new features. 

Follow a good machine learning blog.

A good machine learning blog is excellent for learning more about the field, finding inspiration and examples, and getting data. Machine learning blogs contain a lot of helpful information about how algorithms work. They also allow you to explore different approaches or ideas to improve yourself. 

Learn and practice data visualization techniques.

There are several ways to visualize data, but the most effective way is to use a combination of tools. Data visualization is important because it allows you to understand how your machine learning algorithm works in real time, making it easier to debug problems as they arise.

Learn linear algebra, probability, and statistics.

Linear algebra, probability, and statistics are all part of machine learning: linear algebra studies vector spaces and linear mappings between such areas. Probability is the study of random variables and their distributions (e.g., if you know that a coin is fair, then you can use this knowledge to predict what will happen next). Statistics is data collection, organization, analysis, interpretation, and presentation; it includes descriptive methods like histograms or scatter plots and inferential methodologies like regression models.

Explore a career in Machine Learning with Imarticus Learning.

With this machine learning course with Python, students may begin their careers in data science and machine learning. Through this curriculum, students will grasp machine learning principles and get the knowledge and skills they need to apply these ideas in the real world.

Course Benefits For Learners:

  • This five-month program, developed by IIT faculty members, will instruct learners in using Python to comprehend data mining and machine learning methodologies.

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

Why learn artificial intelligence

Posted on by Imarticus Learning

Why Learn Artificial Intelligence

Is artificial intelligence (AI) hyped or reality? Why is every tech enthusiast learning artificial intelligence? That’s because AI adoption is increasing, and the market is about to reach $190 billion by 2025. In addition, AI technology will increase global GDP by $15.7 trillion by 2030. So, AI is not hyped.

 As per a study, artificial intelligence (AI) can increase business productivity by 40 percent. That’s why almost all sectors are adapting to artificial intelligence (AI), which has increased job opportunities across the globe.

Do you also want to join the world of AI but don’t know what it is? How to start learning AI and the career opportunities in AI and machine learning? Read the blog below to know everything about artificial intelligence (AI).

 Let’s understand what is artificial intelligence.

 Artificial Intelligence (AI) is a simulation of human intelligence processes by machines. With the help of AI, machines can think, learn, and function like humans. Various elements are used for creating artificial intelligence, including machine learning, big data, neural networks, and natural language processing. The importance of artificial intelligence has become vital for the modern world due to its various applications in industries. In short, AI help in saving time doing work.

 What are the 4 types of artificial intelligence (AI)?

 Reactive machine

This type of AI does not use memory or experience for decision-making. It just follows the current scenario and acts accordingly to inform the present decision. 

 Limited memory

 It resembles the reactive machine AI, but historical data is the only add-on. Most devices that we use today are examples of limited-memory AI. This type of AI stores a large amount of data as information and uses this as a reference to make predictions.

 Theory of mind

 AI, which understands human emotions, behavior, and thoughts, is the theory of mind AI. However, machines using the theory of mind AI is a future project yet to be used till now.

 Self-aware

 Building self-aware machines that can generate representations of themselves is the final step in AI development. It’s a development and expansion of the theory of mind artificial intelligence. 

 What does an artificial intelligence professional do?

 The mission of an artificial intelligence professional is to create an intelligent algorithm capable of analysing and predicting future events. Their role is to develop machines that can reason like the human brain.

 As a result, the AI specialist is also a researcher who studies the functioning of the human brain to create computer programs with the same cognitive abilities as humans.

 Who can learn AI?

 Anyone interested in artificial intelligence, data-driven, and familiar with programming languages like R, Python, etc., can learn AI. Developers, analysts, information architects, and analytics professionals can quickly learn AI and build a successful career in the growing field of AI.

 How to learn AI?

 Individuals who want to learn artificial intelligence and pursue this as a career have too many options. AI online and offline classes are readily available. In addition, there is an option of learning AI for professionals or students who cannot attend classes on weekdays by joining the weekend batches. Learning AI includes programming languages like Python, and R. Learning deep learning and machine learning are also included in the AI curriculum. The best way is to start learning the advanced mathematics concept and then move on to the coding language. 

 Why learn AI?

Every day humans generate 2.5 quintillion bytes of data. So we do need AI-enabled machines to manage and process this massive data. Not only this, but AI-powered machines are also widely used in sectors like healthcare, farming, natural disaster, etc. Now, you can imagine the demand for AI, which creates the need for artificial intelligence professionals. 

 An Analytics India Magazine (AIM) and Imarticus Learning study show that AI/ML engineers and big data analytics jobs indicate a maximum upward swing. As per the report, the business analyst role has the most jobs in this data science and AI sector, with 38,974. 

 There are approximately 34,566 data engineer job openings, 19,457 data scientist job openings, 10,564 deep learning job openings, and 2,214 AI/ML engineer job openings.

 Skills an AI professional needs.

The artificial intelligence (AI) sector has tremendous job opportunities and requires many skills and competencies. First and foremost, the desire to know the data insights and knowledge of algorithms, advanced mathematics, and statistics is a must for any artificial intelligence or data science professional. 

 Good knowledge of programming languages like Python is also required to play with the data. In addition, industry knowledge and the latest trends in artificial intelligence and machine learning will be very advantageous. 

 Communication and presentation skills play a vital role in climbing the ladder of the artificial intelligence domain.

 The preferred qualification for artificial intelligence professional

Artificial intelligence profile hiring managers prefer postgraduate degrees in mathematics, statistics, and computer application. In addition, knowledge of programming languages like Python, R, and Scala is very important for predictive analysis. 

 The career path of artificial intelligence (AI) professional

After completing the artificial intelligence course, you can apply for the job roles below.

  1. Artificial intelligence (AI) engineers: They create the artificial intelligence model and make stakeholders understand it.
  2. Machine learning engineers: They design, build and manage machine learning software applications using data.
  3. Big data engineers: They create systems to communicate and collect data.
  4. Data scientists: They collect data, interpret it and predict information.
  5. Data analytics: They analyze data, mine insights, and utilize the information for decision-making.

 So what do we know about artificial intelligence (AI)?

We have explained what is artificial intelligence. A step-by-step guide to learning artificial intelligence and who can learn artificial intelligence. The career path of artificial intelligence professionals. It will help individuals who want to learn artificial intelligence and pursue a career in the field. You can contact us if you are looking for some good courses and programs in artificial intelligence and machine learning. Imarticus Learning offers the best artificial intelligence and machine learning course, designed by E&ICT Academy, IIT Guwahati.

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