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Category: Analytics

Supervised Learning: It’s Not As Difficult As You Think

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Supervised learning is a concept that has been around for decades, but it’s still not as widely understood as other machine learning techniques. This post will help you know what supervised learning is and how you can apply it to your own projects.

What Is Supervised Learning?

Supervised learning allows you to predict the value of a target variable based on an input variable. The input variable, called the feature variable, indicates or classifies future data points concerning their labels. The label refers to whether or not something falls under one category or another; in this case, we’re predicting whether or not our training set will match up with future samples.

Machine learning differs from classical programming because it uses algorithms instead of instructions for how the program should work. We get more flexible, powerful, and capable programs than ever!

Benefits of Supervised  learning

The training data will give you a clear sense of the classes. You can easily comprehend the process of supervised learning. Unsupervised learning makes it difficult to understand the inner workings of the computer, how it learns, etc.

Before providing the data for training, you can determine the precise number of classes. You may train the classifier in a way that has a perfect decision boundary to precisely discriminate between distinct classes, allowing you to be very exact about the description of the classes. You don’t necessarily need to retain the training data in your memory once the entire program is through. Instead, you can stick with your choice.

What Are Some Practical Use Cases For Supervised Learning?

You can use Supervised learning in many real-world applications. Image recognition, natural language processing, and financial forecasting are just a few examples of supervised learning being used to solve problems we all encounter daily. Supervised learning is a powerful tool that you can use in many different fields. It has been around for a long time, but not many people know because it’s not as flashy as machine learning or deep learning. 

Supervised learning is also challenging to start because there are so many different techniques and algorithms that you need to know before you can use them effectively. But with all this said, supervised learning is still one of the best ways to learn!

Learn supervised learning with Imarticus Learning.

Attend the E & ICT Academy deep learning Artificial Intelligence certificate program to discover how to become an AI engineer. This IIT AI ML Course will help students prepare for careers as data scientists, data analysts, machine learning engineers, and AI engineers.

Course Benefits For Learners:

  • To get practical industrial experience and prepare for a fulfilling data science career, students work on 25 real-world projects.
  • You may amaze employers and demonstrate your talents with a certificate authorized by the E & ICT Academy.
  • This machine learning course will help students land lucrative jobs in machine learning and artificial intelligence.

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

A Step-By-Step Guide To Tableau Charts

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A Step-By-Step Guide To Tableau Charts

Tableau is a platform for data visualization that enables you to develop interactive dashboards, reports, and data visualizations. The tool’s straightforward drag-and-drop interface makes it simple to link your data and create stunning images. Tableau offers many different types of charts and graphs. You can create them using Tableau’s standard data visualization tools or with more advanced features.

What Is Tableau?

Tableau is a business intelligence (BI) software that allows you to create data visualizations easily and quickly. With Tableau, you can visualize your data in various formats like maps, charts, and graphs. You can go from raw numbers to fully fledged reports within seconds. 

corporate leadership

Learning Tableau charts can help you make visual data more engaging. It’s a great way to share your findings with the world and make them easier to understand.

Tableau allows users to create interactive visualizations from their data without writing code. Users can create dashboards, reports, and other interactive visualizations with Tableau.

Tableau charts show data in an easy-to-understand format. You can use them in conjunction with traditional charts like bar graphs and line graphs to show the relationship between sets of data points.

Here are some steps that will help you create a great chart with Tableau:

First, you need to identify the data table. You can do this by using the “Get External Data” option in Tableau Desktop or the Get External Data button on the View menu of Tableau Server. In either case, select the table you want to analyze and click OK.

Once you have identified the data table, it is time to create your visualization. To do this, double-click on your data source in Tableau Desktop or select New Visualization from the View menu in Tableau Server. You will be prompted for some information about your visualization (like whether or not it should be interactive) and then given a chance to choose between different types of visualizations.

Once you have chosen which type of visualization you want to create, go ahead and fill out any other optional settings, such as whether or not your visuals will get linked and how many times they should get refreshed each second (this is important if there are any delays between updates from live data sources). 

Learn tableau course with Imarticus Learning

In this tableau course, a student will learn to become a data analyst in the real world and create complex models that produce essential business insights and forecasts.

Course Benefits for Learners:

  • Students will learn Data analytics, machine learning fundamentals, and the most widely used data science tools.
  • To receive a tableau certification, students must finish 25 real-world projects and case studies directed by corporate partners.
  • One of the sought-after abilities in the market today is the ability to visualize data utilizing a Tableau program. Therefore, recent graduates and those just starting their careers might consider enrolling.

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

The role of a supply chain analyst in the logistics and supply chain department

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The Role of a Supply Chain Analyst in the Logistics and Supply Chain Department

The business industry is growing every day and consequently, it is opening several job opportunities. If you are aiming to enter this industry and you have great critical thinking skills along with a strong knowledge of communication, then you can develop yourself to become a supply chain analyst. 

best supply chain management and analytics courses

As a supply chain analyst, you use your critical thinking for the growth of your company. For this role you need to have a very intricate idea of the industry that you are associated with and also you have to actively use your problem-solving skills very often.

To become an expert all you need to do is to have the right course from the right place. Imarticus is all set with its Professional Certification in Logistics and Supply Chain Management Course where you can get the right guidance to make a career in supply chain management.

What is a Supply chain analyst?

A supply chain is an essential part of a business that maintains the relationship between the company, supplier, and customer. The main responsibility of a supply chain analyst is to improve customer services by looking after the company’s supply chain and designing it efficiently under the company budget.

Mainly larger companies hire for this role as their supply chains need regular monitoring and maintenance. This role has a high demand in the manufacturing, retail, e-commerce, government, and food industries. 

If you are opting for this role, you are supposed to look after the logistics system of the company along with the order placement, transportation, and delivery process. In case of any problem in any of these sections, you have to deliver a quick solution without making the company face extra costs.

In simpler language, a supply chain analyst ensures that the company meets the customer demands in no time, without paying extra. So, your responsibilities also include the task of maintaining the supply chain of the company smoothly under a stipulated budget. 

Role of a Supply chain analyst

Supply chain analysts have several responsibilities to perform for their company such as,

  • Designing materials to manage and gather data on the company supply chain.
  • Determining how to manage to ship more cost-effectively.
  • Maintain a good relationship with the vendors. 
  • Analyzing the supply chain to detect malfunctions before they cost any harm to the company.
  • Increase the revenue by cutting extra costs and negotiating the best deals with the partner companies.
  • Have better communication with the supply chain management team.
  • Work closely with procurement, logistics, IT, and other departments to discover concerns and give solutions for that to make the delivery process smoother.
  • Ensuring that corporate policies and procedures are being followed.

The responsibilities may vary according to the needs of the company but these are the most common expectations that the employer may have from the person they are appointing for this role.

Skills required

The skills that may give you better opportunities in the field are, 

  • Knowledge of mathematics, analytical skills, and logical reasoning.
  • A distinct understanding of all the processes included in a supply chain system and maintaining the data on relevant programs like Microsoft Excel.
  • Strong communication skills to negotiate with the vendors and other parties involved in the process. 
  • Foresee problems and suggest required solutions before it gets harmful to the company.
  • The ability to search for the best vendors suitable for various projects. 

Conclusion

The supply chain industry has spanned the industries like automobile, retail, consultancy, e-commerce, customer packaged goods, and so on. It has a higher demand for supply chain analysts, but to make a career in supply chain management it is important to have a relevant degree and skills which you can acquire through the Imarticus logistics and supply chain management course. The top faculty and industry-relevant curriculum will help you achieve the skills and knowledge you need.

5 mistakes to avoid while working with Tableau

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5 mistakes to avoid while working with Tableau

Tableau is a powerful data visualization tool that can help you make better decisions and improve your business. But what is a tableau, and why is it so important?

Its business intelligence software allows you to see and understand data in new ways. For example, tableau can easily create stunning visuals that help you see patterns and trends in your data. This can be extremely helpful in making decisions about your business.

Learn Tableau, as it can help you answer questions like:

-What are the most critical trends in my data?

-Which parts of my data are most important to me?

-What conclusions can I draw from my data?

Tableau can also help you create visual representations of your data that are easy to understand and share with others.

It is very user-friendly, even for people who are not data experts. This is because tableau makes it easy to connect to data sources and create beautiful and informative visuals.

Using tableau in decision support includes quickly and easily visualizing data, understanding relationships within it, identifying trends and patterns, and assessing risks and opportunities. Tableau can also be used in planning processes by helping managers see how individual decisions may impact the organization as a whole.

Before you get started, keep the following pointers in mind

5 mistakes to avoid in tableau

  1. Making too many forecasts – Tableau is a data visualization tool, not an investment modeling or forecasting tool. As such, don’t over-forecast your data or try to model every outcome in your dataset. Your table will become cluttered and less effective overall. Instead, try to focus on one or two key metrics you want to analyze while leaving the rest of the data untouched.
  2. Focusing exclusively on numeric values – Tables ensure powerful visualizations. Still, they can be incredibly misleading if they rely only on numerical values without other supporting information (e. g. labels, visual representations). Instead, try to use other visualizations (e.g., bar charts) and data types (text, pie charts) to help supplement your table’s information and better communicate your findings.
  3. 3. Ignoring axis labels – Tableau will automatically create column headers based on the names of each field in your dataset. Still, you can also add additional text labels to make the axes more visible and easier to understand. Label every axis, so everyone who looks at it understands what they see.

The problem with ignoring axis levels is that it can make it difficult to understand how the data relate. For example, if you have two axes (say, Sales and Cost), it’s easy to see which column corresponds to which axis by looking at the labels on the y-axis (Sales) and x-axis (Cost). However, if you only have one axis level (say, Country), it’s much harder to see which column corresponds to which axis.

  1. Not linking fields – When two or more fields in a dataset are related (e.g., a column that is the result of grouping multiple fields), Tableau will create a link between them so that users can quickly see how those fields are related. Make sure you add appropriate links between your data fields so that readers can see exactly what information is displayed in each cell.
  2. Creating duplicate data – One common mistake table creators make is to include duplicate data within their tables – copying the same values across different cells or even across other rows and columns. This duplication can be visually unpleasing and make it more difficult for readers to understand your data and make informed decisions. Remove any duplicate data from your tables before you upload them to Tableau.

Overall, Tableau is an incredibly powerful tool that can help you boost your business. If you haven’t already started using tableau, now is the time. Learn Tableau

Imarticus Learning offers a postgraduate data analytics program that covers all the concepts of Tableau to help you get skilled. Click Here to know more about it.

Contact us through our chat support on our website, or walk into our training centers and get yourself enrolled in a data analytics course with placement and have a bright future.

The future of data-driven companies: What we know

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The future of data-driven companies: What we know

Data is the fuel through which the digital age advances itself. With the advent of artificial intelligence, it became all the more crucial to rely on data. At present, it is equivalent to any valuable resource available on earth. Even more so, the data science industry saw a steep rise in talent after the recent pandemic struck most of the earth’s emerging data giants.

So what about data is so appealing to the industry expert? Researchers maintain that in the next few years, we will enter a new phase of the data-driven industry where data will be available at a single click or, better, at a single thought. Data can manifest itself in numerous areas and be helpful in the long run. Artificial intelligence claims a good percentage of engagement and employs it further into the system to develop more proficient and unique results. It is safe to assume that a career in data science is a good choice at this point. Moreover, opting for a data science online training program can be a practical step toward building a career with futuristic goals.

Perks of Being a Data Scientist

The main benefit of opting for an IIT data science certification course is that it introduces you to all available niches of the data science industry. In response, you are more confident in your approach to the subject. The toughest of the toughest seem more accessible to you. Besides taking care of the coder in you, the data science industry also opens you up to a bucket load of opportunities. You will find a few of the advantages mentioned in this list.

  • An All-rounded Approach: Data science excels in the field of business marketing, web-retailing, healthcare, city planning, data-driven analytics, and logistics primarily. It is the backbone of these industries. The top tech giants recruit data scientists globally, keeping the diversity open to versatility.

  • Danger Replacement: The data industry has tried its best to replace manual labour with automation, machine learning, artificial intelligence, and robotics. It is for the best since not all job roles provide an exemption from dangerous equipment.

  • Time Management: It is also true in terms of tedious working hours. Computers have been doing the task of reducing manual workload since the dawn of the digital age. Data Scientists do just that with artificial intelligence.

  • Generous Compensation: Data science is one of the top recruitment areas for the top recruiters in the current market trend. Thus, you must consider a data science certification course soon.

What Can We Expect From Data Science?

Data has its unique characteristics. Industry experts suggest that data will be seeing a proper articulation of its features in the next few years. Hence, choose a data science online training course that caters to the best of your interests to understand it better. Some of the future aspects of a data-driven ecosystem are discussed below.

  • Data can be embedded in decision-making, interaction, and information processing.
  • Data can be processed and delivered in real time.
  • Data will not pile up or take more storage but will be stored in usable storage instead.
  • An entire ecosystem of data will be up and going.
  • A data operating model will treat data the same way as a product.
  • Data is likely to be automated to maintain privacy and security.
  • Data can be well-managed for resilience and will not depict instability.

 Conclusion

The job market for the average data scientist is blooming and oversees countless scopes. With popularity so gleaming, it is safe to assume that data science is one of the top-notch careers available. Its futuristic approach can quickly take over any evolving expertise and provide cut-throat competition to other sectors. An IIT data science certification course would be the right place to go for a career in data science.

10 Things To Consider For Your Data Analyst Certification Course

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10 Things To Consider For Your Data Analyst Certification Course

Data Analyst certification is essential in advancing your career as a data scientist. It shows you have the knowledge and skills to work with data effectively while helping organizations make better decisions. To become a certified data analyst, you must take the proper steps to start this journey!

How to Become a Data Analyst?

Data analytics is a lucrative career. It will grow faster over the next decade because it offers many different opportunities for people who want to work from home or go into business for themselves as consultants or freelancers. Data analytics is one of the industries in today’s economy with the most significant growth.

Things to consider for your data analyst Course:

Degree and Domain Expertise

To be a data analyst, you need to have a degree in computer science, statistics, math, or a related field. You also need to demonstrate domain expertise in a particular industry or field. In addition, your education must include understanding the terminology used in this field and how you can apply data analysis to problems within that industry.

Knowledge of Data Analysis Tools

Data analysis tools are a must-have for anyone who wants to become a data analyst. It’s essential to know how to use these tools, especially if you’re going to use them in real-life situations. 

They allow users without experience analyzing large amounts of data at once (or even small amounts) to access their insights without knowing how exactly they work or what results they’ll produce when used correctly. This means that anyone can become an effective data analyst no matter what level of expertise they have already reached within their career, as long as they have access to specific software packages designed specifically for this purpose!

Knowledge of Data Visualization Tools

Data visualization is a crucial part of the data analyst job. It helps you understand your data better, makes it easier for others, and allows you to communicate your findings clearly and effectively.

The best way to learn about visualization tools is by using them yourself—the more experience you have with this kind of software, the better equipped you’ll be when it comes time for certification. Many different visualizations are available online: from simple charts like bar graphs or pie charts to elaborate networks built by algorithms on massive datasets.

Understanding of Statistics and Machine Learning Algorithms

You might wonder what statistics and machine learning do with data analysis. Statistics is the science of collecting, interpreting, and presenting data. Machine learning is a subfield of computer science that uses statistical techniques to allow computers to “learn” without being explicitly programmed. It’s often described as artificial intelligence (AI) function because it mimics human thought processes by building models from large amounts of data.

Learn Data Analytics with Imarticus Learning

In this tableau course, students will learn how to use data science in the real world and create complex models that produce essential business insights and forecasts.

Course Benefits for Learners:

  • Data analytics, machine learning fundamentals, and the most widely used data science tools and approaches should all be familiar to students.
  • To receive a tableau certification, students must finish 25 real-world projects and case studies directed by corporate partners.
  • One of the sought-after abilities in the market today is the ability to visualize data utilizing a Tableau program. Therefore, recent graduates and those just starting their careers might consider enrolling.

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

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

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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.