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Category: Analytics

What are the essential components of exceptional supply chain management?

Posted on by Imarticus Learning

At the most basic level, Supply Chain Management oversees the entire production process, from procurement of raw materials to manufacturing them into goods to finally selling them in the market. This binds an organisation together by keeping both the suppliers and producers in a loop.

Supply Chain Management is very much in demand in recent times because of the rapidly evolving industry. This kind of management helps organisations to reduce costs by streamlining the entire production process and at the same time, making huge varieties of products available to the customers according to their needs. There are several courses that provide certifications for supply chain professionals. 

The major tasks of an efficient Supply Chain Management team involve the following- 

  • Material handling
  • Service creation
  • Fulfilment of the order
  • Information tracking
  • Product lifecycle management 
  • Supply chain planning

Essential Components of Supply Chain Management

  • Efficient Planning- You must make a plan in advance to satisfy the distributor’s or the customer’s needs. To keep ahead of demand, preparedness is essential, even if your company just makes up a small portion of the supply chain. Planning becomes vital in this situation. You must ascertain the impact your company has on the supply chain before deciding how to preserve efficiency while providing your customers with continuous value. Inventory levels are automatically updated as items enter and exit a warehouse, and the information is updated. From there, you may examine the info, predict demand and order the products or resources required to satisfy future consumer demands.
  • Low-cost sourcing- Making decisions about who gets what is a component of Supply Chain Management. You must supervise order transactions, control inventory movement, approve payments and occasionally send out advanced shipment alerts when there is an anticipated delay even in the warehouse or distribution centre.
  • Modern technology usage- It is crucial to keep an inventory to have a highly successful Supply Chain Management system. An inventory is a ready list of the things, supplies and other necessities needed to make a good or provide a service. To distinguish between available stock and required stock, this list must be updated frequently. The ability to produce and sell products is impossible without effective inventory management, hence it is essential to the operation of Supply Chain Management. Currently, businesses are beginning to pay greater attention to this component due to its influence on production.
  • Efficient production and delivery- Your company needs a supply chain to guarantee the traceability and authenticity of every item if it deals with any form of food, drink or chemical. For example, Supply Chain Management makes sure that the ingredients required to make a box of cereal have been accepted, processed, shipped, stored and delivered following stringent guidelines.
  • One option to automate this part of the supply chain from a warehouse perspective is by employing barcodes for validation and traceability. Supply Chain Management and logistics are conjoint. It’s critical to coordinate orders, pick and pack, plan deliveries, invoice, dispatch, notify and collect payments. Every exchange along the road needs to be closely watched to ensure there are no mistakes or delays, even when using third-party courier systems.
  • Reverse logistics- Building networks to facilitate and speed up product returns is another aspect of Supply Chain Management. Finding a new use for the goods includes considering options like reproduction, scraping or putting them back in the warehouse. Supply Chain Management involves being adaptable and satisfying the needs of the customer, in addition to adjusting to changes in supply and demand.
  • Grasping trends accurately- A constant flow of information controls the world today. A company must keep up with all the most recent information regarding the numerous facets of its production to be successful. If the information is effectively and promptly communicated throughout the different levels of the organisation, it will be easier to understand the market patterns of supply and demand for a specific product. In a knowledge-based economy, information is essential, and a business’s prospects may be doomed by ignorance of any business-related topic.

Supply Chain Management Course

Nowadays, Supply Chain Management is a very popular course. To be successful in this dynamic, ever-changing industry, businesses need teams with adequate supply knowledge to help them and guide them. This field provides huge growth opportunities because of being highly demanded.

The IIT Roorkee Supply Chain Management course is provided by Imarticus. Their professional certification in Supply Chain Management and Analytics is an ideal program one can choose to become a supply chain analyst. This program provides you with live classes by top professionals and industry experts, hands-on learning experience with real-life Supply Chain Management project work, and an IIT Roorkee alumni status.

This certificate course will help you gain job-relevant skills and will help you take strategic decisions. It helps you with impressive learning and project portfolios that will help you in the placement process. If you want to take up a Supply Chain Management course, this is your ideal choice. 

Conclusion

Supply Chain Management is a crucial part of the dynamic business world. It deals with the most crucial aspects of a business. Hence, proper training courses must be taken up to be able to contribute to the success of an organisation. The right course like that of Imarticus will provide you with an accurate skill set and proper training, helping you get attractive offers. To succeed in Supply Chain Management, one must make decisions that will shape the course of a business, so it is advisable to take a certificate course to have hands-on experience.  

Why is the supply chain a principal driver of business growth in 2023?

Posted on by Imarticus Learning

A supply chain is a network of people, resources and organisations involved in creating a product for sale. This chain-like system starts by sourcing the raw materials, turning them into valuable products, and finally making them ready for sale.

In recent times, the supply chain has evolved by streamlining the process of making goods available to consumers at a much lower cost. This has increased the living standards of society as a whole. The entire process of manufacturing and producing goods has become a whole lot easier. The supply chain keeps the group of suppliers and producers connected and managed efficiently. 

There are huge growth opportunities in this field as jobs are increasing with each passing day. To become a supply chain analyst, more people are showing an interest in this field and looking for the best supply chain management courses to pursue. 

There are many ways in which the supply chain is evolving in this ever-changing world – 

  • They are making it easier for the consumer to purchase products from a store or online websites. 
  • They are incorporating a lot of climate-friendly measures in the production of goods. They are making sustainable environment-friendly goods to attract climate-conscious consumers.
  • They are constantly adopting new practices of sourcing raw materials and also trying out new suppliers to bring in a variety in their product ranges and make them appealing. 

Importance of Supply Chain as a Driving Force of a Business

  • Better after-sales service

    The main responsibility of an efficient supply chain is its customer service and how it manages the flow of services required after a sale is made. This decides the satisfaction of a customer and ultimately, the fate of the company. Customers should get optimum support, answers to all their queries, and solution to their issues. The job of the supply chain is to keep the after-sales service accessible for all so that the customers can contact them as and when required. This will make them feel connected to the organisation.

     

  • Upgrading the product quality
    This is one of the most important tasks of a supply chain. In this dynamic world with fast-paced industrial evolution, they should try to upgrade the quality of launched products constantly. They should play around with the packaging to make it lucrative or they should work on procuring long-lasting, durable products which are eco-friendly to attract all kinds of consumers. They can also invest in sourcing high-quality raw materials, which will make the products heavy-duty, adding to the goodwill of the business.
     

  • Reducing manufacturing costs

    Reduction of costs is the most difficult task for a business to handle and that is why supply chain teams try their best to incorporate cost-effective measures for the production of goods. This involves introducing newer low-cost machinery, buying raw materials at the wholesale rate, reducing the number of defective items, and increasing the efficiency of the chain system. They could also work to limit the number of suppliers so that the cost can be managed as well as try to target a large range of consumers so that the sale increases, generating profits for the company.

     

  • Introducing optimal marketing strategies

    It is the role of the team to come up with alluring strategies to increase sales. They should present the product in the best way to attract customers to make a purchase. Increased use of advertisements and social media can also be fruitful in generating buzz about a product. This is the deciding factor for the success of a product.

     

  • Bettering the financial condition

    The supply chain team has to work towards improving the financial position of the business. It has to produce good quality products so that the customers return to make a purchase again. This is linked to the company’s goodwill, helping it build a brand name and bettering its position. It has to work in a way that the company generates profits, has reduced production costs, increased cash flow, and reduced fixed assets. This will prove that the company is adapting to new changes and making progress, adding to its success. 

Supply Chain Management Course

Supply Chain Management is a course that is very much in trend these days. In this dynamic, ever-changing industry, businesses require huge teams with adequate supply knowledge to help and guide them in this process for a company to emerge successful. 

Several courses provide certifications for supply chain professionals, but they are not always legitimate or helpful in improving your skill set or providing you with proper training. The best supply chain management course with analytics is provided by Imarticus. Their professional certification in supply chain management and analytics is in collaboration with IIT Roorkee.

This program provides you with live classes by top professionals and industry experts, hands-on learning experience with real-life supply chain management project work, and an IIT Roorkee alumni status. This certificate course will help you gain relevant skills and will help you take data-driven decisions at strategic levels. It helps you with impressive learning and project portfolios that will help you in the placement process. If you want to enrol in a supply chain management course, this is it. 

Conclusion

A Supply Chain Analyst is a profession that has been rapidly evolving in the last few years. It deals with the most important aspect of a business — demand, and supply.  One needs to take up a proper course to know the trends and dynamics of it to be able to contribute to the growth of an organisation. The right course will provide you with the accurate skill set and proper training, making you lucrative to recruiters. Supply Chain Management is not as easy as it looks and requires one to take decisions that could make or break a business, so it is advisable to take the required guidance before you step in. 

IIT-Roorkee Machine Learning Certification: Why It Is Important?

Posted on by Imarticus Learning

As we all know, the technological revolution is taking the world by storm. Every day, new technologies are being developed and used to solve problems in many different fields. One of these technologies is machine learning which has come a long way. As a result, an IIT-Roorkee Machine Learning Certification is becoming increasingly important for anyone who wishes to stay ahead of the curve.

The IIT-Roorkee Machine Learning Certification provides students with a comprehensive overview of the field and its principles. From understanding foundational concepts to practical applications in real-world scenarios, this certification helps equip learners with the knowledge they need to succeed in a rapidly changing world. Additionally, the program allows learners to stay ahead of the curve by learning cutting-edge techniques and algorithms for machine learning tasks.

This post will discuss why the IIT-Roorkee Machine Learning Certification is important. We will explore how it can help learners stay abreast with the latest developments in machine learning and help them gain a competitive edge in their respective fields.

Why Is IIT-Roorkee Machine Learning Certification Important?

IIT-Roorkee Machine Learning Certification is important for a variety of reasons.

First and foremost, it equips learners with a comprehensive understanding of the field. The program covers both theoretical and practical aspects, helping learners develop an in-depth understanding of the fundamentals as well as more advanced concepts. As a result, learners are better prepared to apply their knowledge in real-world settings.

Aside from providing a broad overview of machine learning principles, the IIT-Roorkee Machine Learning Certification also emphasizes the importance of staying up-to-date with the latest advancements in the field. Learners are exposed to the newest techniques and algorithms which they can use to tackle complex tasks. This ensures that learners remain on top of their game, allowing them to stay ahead in their respective fields.

Finally, the IIT-Roorkee Machine Learning Certification also provides learners with valuable industry connections. Learners have opportunities to network with professionals from various industries and gain valuable insights into their respective fields. This helps them remain knowledgeable on the latest trends and advancements in their chosen field.

How can one pursue the IIT-Roorkee Machine Learning Certification?

The IIT-Roorkee Machine Learning Certification is offered as an online program. The course consists of several modules, each module focusing on a different aspect of machine learning. Learners can take all the modules at once or just choose certain ones to focus on depending on their interests and goals.

The course is also designed to be flexible and accommodate different learning styles. Learners can choose from various materials such as videos, lectures, quizzes, and more to best suit their needs. Moreover, the instructors are highly experienced professionals who have a deep understanding of machine learning principles and techniques.

Apart from the online course, prospective learners can also opt for an on-campus program that focuses specifically on the IIT-Roorkee Machine Learning Certification. This option is ideal for those who want to take their time and gain a deeper understanding of the subject.

So, no matter what path you choose to pursue, the IIT-Roorkee Machine Learning Certification, you can be sure that it will be a valuable asset for your professional development. Not only will this help you acquire the skills and expertise to remain informed on modern industry trends, but it will also open up career prospects in an assortment of sectors.

Explore Data Science and machine learning with Imarticus Learning

Do you want to become a data scientist and enhance your machine-learning abilities? Then look no further! IIT Roorkee Certificate Program in Data Science and Machine Learning is the perfect avenue for you. Start building up foundational knowledge today so that you can develop skills in this high-demand field tomorrow!

 Course Benefits For Learners:

  •  Learn from IIT Roorkee’s experienced instructors.
  • Gain in-depth knowledge of the fundamentals as well as advanced concepts of Data Science and machine learning.
  • Give yourself a career advantage with our IIT Roorkee data science online course – where you will gain an in-depth understanding of cutting-edge technology that will open up extraordinary opportunities.

Visit our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, or Ahmedabad, or get in touch with us via chat support.

A Brief Timeline of AI: Here’s Why you should opt for an IIT Artificial Intelligence Course

Posted on by Imarticus Learning

In today’s digital age, technology has taken giant strides across every stream. Various sectors like are adopting or upgrading technology at a rapid pace. Artificial intelligence (AI) technology has gained significant popularity in recent times. AI adoption helps streamline processes, to reduce man-hours and overhead costs across sectors. So, sectors are adopting AI technology creating increased job opportunities in the AI field. Examples are education, the corporate sector, tourism, healthcare, retail and hospitality.

The IT market expects to reach $190 billion by 2025. By 2030, AI is expected to increase global GDP by $15.7 trillion. With so much happening on the AI front, it is natural for tech enthusiasts to look out for AI courses.

Does AI and Machine Learning (ML) make you curious? Do you want to learn AI but do not know where and how to start? Courses like IIT AI ML Course provide the best learning outcome for AI and ML learners.

Continue reading this blog to understand AI. Also, what makes an IIT Artificial Intelligence Course so unique for AI learners?

What is Artificial Intelligence?

AI is a computer science branch. It talks about developing intelligent machines to think and work like humans. AI simulates human intelligence through machine technology. For instance, learning and speech recognition. Different factors are responsible for developing AI. These include ML, neural networks, big data, and natural language processing. AI finds use in different industries. It saves time and effort across processes. So, AI has gained increased prominence in the modern tech-enabled world.

Who is an AI Professional?

An AI professional develops an intelligent algorithm to analyse and forecast future events. They create machines to reason, think and function like humans. An AI professional studies human brain functioning. It helps them to develop computer programs that replicate human cognitive abilities.

Who can learn Artificial Intelligence?

Students or tech career aspirants interested in learning artificial intelligence and data analytics. Also, people who love programming. People with knowledge of programming languages like Python etc.

 How can one learn AI?

The popularity of AI technology has led to many AI courses now being offered. AI Courses Online and offline are both available. Even weekend courses are available for students or working professionals. One can start AI learning with advanced mathematics and then consider learning to code. The IIT AI ML Course offers practical knowledge of ML, deep learning, and AI concepts. It also provides real-world AI and MI projects. It will help a student or professional build a strong AI concept for becoming an AI professional.

Skills required by AI Professional

Anyone looking to become an AI professional should have below skills:

· Technical knowledge of data insights

· Knowledge of algorithms, advanced mathematics, and statistics. A post-graduate degree in these areas will help.

· Advanced knowledge of programming languages like R, Python etc.

· IT industry knowledge and the latest AI and ML trends

· Communication and presentation skills

What Career Path can AI professionals take?

An IIT Artificial Intelligence Course will help you become

·         AI Engineers to create artificial intelligence models

·         ML Engineers to develop, build and manage data-enabled ML software applications

·         Big Data Engineers to develop  systems for communication and data collection

·         Data Scientists to collate and interpret data and predict information

·         Data analytics to analyse data and mine insights. Use the information to make business decisions

What’s unique about the IIT AI ML Course?

The IIT AI ML Course is a 9-month Artificial Intelligence and Machine Learning Course. The course is a tie-up between Imarticus Learning and E&ICT Academy, IIT Guwahati. The advanced IIT Artificial Intelligence Course helps master AI and ML concepts. Aspiring AI and ML career aspirants will find this state-of-the-art course very insightful.

The IIT AI ML Course offers the below benefits to make you a new-age AI and ML Specialist.

· Prepares for Data Scientist, Data Analyst, ML Engineer, and AI Engineer roles

· Master ML & AI skills via the IIT Artificial Intelligence Course. E&ICT Academy and AI & ML industry leaders have designed the course.

·Data science, machine learning, deep learning, natural language processing and computer concepts covered in the course

· Gain hands-on experience with 20+ live AI-ML business projects

·Take part in a unique 3-day Campus Immersion module at the E&ICT Academy, IIT Guwahati Campus. Interact with their students and AI&ML faculty.

· Get an opportunity to take part in a national Hackathon to upgrade your AI and ML expertise

Conclusion

AI has become important in today’s dynamic business environment. IIT AI ML Course offers AI concepts. It offers an expert mentorship facility. Practical real-world knowledge of AI and ML forms part of the course. It is a partnership between Imarticus Learning and E&ICT Academy, IIT Guwahati. Our course offers live AI and ML projects from different industries. It helps your transition as an AI professional.

For more on our AI program, visit our website, chat support, or training centres. We are in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, and Ahmedabad.

5 Examples where Tableau can Simplify Your Analysis

Posted on by Imarticus Learning

A tableau is a powerful tool for analyzing data but doesn’t replace statistical software. Tableau can simplify your analysis process by allowing you to visualize the results of your analysis in a way that’s easy for others—and yourself—to understand.

In this post, we’ll look at five examples where Tableau simplifies your analysis:

Finding the right data can be complex.

corporate leadership

Data quality is a huge issue in data analysis. With the right data, it can be easy to make extraordinary discoveries and gain insights through your analysis. However, if you’re trying to find the right data out of thin air or if there is no way for you to get hold of specific information (like records that are only available electronically), finding good results will become an uphill battle. Tableau can help with both these problems by allowing users access to different sources of information so they can find exactly what they need when they need it most!

Self-service analytics

Self-service analytics means allowing non-technical users to work with data. A tableau is a great tool for this analysis because it’s easy to use and learn, so you don’t need extensive technical knowledge or training.

Tableau has many benefits when it comes to self-service analytics. The software can be used on mobile devices, making it easier for your organization or business unit to analyze its data on the go—and without needing any special hardware or software.

Location-based analysis

A location-based analysis is a powerful way to analyze data by location. For example, you can use Tableau to find patterns in your sales data by state or zip code. You can also visualize this information and see what’s happening at a particular store or location based on where people buy products.

Visualizing and presenting data

A tableau is a tool for making data-driven decisions. It’s also a tool for visualizing data, presenting it in charts and graphs that make sense to people who aren’t experts on analytics or statistics. And finally, it’s a tool for communicating your analysis—to other people who may be working on similar projects but with different goals than yours.

Combine multiple sources of information 

You can use Tableau to combine data from multiple sources in one place. This powerful capability can simplify your analysis, particularly when combined with advanced mapping functionality. 

Tableau can be used as a standalone tool or integrated into other analytics software like Excel or SAS. Learn Tableau online with other data sources such as Microsoft Access or SQL Server databases. 

Discover a career in data analytics with Imarticus Learning

With this data science course, you can become a data analyst and receive a job offer. Create complex models using data science that produce vital business forecasts and insights by putting your new knowledge to practical use.

Course Benefits for Students:

  • The most widely used data science methodologies and tools, as well as data analytics and the fundamentals of machine learning, will be familiar to students.
  • To receive a data analytics certification course, students must complete 25 real-world projects and case studies directed by business partners.
  • Using a data analytics tool to display data is one of the most in-demand skills in the market. Therefore, recent graduates and those starting their careers might want to think about enrolling.

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

How To Build a Twitter Sentiment Analyser Using Natural Language Processing

Posted on by Imarticus Learning

Natural language processing is the capability of the computer program to comprehend the human language, both verbally and manually and then use it for communication. Computer systems use linguistics, computer science and artificial intelligence for this complex operation. After understanding the context of the textual or verbal content, they can use it to infer, analyse, and make something of their own. In simpler terms, they are trying to understand and use language just like a human. 

Building a Twitter sentiment analyser

NLP is a part of a machine learning course with placement. You are trained to develop code for activities like these, where you will be building a Machine Learning model that will try to understand the sentiment behind a tweet. Using this Twitter sentiment analyser, you can try to understand which tweets have hate speech or objectionable speech in them. It could also be used to filter sexist and racist tweets as well. It is an activity that is related to supervised learning.

For this activity, you would need the following:

  1. Coding knowledge of Python.
  2. You will need to use various libraries of Python and natural language processing.  
  3. A dataset consisting of tweets. This dataset can be downloaded from the Twitter API.
  4. Knowledge of three classifiers – logistic reasoning, Bernoulli Naïve Bayes and Support Vector Machine (SVM)

Coming to the dataset will contain various fields like:

  • Twitter handles: The id of the user
  • Ids: Unique tweet id
  • Date: The tweet date
  • Flag: It refers to the social platform’s filtering response to indicate the query’s polarity, i.e. is the tweet positive or negative? If no such response exists, then the default value of this response is NO QUERY.
  • Text: The text of the tweet. This is the content that we have to process and comprehend the context.
  • Stopwords: A list of stopwords or words that are irrelevant for processing is provided to the machine learning dataset so that these words are not used in the assignment.

 

The rest of the other fields will be removed or overlooked while the text will be processed for sentiment comprehension and reporting. This machine learning technique is used by all websites, mainly social media platforms, forums and dating apps, to filter and remove objectionable content. Along with the filtering script, the sentiment analyser is used to understand the milieu of the tweet.

What does the project pipeline contain?

The chronological steps that form the project pipeline for the machine learning assignment are given below:

  • Import the required dependencies i.e. the ML libraries that are required to understand the emotion behind the tweet. For this, you could import the Seaborn library or the Wordcloud library.
  • Read and load the dataset. The dataset will be loaded onto the ML model after cleaning the raw data and extracting the information relevant to the code development target.
  • Exploratory data analysis. Analysing the data for the specific target variables. Which tweets have the data variables and which tweets do not have them? The empty values are treated as NO QUERY or null valued fields.
  • Data visualisation of target variables. The visualisation of the usage of the target variables in a pictorial manner will tell how densely the emotional words are used. This will help in extracting the necessary language indicators that will help to understand the context of the tweet.
  • Data pre-processing. After the visualisation has been done, the data will be further filtered for being split up and for training the machine learning model for future analysis of the tweets. Stemming and lemmatization are performed in this step which helps to reduce the language to its root form by understanding the meaning of the words.
  • Splitting our data into train and test subsets. This is an intermediary step which will be necessary for the training of the model.
  • Transforming dataset using TF-IDF vectorizer. This will help to evaluate the model with the help of the transformed data. The polarity of the words, either positive or negative will be processed for matching with the sample data. Here numerical values are given to various emotions.
  • Function for model evaluation. The context will be understood in this stage based on the sample dataset and the inferred dataset. After that, a comparative analysis will be done which will help us to understand the extent of the polarity of the words.
  • Model building. After the sample dataset has been analysed and processed for the context, this data will be used for the evaluation of future data.
  • The assignment will be concluded with the necessary inferences from the experiment and analysis of the sample dataset.

Once you enrol for PG in data analytics, you will learn more about this in greater detail. Also, if you take admission with Imarticus Learning for a PG program in machine learning and artificial intelligence, you will participate in live projects that will help you understand how to manage professional responsibilities.

To sum up, if you plan to learn how to build Twitter sentiment analyser or similar programs, then learning natural language processing is the right first step. Here, you will learn the basics of AI and ML, which will help you build such an extensive program without any hassle. 

Supervised Learning: Definitions and concepts you need to know

Posted on by Imarticus Learning

In recent years, artificial intelligence (AI) and machine learning (ML) have gained immense popularity and are expanding their horizons rapidly. It is said that technologies driven by ML and AI are yet to show their true potential. Hence, companies from every sector are adopting AI and ML and trying to integrate various powerful technologies. This is why, the future of artificial intelligence and machine learning is quite promising.

One of the subfields of artificial intelligence and machine learning is supervised learning. To learn and understand more about this sub-area, one can get enrolled in online artificial intelligence and machine learning course. This will help to create a vivid understanding of supervised learning. Subsequently, an individual can commence a successful and lucrative career in supervised learning.

What do you mean by supervised learning?

Supervised learning is one of the promptly growing sub-arenas of artificial intelligence and machine learning. This branch deals with issues faced in the real world. It also has the potential to solve these problems. Notably, problems like the segregation of spam mail and messages can be easily done with the help of this amusing technology. Supervised learning consists of various datasets and algorithms that can predict the results automatically.

If an individual is willing to commence a career in supervised learning, then he/she must have detailed knowledge about it. For this, one can opt for online supervised learning training. This will help you to bag lucrative job offers.

How does supervised learning function?

Supervised learning acts on the training set. This dataset trains a model for the desired output which is later yielded by the model. A training dataset mainly consists of various inputs and correct outputs. This procedure assists the machine or model to comprehend real-life problems over sufficient time. The training dataset adjusts all the errors until it is minimised and accurate. 

Supervised learning segregates problem detection into two distinct categories. And, they are:

Classification

Classification can assign tested data to accurate categories with the assistance of an algorithm. It traces, defines, and labels different data entries within the dataset of the model. The few most commonly used classification algorithms are k-nearest neighbour, linear classifiers, decision trees, random forests, and support vector machines (SVM). These algorithms are user-friendly and easy to use. 

Regression

Regression is used to comprehend the link between dependent and independent variables. It is usually used to create projections or statistics. Some of the most used regression algorithms are logistical regression, linear regression, and polynomial regression.

What are the various algorithms of supervised learning?

Supervised learning has various types of algorithms. And, these have been precisely discussed below:

K-nearest Neighbour

KKN is also known as k-nearest neighbour. This segregates data points with the help of proximity to other data. This algorithm tends to assume that the same data points can be located exactly near each other. So, it calculates the distance between two data points with the assistance of Euclidean distance.

Random Forest

One of the most famous and flexible supervised learning algorithms is Random Forest. It can be used for classification as well as regression. Initially, it obtains all the unrelated decision trees and unifies them together to obtain more precise data projections  

Support Vector Machine (SVM)

One of the most popular supervised learning models is Support Vector Machine (SVM). This was created by Vladimir Vapnik and can be used for regression as well as classification. This model creates a hyperplane, where distance is maximised between two classes of data points. This hyperplane is also known as the decision boundary.

Logistic Regression

Logistic regression is generally used when there is a binary or dual output like ‘yes’ and ‘no’. This regression model is built to comprehend the bond between the inputs. However, the main task of Logistic Regression is to resolve problems like spam identification.

Linear Regression

The main task of Linear Regression is to find the link between a dependent variable and independent variables. It is also responsible for foreseeing future results. There are two kinds of Linear Regression, namely Multiple Linear Regression and Simple Linear Regression. For an instance, when there is only one dependent variable and one independent variable, it is known as Simple Linear Regression. However, when there is more than one independent variable, it is referred to as Multiple Linear Regression. The method of least squares is used to plot the line of best fit for different linear regressions. 

Conclusion 

Imarticus Learning is offering an IIT artificial intelligence course that covers the entire portion of supervised learning. This is an online course that is led by experienced instructors. The course will open many lucrative job opportunities for those who want to commence a career in this domain. Thus, without any delay, get yourself enrolled in this online course. 

A quick guide to understanding neural networks and their applications

Posted on by Imarticus Learning

Introduction

We have been hearing about AI everywhere and how it will replace humans in many sectors.

But do we really know what it is? And, how is it going to affect the human race?

Nowadays, AI is a commonly used terminology in analytics. It is often used in neural networks or artificial neural networks. Especially, if you are looking to build a career in data analytics.

An algorithm called a neural network allows a computer to learn from observational data. Computing neural networks are modelled on how the biological nervous system processes information.

Interconnected neurons with input-receiving dendrites make up biological neural networks. They generate an output based on these inputs and send it to another neuron through an axon.

History of AI

Let’s dive deeper into the history of AI and how it evolved to have a better understanding of it.

Since the 1950s, researchers have been working to create smarter and better robots. To do this, they mimic how neurons function.

Humans ultimately succeeded in building such a computer. A computer that could identify human speech, after much trial and error. Only after the year 2000 were people able to achieve deep learning. It is a branch of AI that can recognize and differentiate between different images and movies.

Deep Learning: Do we know what it is?

Deep learning is a subset of machine learning. It enables computers to learn from examples as people do.

Instead of hardwiring a computer programme to detect and learn, machines are educated using images as examples. You have control over the factors that feed into it, not how it knows. The computer recognizes the object based on the previous input photographs.

Any system using deep learning is powered by a synthetic version of a biological neuron.

Origin of the term “Neural Network”

After learning the history of AI, let’s dive into the origin of the term ‘Neural Network’.

The earliest prototype of an artificial neural network was created by logician Walter Pitts and neuroscientist Warren S. McCulloch. Their work is where the word ‘neural network’ originated. They discussed the idea of a neuron in their work. A neural network is a single cell that is part of a network of cells and takes inputs, evaluates them, and produces an output.

Functions of Neural Networks 

Neural networks can carry out the following tasks.

Text translation.

Recognize faces.

Identify speech.

Identify text written by hand.

Command robots.

These are just a few examples, but neural networks can perform many more functions.

Types of Neural Networks

We have talked a lot about the history and origin. Now, let’s see the types of Neural Networks.

Multi-layer perceptron

The neural network employs a nonlinear activation function and a multi-dimensional perceptron. The multi-dimensional perceptron might have three or more layers.

Convolutional Neural Network

This neural network employs a variant of complex perceptrons and the convolutional neural network.

Recursive Neural Network

It uses weights to produce structured predictions.

Recurrent Neural Network

A recurrent neural network is the fourth one. It links neurons in a specifically directed cycle.

LSTM neural network

LSTM neural network utilizes the recurrent neural network design. This long short-term memory neural network lacks an activation function.

Sequence-To-Sequence Models

The last two modules create a vector space from a large amount of text. It uses two recurrent networks and shallow neural networks.

How do the neural networks work and are put to use?

As discussed earlier, the purpose of neural networks is to find patterns in data. Clustering, classification, and prediction are used to separate these patterns. These all address specific issues that apply to various fields. The fields include marketing, sales, security, and finance.

Neural networks are used for a variety of tasks. It includes facial recognition software to apprehend criminals and daily stock market forecasting.

These networks can be leveraged for marketing objectives. They can be used for chatbots, target marketing, and market segmentation.

Neural networks will be used in biomedical systems in the upcoming years. They will be used to track down diseases. Or even to determine the probability of a person having a particular genetic characteristic or anomaly.

Conclusion 

Artificial neural networks come in a variety of forms and functions to yield astounding outcomes. The neural networks are constructed similar to how brain neurons function. It is their most crucial feature.

Consequently, they are built to acquire more knowledge and make decisions in a human-like manner. Thus, neural networks can actually deliver better results. It contrasts typical machine learning algorithms which seem to stop after a certain point.

Data science is used in the real world and creates prediction models to improve business outcomes. This ensured placement program is appropriate for professionals and recent graduates. Anyone who wants to pursue a career in data analytics and science can pursue this degree.

Get in touch with us via chat support for building a data science career. You can also visit our training facilities in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, or Ahmedabad.

Machine learning tools that data scientists must learn in 2023

Posted on by Imarticus Learning

Machine Learning is an inseparable part of Data science these days. With the advancement in software development in AI and ML, several advanced and cutting-edge Machine Learning tools have been introduced in the market. 

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Due to popular demand, these tools have become easier to access, and every data scientist should avail of this wonderful opportunity. If you are a data scientist pursuing a PG in data analytics, it is even more important that you learn how to use these tools. Learning these tools will also be advantageous for your curricular studies, and you can upgrade your skills. 

Top 8 Machine Learning Tools Every Data Scientist Must Learn

And now, let me share about each of them in greater detail!

  1. Python

Python is a widely used programming language and a useful tool in data science to analyse data. It can also be repurposed for machine learning and deep learning. The syntax is also quite easy, and the programming language has rich libraries. 

The community is also very active and responds quickly in case you have an issue. There are also regular boot camps for Python in India. There are many resources where you can learn Python. You can take an online course or read a book to learn the tricks. 

  1. NumPy

It is an acronym for Numerical Python. It gives support for multi-dimensional arrays and matrices. This Python library uses C/C++ for its development. The biggest advantage of using NumPy is that it provides all the necessary mathematical support for ML. 

It also uses less memory and has a faster performance when compared to other libraries. It is truly an asset for Data Scientists who use machine learning for projects like random password generators, statistical analysis, calculators or video games.

  1. Pandas

It is a data analysis and manipulation library built over the NumPy package’s architecture. It helps to handle tabular data. Pandas are quite flexible, as they can be used with other tools as well. You can effectively use it to build a recommendation system like Netflix or Python. You can also make prediction systems for stocks and neuroscience from this package.

  1. Scikit 

It is an open-source ML library for Python. It is built over the architecture of NumPy, SciPy and Matplotlib. Since it is accessible and reusable, it has great flexibility. It can be used for training and testing models using classification, clustering and regression algorithms. 

Apart from making prediction analysis, recommendations systems and automation, it can also be used to develop evaluation and matchmaking systems.

  1. Matplotlib

It helps in data visualisation and graphical plotting and is a wonderful OOPs tool that helps make embeddable plotting APIs. Since it can be used cross-platform, it can be integrated with third-party apps. It includes LaTex to develop neuroscience apps, stock price evaluation systems and also for game development.

  1. R Programming

R is another famous tool for Data Scientists who are also into machine learning. It is also a good choice of popular programming language highly regarded by statisticians or data scientists. It is also useful for a visual representation of data (with the help of ggplot2). 

R has many packages, making it a perfect scientific research tool. It finds many applications in the healthcare data sector and other fields that heavily use statistics. It is also best suited for data mining.

  1. TensorFlow

TensorFlow is quite a robust machine-learning tool developed by Google. As a data scientist, you can use it to build and train machine learning models in a short time. It provides a data automation platform. You can also train and monitor your models using TensorFlow. This tool is extremely useful but only when the hardware is decent enough.

  1. Hadoop 

Apache Hadoop is a collection of open-source software platforms that allows data scientists to use a whole computer network to solve Big Data and Computation problems. Hadoop provides a software framework that helps distribute the storage and improves big data processing using the MapReduce programming model. 

Now Hadoop is used to power many cloud storage apps and also powers search engines and social networks. Its community is so widespread that you have multiple resources to learn Hadoop online.

As an aspiring data scientist, it might be challenging to learn these separately. However, there is an option to learn all these tools at once. Imarticus Learning has a good data science course with placement, which covers all these important tools as a part of its curriculum. You will be taught by the best faculty and even be given a job placement after ending this course. What else could you ask for? 

The Importance of PG Data Analytics in A Data Analyst’s Life

Posted on by Imarticus Learning

As a data analyst, you know that having accurate, reliable data is essential to your job. But what if the data you’re working with could be cleaner? That’s where PG in Data Analytics comes in. Data Analytics is a powerful tool that can help you clean and organize your data to get the most out of it. This blog will show you how to use Data Analytics to make your life as a data analyst easier.

Importance of Data

As a data analyst, one of your most important responsibilities is to ensure that the data you’re working with is clean and accurate. This can be challenging, especially when working with large data sets. 

Data analytics’s features make it the perfect tool for data analysts. Additionally, it allows users to filter and sort data to find the information they need quickly and easily. Finally, Data Analytics offers several advanced features that enable users to customize their experience. 

How to Use Data Analytics?

Now that we’ve discussed some of the features that make Data Analytics a valuable tool for data analysts, let’s talk about how to use it. When you first open Data Analytics, you’ll see a dashboard with various options. The first thing you’ll want to do is select the “Data” tab. This will take you to the “Data Explorer” page, where you can begin cleaning and organizing your data. 

Select the “Filter” option from the left-hand menu to clean your data. This will allow you to specify which columns you want to include in your analysis. Next, select the “Sort” option from the left-hand menu. Finally, select the “Visualize” option from the left-hand menu. This will enable you to create charts and graphs that will help you better understand your data. 

The Benefits of Getting Started with Data Analytics

Many learners are intimidated by the thought of data analytics. They see it as a complex, confusing subject that needs to get explained. However, data analytics can be advantageous and exciting once you start. 

Here are some benefits of getting started with data analytics: 

You can use data analytics to answer all sorts of questions about our world. For example, you can use data analytics to determine why crime rates are higher in certain areas or what factors contribute to successful relationships. By understanding the data, you will better understand the world around you.

Data Analytics is a powerful tool every data analyst should have in their arsenal. It offers an intuitive interface, the ability to filter and sort data, and several advanced features that make it an invaluable tool for anyone who works with data daily. So what are you waiting for? 

Discover PG in data analytics with Imarticus Learning

 You might be able to find employment at Tableau with the aid of this data analyst training. Create complex models using data science to produce vital business forecasts and insights using what you’ve learned.

Course Benefits for Students:

  • Students will be familiar with the most common data science methodologies and tools and the fundamentals of data analytics and machine learning.
  • Students who complete 25 real-world projects and case studies under the guidance of business partners will receive a data analytics certification course. The data science and analytics course is an intensive program that will teach you how to use data analytics to solve real-world problems.
  • One of the most in-demand skills on the market is the capacity to display data using a data analytics tool.

Visit our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, or Ahmedabad, or get in touch via the chat support system.