Why are companies greatly demanding candidates with a artificial intelligence certification?

Posted on September 11, 2022March 29, 2024 by Imarticus Learning

Artificial Intelligence has expanded at an exponential rate in recent years, despite significant progress in the field. In the field of computer science, AI practices can be found everywhere. It provides you with an idea of how many different ways a computer system can be designed.

It is designed to carry out the cognitive functions that humans have specified. This indicates that the scope of an artificial intelligence course is enormous, and AI has potential that is currently beyond human grasp.

Scope of An Artificial Intelligence Course in India

Artificial Intelligence has enormous potential to transform every sector of the economy for the greater good.

AI encompasses a wide range of technologies, including self-improving algorithms, machine learning, big data, and pattern recognition, to name a few. There will be few industries or sectors left unaffected by this potent weapon in the not too distant future. This is why online Artificial Intelligence courses are becoming increasingly popular in India.

With each passing day, the gap between the number of AI professionals required and those available widens. Corporations are spending money to train their existing employees on Artificial Intelligence technologies. However, the demand is far higher.

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Learn AI via 25 in-class, real-world projects focused on offering exposure to various industries. This 9-month program will help you prepare for the roles of Data Scientist, Data Analyst, Machine Learning Engineer, and AI Engineer.

This machine learning certification program was established in collaboration with the E&ICT Academy, IIT Guwahati, and industry professionals to give an optimum learning outcome

This course will strengthen your core abilities, allow you to take advantage of our Expert Mentorship program, and give you a practical grasp of AI and Machine Learning.

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Take Away

AI is one of the most popular technologies on the planet because of its diversity and superior solutions. It has been rapidly expanding. As you can see, the scope of AI has broadened to include a wide range of industries, including healthcare, transportation, etc., security, etc. Multiple industries require the expertise of experienced AI specialists as a result of this increase.

Check out Imarticus IT classes, targeted at working professionals, if you want to learn more about AI and machine learning algorithms.

Supply chain management course with analytics: The cog that keeps things moving

Posted on September 10, 2022February 7, 2023 by Imarticus Learning

Supply Chain Management Course with Analytics: The cog that keeps things moving

The term “supply chain management” conjures up images of warehouses and trucks for many people. But the reality is that SCM is so much more than that. In today’s world, the supply chain is complex and global, and it requires a sophisticated understanding of data and analytics to manage effectively.

So, in this post, we’re going to look at What supply chain management is, the different aspects of SCM, How analytics play a role in optimizing the supply chain, and the benefits of pursuing a supply chain management course with analytics.

What is Supply chain management?

Supply chain management (SCM) is the process of coordinating and optimizing all the activities in the supply chain, from raw materials procurement to manufacturing to distribution. The goal of SCM is to ensure that finished products reach consumers in a timely and cost-effective manner. More and more, SCM is being viewed as a strategic differentiator that can give companies a competitive edge.

What are the different aspects of SCM?

There are four main aspects of SCM:

Procurement: This is the process of sourcing and purchasing raw materials.
Manufacturing: This is the process of transforming raw materials into finished products.
Distribution: This is the process of delivering finished products to consumers.
Returns: This is the process of handling returns and refunds.

How do analytics play a role in optimizing the supply chain?

Analytics is playing an increasingly important role in SCM, as it can provide insights that help organizations optimize their supply chains. By analyzing data on things like supplier performance, customer demand, inventory levels, and transportation costs, organizations can make changes that improve the efficiency as well as the effectiveness of their supply chains.

What are the benefits of pursuing a supply chain management course with analytics?

There are many benefits to pursuing a supply chain management course with analytics.

First, it will give you a better understanding of using data and analytics to optimize the supply chain.
Second, supply chain management training helps you develop the skills needed to manage a complex global supply chain.
Third, it will allow you to earn a supply chain certification online, which can help you advance your career.
Finally, it will position you for success in an increasingly data-driven world.

Explore SCM course with Imarticus Learning.

Imarticus Learning offers a comprehensive supply chain management course with analytics that will give students the skills and knowledge they need to succeed in today’s data-driven world. The course covers all aspects of SCM and includes a strong focus on analytics.

Course Benefits for Learners:

Imarticus Learning supply chain certification is widely recognized and respected by employers.
The course is designed to help learners develop the skills needed to manage a complex, global supply chain.
The course is designed by IIT faculty and covers all the latest trends in SCM.
Students will get to learn about various analytical tools and techniques that are used in SCM.

Top 7 examples of supervised learning algorithms

Posted on September 9, 2022April 8, 2024 by Imarticus Learning

Top 7 examples of supervised learning algorithms

Supervised learning algorithms are great for solving problems with a large amount of training data. The supervised learning algorithms are great for classifying high-dimensional data representing high-dimensional vectors and matrices. This post will discuss seven examples of supervised learning algorithms.

Linear Regression

It is a supervised learning algorithm that relates the value of one or more independent variables to the value of a dependent variable. The goal is to find linear combinations of these independent variables that can also predict values for your dependent variable.

The process behind linear regression is simple: you have some data, which might be either a set of samples or entire population distribution.

You then choose one or more continuous variables and their corresponding values and use them as inputs into a linear equation whose coefficients represent how much each input contributes to predicting your outcome variable’s value.

Decision Trees

Decision trees are based on the principle that if you have enough examples of your training data set, then you can use these examples to create one tree per decision variable in your problem. In this case, multiple branches would come out of each node, representing different possible outcomes or predictions made by our model using each input variable.

Support Vector Machines (SVM)

Support vector machines (SVM) are supervised learning algorithms in binary classification. The SVM is also known as a kernel-based classifier. It uses the concept of high-dimensional data points to determine which of two classes (or categories) will be most beneficial for further analysis and prediction.

Logistic Regression

It is a supervised learning algorithm that can use for classification, binary classification, and multi-class classification. Given its probability density function, it predicts the probability of an event occurring.

Nearest Neighbor

Nearest neighbor is a supervised learning algorithm used to classify data.

The algorithm uses information about each point in your datasets, such as its x and y coordinates and color or shape, to determine how similar each point is to itself (its Euclidean distance). The value of each feature used by this algorithm will vary depending on what you’re trying to do with your data set.

Gaussian Naive Bayes

The Naive Bayes model is a generative model. (Gaussian) Naive Bayes assumes that each class has a Gaussian distribution. The basic idea behind GNB is that we have a set of training data (a bunch of examples), and we want to predict what event will happen next in our new example.

Random Forest

Random Forest is a supervised learning technique that uses multiple decision trees to make predictions. It gets used in many fields, including biology and machine learning.

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Learn how to become an AI engineer by enrolling in the E & ICT Academy’s deep learning Artificial Intelligence certificate program. Students will benefit from this IIT AI ML Course as they prepare for careers as data analysts, data scientists, machine learning engineers, and AI engineers.

Course Benefits For Learners:

Learners work on 25 real-world projects to gain practical industrial experience and prepare for a rewarding career in data science.
With a certificate authorized by the IIT Guwahati, E & ICT Academy, and an Imarticus Learning-endorsed credential, students can impress employers and demonstrate their abilities.
Students who complete this machine learning and artificial intelligence course can land lucrative jobs in the field of machine learning and artificial intelligence.

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

Types and uses of supervised deep learning algorithms

Posted on September 8, 2022April 8, 2024 by Imarticus Learning

Types and uses of supervised deep learning algorithms

Supervised learning is a machine learning technique in which data has to be given to the machine to learn by itself. Supervised deep learning is the art of teaching a machine to learn without human help. The idea behind supervised and unsupervised learning is to train your model using a dataset to perform best.

Supervised methods typically predict general categories from large amounts of input data. For example, a supervised neural network might get used to identify a person’s profession without knowing their job title. This post will review supervised deep learning algorithms and explore their various applications in the market today.

Introduction of supervised learning

Supervised learning is a type of machine learning that uses labeled training data to train an algorithm. In supervised learning, you have samples from the target class, such as a set of images or text documents, and you want to learn one or more functions that map each sample into its class (for example, image recognition).

You can use supervised learning for many applications, including classification (training algorithms like neural networks), regression (linear regression), anomaly detection with support vector machines, k-means clustering, etc.

Types of supervised learning

Supervised learning is a type of machine learning where the algorithm gets trained on labeled data. It involves extracting features from the input and then predicting the output of a function based on its input.

The main benefit of supervised algorithms is that they can classify or predict an outcome depending on their nature.

Classification is a Supervised Learning task in which the output has defined labels (discrete value)
Regression is a Supervised Learning task with a continuous value output.

Supervised Learning Algorithms in Action:

Linear Regression
Logistic Regression
Nearest neighbor
Gaussian Naive Bayes
Decision Trees
Support Vector Machine (SVM)
Random Forest

Uses of supervised deep learning algorithms

It allows one to use the experience to optimize the performance of an algorithm. It solves complex real-world problems such as computer vision, spam filtering, fraud detection, voice recognition, and other applications.

You can use supervised deep learning algorithms for the following tasks:

Classification
Regression
Recommendation systems
Facial recognition and image segmentation

You’ll also want to know that supervised deep learning algorithms get used in many other fields, including:

Image classification (e.g., object detection)
Image segmentation (e.g., text)
Image detection (e.g., objects in an image)

Learn Machine Learning and AI Courses with Imarticus Learning.

Learn how to become an AI engineer by enrolling in the E & ICT Academy’s deep learning certification course. Students will benefit from this IIT AI ML Course as they prepare for careers as data analysts, data scientists, machine learning engineers, and AI engineers.

Course Benefits For Learners:

Learners work on 25 real-world projects to gain practical industrial experience and prepare for a rewarding career in data science.
With a certificate authorized by the IIT Guwahati, E & ICT Academy, and an Imarticus Learning-endorsed credential, students can impress employers and demonstrate their abilities.
Students who complete this course on AI and machine learning can land lucrative jobs in the field of machine learning and artificial intelligence.

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

Top 11 SQL queries that a data scientist should know

Posted on September 7, 2022March 29, 2024 by Imarticus Learning

Top 11 SQL queries that a data scientist should know

A relational database management system uses SQL for managing data and for storing it in tabular form. SQL stands for Structured Query Language and it is a query language that is extremely useful for data scientists.

If you want to learn data analytics and get a data science certification course done for your progressive career, SQL is absolutely essential.

Here are some essential SQL queries for data scientists. Data scientists need to be able to retrieve and store the data which you have derived without the help of others.

Must know SQL queries for a data scientist

A data scientist must know the following basic queries to store and manage data in a tabular form in SQL. These commands may be enumerated as follows:

For creating databases

Before creating a database you first need to check what are the databases that are already present. For that, the command is:

mysql> SHOW DATABASES;

Fresh databases can be created with the understated command:

mysql> CREATE DATABASE medium;

For creating tables

Tables can be created in databases with the following command. Here is an example of creating a table for students:

mysql> CREATE TABLE (Student [table “” not found /]
) (

-> Id int primary key,

-> Name varchar(20),

-> Subject varchar(20),

-> registered char(1)

-> );

The primary key establishes that each row is uniquely identified.

Delete a table

For deleting a database, you will have to use this command:

mysql> DROP DATABASE medium;

Add new columns

New columns can be added using the ‘alter table’ statement and using the following command:

mysql> ALTER TABLE student ADD gpa DECIMAL(3,2);

Delete a column

A column can be deleted using the ‘alter table’ statement and using the following command:

mysql> ALTER TABLE student DROP gpa;

Describe statement

if you want to ascertain a specific table’s overview, then the following command will be used:

mysql> DESCRIBE student;

Add a new row

After the creation of the table, you need to add rows and values to it otherwise it will remain empty. Rows can be added to tables by using the following command:

mysql> INSERT INTO student VALUES(1, “mary”, “accounts”, “F”);

mysql> INSERT INTO student VALUES(2, “Anderson”, “Mathematics”, “F”);

mysql> INSERT INTO student VALUES(3, “George”, “Physics”, “S”);

mysql> INSERT INTO student VALUES(4, “Natalie”, “Biology”, “S”);

The values have to be in accordance with the predefined data type.

Delete a row

If you are trying to delete a row then the ‘delete from’ command has to be used in the following way:

mysql> DELETE FROM student (table name);

WHERE (condition);

The condition for deleting the row must be declared after the ‘where statement’.

Update a row

If any update or addition is required in the rows that already exist, that can also be made in the tables. This command must be used for the same:

mysql> UPDATE student SET subject= “Accounts” WHERE Id = 2;

The update must be specified after the keyword ‘set’ and the condition has to be specified after the keyword ‘where’.

Select a row or rows

Queries are run so that data can be retrieved from databases and the ‘select statement’ is used for that purpose. You can specify the row or the column that you need to retrieve data from, using the ‘select command’ in the following manner:

mysql> SELECT Name, Subject FROM student (table name);

Select based on a condition

To retrieve the data from all the rows while applying some filters and conditions to the rows to make the data analysis more precise, you should use the following command needs to be used:

mysql> SELECT * FROM student WHERE Subject = “Mathematics”;

Conclusion

Learn data analytics to get a hang of the most important SQL queries to become a data scientist. Enrol in the Data Analytics course with placement by Imarticus and it will act as a launchpad for your career.

Building a data analytics project with Python? Here are a few tips

Posted on September 5, 2022April 8, 2024 by Imarticus Learning

Building a data analytics project with Python? Here are a few tips

We are in a technology-driven era where everything is based on data and the reports or insights derived from data. The scope for a data analyst is increasing day by day and if you want to learn data analytics and gain the foundation knowledge of data science for business application then we’re here to provide you with the first step towards your goal. You will get your desired data analytics course with placement.

What is data analytics?

Data analytics is the procedure of thoroughly going through the data and examining it in groups and sets. It is done to find the trends and draw certain conclusions based on the information they have. With more technological advancements, Data Analytics is majorly done through various software, systems and techniques.

For the most part, Data Analytics refers to an array of applications that works through fundamental business intelligence, online analytical processing and reporting. Hence, the use and importance of Data Analytics in business can never be understated.

Data Analytics and python

As data analysis is done for finding out the future course of action in any organisation or business, it also encompasses various programming languages. The use of various programming languages helps organisations to make decisions more precisely and efficiently.

The use of python in data science and analytics is not unknown. Data scientists use various programming languages including python to prepare data for statistical analysis. Python is also used in other fields as well rather than only Data Analytics which also proves to be very effective for any organisation.

Python is used for general purposes and provides three major advantages which have been enumerated as follows:

More readable when compared to other programming languages.
Simpler to work with and compile data.
More flexible in the learning phase of the language and easy to understand.

Uses of python in Data Analytics

Python has become one of the most popular and widely used programming languages in the world in recent years. Its uses range from machine learning to building web pages and also to software testing. Python is a general-purpose language and its usage is wide enough to cover data science, software and web development, automation, etc.

Python can be used for the following purposes in an organisation:

Developing web pages and software.
Automation on scripting.
Data analysis and data visualisation.
Software testing and web development.

The main focus of the scores is python’s usage and data analysis and machine learning. Python has become a basic requirement for data analysts and scientists to conduct complex statistical calculations in data science. It also has to create data visualisation, create machine learning algorithms, analyse and compare data, etc.

Data analysts and data scientists use the graphic visualisations that python builds. The visualisations include graphs, charts, histograms, and flowcharts that give an official representation of the data that has been assessed. Based on that data the future course of action for the organisation will be taken by the authorities.

Python also has a lot of libraries that can help programmers code programs for data analysis more quickly and efficiently. It helps the data to become more concise and precise.

Essential Tips

Here are some essential tips for building a data analytics project with Python:

Use a python cheat sheet for syntax.
Clean and rid your data of noise.
Use Python’s interface and the available plugins to prepare the data without the need to write too much code.
Use an online compiler for projects such as Google Collab that can run your project in real time.
Use libraries such as pandas, NumPy, Tensorflow, PyTorch, SciPy and scikit-learn.
Use Matplotlib for visualisations.
Format and structure your target data for efficient analysis.
Use statistical methods for handling and processing your data.
Use nested list comprehension to get rid of for loop complexities.

Conclusion

Data analytics is a great discipline to be a part of and to have a successful career. Previously data was collected from a single source in a standard format but now as data science has become more advanced, data is collected from multiple sources and the requirement of data scientists and data analysts is increasing day by day. Learn Python online training course with Imarticus and kickstart your career towards growth. We offer courses with placements for your bright future.

You want to learn artificial intelligence: What next?

Posted on September 2, 2022March 29, 2024 by Imarticus Learning

You want to learn artificial intelligence: What next?

Artificial intelligence is an evolving technology that has been providing us with a plethora of job opportunities. With the advancement of data science and technology, artificial intelligence has made a mark on the lives of people. Not only locally, AI and machine learning also have a huge impact on the market. If you are looking for a course to learn python online, or learn artificial intelligence online, keep reading to know about this certificate course provided by IIT Guwahati and excel in your skills.

AI or Artificial Intelligence is a technology that helps the machine to simulate human behaviour accurately. Artificial intelligence is a very trending technology in the course of data science which is the future for creating an efficient intelligent system. Sometimes, amateurs confuse AI and machine learning to be of the same category, however, it is not the same as one another. Machine learning, on the other hand, is a subset of AI which helps a machine learn past data that has been processed into it without being explicitly programmed to it.

AI and machine learning however do have the common goal to simplify complex problems to be solved much more efficiently. If you have pursued a career in data science and want to excel in your further knowledge in AI, this Machine learning certificate course by IIT Guwahati will be beneficial for you. You can learn about the principles of AI and machine learning with industry approved curriculum.

Certification In Artificial Intelligence And Machine Learning At E&ICT Academy, IIT Guwahati

IIT Guwahati has been offering this lucrative course to learn artificial intelligence online which will help to excel in the career of the learner in the field. IIT Guwahati has been offering a plethora of course materials based on this subject which will help you excel in your knowledge of AI and machine learning.

The faculty who will be taking the classes for this course are highly trained and will provide the learners with their keen insights. Learn about the prerequisites, job opportunities and other important notions that will benefit you in taking up this career.

Prerequisites

If you are interested in pursuing this certificate course on artificial intelligence and machine learning, then you should have a bachelor’s or a master’s degree in either of the following disciplines. They are:

Computer Science
Engineering
Maths
Statistics
Economics
Any other science stream subject.

The learner should have a minimum of 50% in aggregate in graduation.

Job Opportunities

With the advancement of the disciplines in data science, AI and machine learning is increasing by leaps and bounds. This method has been adopted by several multinational corporations and there are swathes of inspiration for learners who are thinking of taking up this course. The base salary of a Data scientist in India is an average of Rs.881,097 in a year. The job opportunities after completion of this course are feasible and will perk up career opportunities. Besides, the course has given job opportunities to many learners in Deloitte, Infosys, KPMG, Accenture, Kotak Mahindra Bank etc.

Assignments And Mode Of Training

Learners have to do online training classes for getting this certificate course. They have to dedicate 8hours a week to attending live sessions by academicians and industry experts. There will be more than 100+ assignments alongside 25 real industry projects which will help in providing practical knowledge to know about JupyterHub, NumPy, OpenCV etc.

Conclusion

AI and Machine learning has been providing a plethora of learning opportunities to individuals which helps in advancing careers for individuals in data science. You should check out Imarticus’s website about pursuing the Certification Of Artificial Intelligence And Machine Learning which will give you a keen knowledge of data science and its different technological subsets.

The domains of AI and Machine Learning (ML) are one of the fastest-growing sectors in the world. We are increasingly getting more dependent on services and products that are driven by AI and ML. AI/ML makes our systems faster, making our jobs easier. In this domain, there are always innovations being introduced and researched. Thus, the AI/ML sector is an extremely lucrative sector to join.

I chose the wrong data science course and how you can learn from my mistake

Posted on August 29, 2022March 29, 2024 by Imarticus Learning

I chose the wrong data science course and how you can learn from my mistake

The field of Data Science is ever-growing. People from all walks of the IT sector are getting trained in data science as it seems to be one of the most promising career paths of the future, If you know your way around the field, then you know that data science is exactly that, a science. Not just a part of IT and business. A lot of people are opting for data science online training to build a solid career in data science. But what if I told you, that one of the biggest mistakes you can make is choosing the wrong data science online training course and wasting your precious time? Not only that but there are a lot of mistakes you can make while self-studying for data science. This article can help you refrain from making these mistakes and tell you which path will take you to the top fast.

Some Common Mistakes People Make while Learning Data Science

Now that we have delved into the topic, let us talk about some of the most common mistakes people make out of misconception while learning data science.

Choosing the Wrong Course

You will have a lot of choices to choose from if you want to learn data science. There are many courses, books, videos and blogs at your fingertips. But overwhelming yourself with options is not the right way to go. Instead, focus on the courses that match your learning style and choose books and blogs that seem easy to you.

You have to slowly level up in this field and it can take a long time. One of the courses that can help you in this field is the IHUB Data Science and Machine Learning certification course. Which is an IIT Data Science Certification Course to help people get started on their career path as data scientists. It is a comprehensive course that teaches you the basics of data science and machine learning.

Lack of Projects

Learning data science includes a lot of studying, but it also involves a lot of hands-on projects. A lot of the time, people get so immersed in studying that they forget that in order to have a good career in data science, they will also need practical experience in the field. The most important thing to do alongside studying is making sure you work on a project for each thing you learn. That way, you will have real-life experience of what you are studying.

Lack of Time Management and Planning

One of the mistakes aspiring data scientists make while learning the subject is underestimating how much time and commitment it takes to properly learn data science. Another one is lacking a solid plan. Depending on your study methods and what you want to learn, you have to have a future plan for your study course. It should include all the courses, books, topics and projects you want to take on. This way, you will understand the subject better and also manage your time well.

Too Many Research Papers

Last but not the least, make sure you do not get too lost in research papers associated with your topics. It is normal to want to expand your horizons and learn about what the experts have to say about the topic you are pursuing. But often these high-end research papers can be too hard to comprehend for new learners. Better keep these for when you have a better understanding and grasp of the subject.

Conclusion

Now that you have learned which mistakes to avoid while learning data science, you can go about your way to pursue the subject. Remember that you can always learn from your mistakes down the line and make yourself better in the future. Data Science is a very specialised domain and you must be extra careful to avoid the mistakes that have been discussed. Making these mistakes can lead to you wasting your precious time, money and other resources.

For instance, if you do not take up real-world projects and simply keep learning from study material or lessons, you will lack the experience to be job-ready. Companies are also more likely to hire you if you have deployed projects rather than how many courses you have completed. However, a well-rounded certification such as the one offered by Imarticus can help out a lot.

How to learn natural language processing if you’re bad at math

Posted on August 29, 2022July 26, 2024 by Imarticus Learning

You have nothing to fear if you believe your math skills are lacking. You must be familiar with math principles to use natural language processing. Even if studying arithmetic is challenging, using the appropriate approach will be helpful. You only need to know math fundamentals to learn about natural language processing. To groom your math fundamentals in data science, you have multiple e-learning platforms that offer you some of the best data science courses with new-age curriculum and syllabus.

The most crucial aspect is to have a curious mindset and a learning attitude. This will help you identify the simple methods to solve the technical issues. You don’t have to worry if you are not a professional in math since learning NLP requires basic math such as Linear Algebra, Calculus, probability, and statistics.

Natural language processing is the branch of Computer Science that deals with Artificial Intelligence. Here, the computer is given the ability to understand the text and spoken language of human beings. Rule-based modeling, statistical learning, and deep learning models are used in Natural language Processing. These models are essential for the computer to comprehend spoken language.

Since machine learning uses data to learn the mathematical relationship between input and output, it is necessary to know basic math. In NLP, math is concerned with simply calculating demographic data and customer group identification. It deals with predictions and classification.

Hunt the Textbooks

Some of the courses for learning Natural Language Processing offers insightful resource material. It comprises the empirical path of finding out trial and error. You can practice several practice papers and master natural Language Processing without worrying about arithmetic. You may keep track of your progress by testing the codes on the software packages listed in the resource material. You can use the trial and error method rather than concentrating on complex ideas like “Boolean Logic.”

Online

Do It Yourself Tutorial–

You can enroll in the course and use the tutorial lessons to try your hand at NLP. You can follow the simple instructions in these Scikit-Learn, R, and WEKA tutorials. This will enable you to try them out and aid your coding knowledge. Various levels exist, including Beginner, Intermediate, and Professional. You can test all kinds of challenges with the aid of these levels. Apart from that, multiple algorithms will simplify the mathematical equations. Hence, enabling you to learn Natural Language Processing. These online tools help you solve easy to complex math problems since the tool has the formula.

Exploring libraries–

The NLP learning courses offer a vast arena to explore libraries. There are multiple books and online study material for you to browse. Apart from this, the libraries also consist of projects on a trial basis. Thus, enabling you to try multiple demo projects.

Text Pre-processing

The text pre-processing doesn’t require you to be an expert in using it. You can begin learning the fundamentals of NLP with your current level of arithmetic proficiency. The first stage in learning natural language processing may be text processing. The text umbrella approach is used in text processing to prepare the text for additional analysis. Along with the Lexicon, this also includes stemming, lemmatization, tokenization, and stopword elimination. Here,the pre-processing methods are straightforward and accessible.

Keyword Extraction

NLP involves analysing the text and extracting the relevant words. It is the process of extracting keywords and analysing the text. It is an automated process of extracting helpful text by using Artificial Intelligence. The software allows you to extract keywords from your specified category and is pretty customisable. For instance, the automatic keyword extractor works if you want to remove the names. Your ability to guess the names and other pronouns is decreased.

Investigation of Algorithms

The NLP courses focus on investigating pre-existing algorithms. Here, the machine learning online training structure provides projects on which the algorithms have already been applied. Choose the correct algorithm and comprehend the application procedure. This will also aid in your comprehension of how the algorithm was used to generate the desired result. As a result, you can practice the algorithms without dealing with challenging mathematical problems.

Although essential, mathematics is not too complicated for you to understand. You can study NLP with the necessary course materials and carefully selected lesson videos. You can learn logical and abstract NLP concepts by enrolling in the online machine learning class.

For more information about Natural Language Processing, you can contact us by using the link on the webpage.

You can browse our data science course and training facilities in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, or Ahmedabad. You can also contact us using the live chat support, available 24/7.

Frequently Asked Questions

Which topics are included in the data science course?

A beginner’s Data Science course syllabus encompasses fundamental and advanced principles in data analytics, machine learning, statistics, and programming languages like Python or R. The curriculum guides students in interpreting large datasets, recognising patterns, and constructing predictive models. Additionally, the course delves into aspects of data security, ensuring a comprehensive understanding of safeguarding information in Data Science.

Can a fresher apply for a data science course?

Data Science is rapidly becoming a highly sought-after career choice, particularly for fresh graduates with a Computer Science, Mathematics, and Statistics foundation. The job landscape in India is poised for significant growth, with projections indicating the creation of 11 million Data Science jobs by 2026. This underscores the increasing demand and promising opportunities in the field for aspiring professionals.

Who can be eligible for a data science course?

Typically, individuals with science, technology, mathematics, or engineering backgrounds are eligible to enrol in a data science course. The eligibility criteria often include a graduation in Science or Engineering with a minimum aggregate of 50%, where subjects like Mathematics, Statistics, Computer Science, or Information Technology are considered core requirements.

Why learning inventory management is important for a supply chain analyst

Posted on August 28, 2022August 28, 2022 by Imarticus Learning

Why learning inventory management is important for a supply chain analyst

Any company’s inventory consists of a complete list of items like the stock of goods, property, or contents. It is often considered the most vital asset for that company. And inventory management is where all aspects of the supply chain system merge; therefore, a thorough understanding of inventory management is a critical requirement for a supply chain analyst.

A deficit of inventory during needed times might lead to dissatisfaction among customers. On the other hand, a surplus of stock would also cause issues. Proper care must be taken to prevent spoilage, theft, or damage, adequate insuring, etc.

Large companies with highly complex supply chains ought to strike the right balance between keeping too much and too little inventory. One can achieve this through detailed analysis, learning, and inventory tracking by the supply chain analyst.

What is an inventory management system?

The entire process of ordering, storing, tracking, and using inventory throughout the supply chain is called an inventory management system. While previously only very rudimentary methods for inventory management were used, such as spreadsheets, manual order placements, and hand-counted stock records, now it has been replaced by advanced tracking software.

It is also certain that in the future, with rapid innovations in the field of Artificial Intelligence, the Internet of Things, blockchain, and quantum computing, supply chain operators will continue to transform the inventory management system drastically.

How does inventory management function?

The primary goal of inventory management is to ensure that the right product is kept in the right place and delivered at the right time. This entire inventory tracking, from manufacturer to warehouses and then the sale, must take place efficiently. For that to happen, inventory management has to abide by three fundamental steps.

1. Purchasing inventory: ready-to-sell goods are directly transferred for storage in the warehouse or given up for sale.

2. Storing inventory: goods and materials are stored until required. At a later point, they are transferred into the fulfillment process and made ready to sell.

3. Profiting from inventory: the amount of output available for sale is regulated, and finished goods are held in the inventory for a period, which increases the value and thereby adds to inventory profit.

Inventory management for supply chain professionals

Effective inventory management can significantly facilitate the flow of goods and services between businesses to their respective customers- the primary responsibility of supply chain professionals. Supply change management also includes the warehousing and movement of raw materials, work-in-process inventory, tracking of finished goods, and order fulfillment from the point of origination to the final sale. Below is a list of features of inventory management that greatly helps supply chain analysts and professionals achieve their goals:

· Inventory tracking: knowing exactly where a good or product is located enables supply chain professionals to get hold of it and transfer it forward in the supply network.

· Order management: tracking orders, customizing pricing, initiating the return and subsequent refund, and sending quotes all ease the transaction between customers and the business.

· Transfer management: since the essence of supply chain management is enabling inventory movement and storage, transferring product to where it is most valued is an indispensable quality in the supply chain relationship.

· Reporting and analytics: inventory management’s reporting and analytics aspects permit supply chain analysts to evaluate and predict future sales- this would significantly reduce the chances of unprecedented shortages or surplus in goods.

· Purchasing: creating, managing, and tracking purchase and orders simplify the supply chain network.

· Shipping capabilities: automating shipping of goods from the inventory would eliminate the risks of late deliveries or delivering wrong packages.

A career in supply chain management

The purview of supply management is vast- it encompasses aspects from manufacturing, packaging, storing, transporting, logistics, and final delivery- therefore, it creates multiple job positions from different sectors. Some of the most notable careers in supply chain management are production planning, Logistics Resource planning, Transport administration, Warehouse management, Quality management, Purchasing management, etc.

The diverse sectors incorporated in supply chain management offer numerous job roles and positions for employment-seekers. As India is experiencing a surge in industrial companies and markets, the demand for supply chain professionals also mounts. Moreover, as you enter the supply chain industry, you will have to harbor a wide array of transferable skills- planning, management and administration, evaluation and forecasting, workflow optimization, etc.- that would be useful for any career path.

Young job-seekers can capitalize on this opportunity offered in the supply chain management domain by enrolling in the Professional Certification in Supply Chain Management & Analytics course of IMARTICUS Learning. It is an online course developed by IIM Roorkee that prepares you for the high-demand job roles in supply chain management.

To avail of this course in Professional Certification in Supply Chain Management & Analytics, you can contact us through chat support. You can also drive to our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Hyderabad, Kolkata, Patna, Cochin, Jaipur, Dehradun, Lucknow, or Ahmedabad.