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Category: Analytics

Robot pizza delivery: The tale of AI and smartest self-driving engineers

Posted on by Imarticus Learning

The demand for AI certification courses is increasing day by day. People are interested in understanding this technology and learning how they can utilize this knowledge for their growth and how they can come up with unique innovations to help make their lives easier. One such innovation is the Robot pizza delivery system!

The tale of self-driven pizza delivery ‘robots’

When you order pizza online, you will see options- takeaway and delivery. Now, people in Houston, Texas have an additional option for their Domino’s Pizza delivery, NURO 2. It is a smart robot that delivers your pizza to your doorstep. Customers can track these self-driven robotic vehicles on GPS. On delivery, they need to enter the PIN to get access to their orders. 

NURO is a startup founded by Dave Ferguson and Jiajun Zhu. This AI-based company added robotics into the picture to produce the spawn of their original project Google’s self-driven cars. The founders were the leads of the projects that included Machine Learning, computer vision, prediction, for the former and the latter handling the perception and stimulators.  

After quitting their jobs with Google, they founded this company and now have thousands of employees and are making hundreds of self-driving robotic vehicles for deliveries. 

What it means is that their core technologies involve AI and Machine learning which helps achieve this feat. It also shows that even a startup by someone having expertise in the technology can achieve much more and faster than the giants in the field. 

Learning AI and ML

These days one can easily find a course to learn AI and ML. But what matters more is what exactly you learn and from where you get the expertise. When you choose a course, it’s better to be from one of the leading institutions such as the IIT. 

You can enroll for the IIT AI ML course, the Certification In Artificial Intelligence & Machine Learning, By E&ICT Academy, IIT Guwahati. It is a 36-week course with a 3-day on-campus immersion at the IIT. The curriculum covers all the latest technologies associated with AI to help you get a strong base of the technology. 

Participants will be getting to work on the ML and Deep learning projects to shape them into experts in this field and prepare for the in-demand job profiles. 

More about the AI ML course

This Artificial Intelligence certification is not for novices but for those with a degree in related subjects such as Computer, engineering, science, maths, economics, or statistics or having at least 2 years of experience in related fields. 

The classes will be online-mode except for the 3 days on the campus. The live training classes will be conducted by experts in the industry and by the professors at the academy. 

There will be a Capstone project towards the end of the course which will be from various industries such as real-estate, security and surveillance, mobile manufacturing, hospitality, airline, marketing, healthcare, advertisement, education, e-commerce, etc. You can choose your project as per your interest. 

Apart from the project the participants will be getting mentorship assistance for motivations, tips, and encouragement for the progress, and will also get long-term assistance and connections, even after the course. 

Conclusion

The certification in AI is available for beginners and experts and one can choose the most suitable one according to the industry and interests. The prospects of using AI in day-to-today lives is increasing exponentially. Those who have creative visions must surely learn more about this technology. Who knows, maybe you can also come up with such innovations as the robot pizza delivery!

Here’s how digital transformation will impact supply chain analytics course training

Posted on by Imarticus Learning

The recent tide of digitization has impacted all corporate sectors heavily. And the supply chain management sector is not left behind. From logistics, and manufacturing to customer satisfaction, digitization has impacted this sector massively. Now the question stands at what changes or impacts digitization has had on the supply chain analytics course training.

Obviously, with a new form of trading system, the training for that is also going to enhance or differ from the traditional system a bit. The best thing you can do here if you are interested in a supply chain analytics career is to pursue a proper supply chain analytics course training. 

A lot of institutes in India offer a stellar supply chain analytics certification course. Imarticus Learning with their incorporated IIT supply chain management course will help you hone your skills to the max. Not only that, but their real industrial exposure and placement offers will help you start your career right off the bat with great opportunities. Here, we are going to talk about how digitization will impact the supply chain analytics course training and what students will be learning in this training. Please go through the whole article to learn more.

Real industrial case files

An approach to teach and learn about the supply chain system with real industrial cases. These include but are not limited to topics related to procurement, warehousing, production, and distribution as well as planning. This segment will elaborate on how digitization can and will help in the betterment of these processes. So that they run smoothly, efficiently and stay up to par with customers’ needs.

Supply chain analytics and more

Digitization of the supply chain analytics will also need the professionals to have a skill set that can provide the companies help with any problems they might be facing in the digital analytics department. For that, a training course will need to focus on supply chain analytics, inventory management as well as machine learning forecasting, and so on.

A managerial approach to cases

Another thing the courses will have to focus on is a managerial approach to real industrial cases. Although a digitization process seems at its heart tech-centric, in the supply chain management sector, what needs to be done is to build management professionals who can maintain the smooth flow of operations through advanced digital means.

Intelligent optimization techniques

With intelligent transportation, logistics network design, transshipment, drones as well as linear programming and sensitivity, students are going to enhance their skills of supply chain optimization techniques in their course training with models. This will ultimately help them tackle real problems of the industry later on in their careers.

The cyber designing system

Students will also need to learn about the basics of cyber designing systems. That is, they will need to learn how to build as well as interpret a linear model to predict trends. With digitization impacting the course, the segment will focus on visualization techniques as well as pre-processing. Data collection and analysis are a crucial part of the model-building process.

Big data analytics

As the market moves forward to more niche problems, big data comes in to solve many of them. At once, the impact, as well as the value of inventory analytics in this process, is irreplaceable. By storing and analyzing this, it provides a lot of insight into the past trends, present conditions as well as future predictions. The training will be focusing on value, tools, and trends as well as the economic indication and financial performance of the inventory management. 

Conclusion

Digitization of the supply chain management is a big step forward to make the system transparent and customer-friendly. For people looking into a career in this sector, check out Imarticus Learning’s supply chain management certification course and give your career the push it needs.

AI Courses: The Future of eSports?

Posted on by Imarticus Learning

Over the last few years, eSports has gained popularity. There are various online games and leagues where players can participate and win. This industry is currently booming but it can experience more gains through the implementation of AI and machine learning. If you are interested in a career that combines AI with eSports, then you can choose an artificial intelligence certification from Imarticus Learning. 

How Can AI Influence the Future of eSports?

Artificial intelligence, in the current times, influences almost every industry. eSports or electronic sports can benefit from AI and machine learning as it is online and already relies on technology. Thus, its presence and outcomes can improve through the correct implementation of artificial intelligence. If you have an AI certification from a reputed institution, you can opt for a career in the eSports sector and use your AI and ML skills to make a difference. 

Take a look at the aspects in which artificial intelligence can affect the future of eSports. 

 

  • Better Sponsorship Deals

 

For eSports leagues to improve viewership and become more popular, an online or social media presence is necessary. With artificial intelligence and machine learning solutions, you can increase the visibility of a particular eSports platform or even a game. This leads to better sponsorship. Sponsors offer deals to leagues or games that already have a following and can provide a platform for advertisements. Therefore, to receive more sponsorships, and become visible to potential investors, eSports companies can use artificial intelligence tools. 

 

  • Improvement in Broadcast

 

Broadcast is only possible if the right audience is present to watch the games. Analysis through artificial intelligence can determine which is the ideal audience. AI can analyse audience responses and predict what kind of games will gain the most views when broadcast. 

 

  • Advanced Training in eSports

 

Artificial intelligence is primarily used for accurate analysis. In eSports, AI solutions can analyse data across platforms and leagues to determine what the condition of the players are and how the audience is responding to certain games. After analysing individual players, the insights can provide important information on how a player can improve their techniques. This ensures advanced training and better outcomes. 

Where Can You Receive the Best Training in AI and Machine Learning?

A degree in artificial intelligence and machine learning can open many doors in multiple industries. To receive holistic education in AI and ML, you can opt for Imarticus Learning’s Certification in Artificial Intelligence & Machine Learning. For this course, we have collaborated with the E&ICT Academy and IIT Guwahati, to provide you with the best hands-on training on AI and machine learning. Our course curriculum has been created by industry experts and renowned academicians who have been in the field for a long time. Therefore, you will get to learn about current business conditions and industry needs. The IIT AI ML course will include more than a hundred assignments and twenty-five industry projects. This course is ideal for students who have a Bachelor’s or Master’s degree in Mathematics, Science, Engineering, Economics, Statistics, or Computer Science. If you have scored at least 50% in your graduation, you can apply for the AI ML course. At Imarticus Learning, we provide the best training and hands-on experience through this online course. 

The certification in artificial intelligence & machine learning will help you learn about the implementation of AI on various industry processes and organisations, including eSports. You will learn through live lectures where our academicians will interact and encourage you to participate in learning sessions. This course is, therefore, ideal for networking and landing a rewarding job. 

Hands-on Python and R in Data Science

Posted on by Imarticus Learning

Hands-on Python and R in Data Science

R and Python are both equally great programming languages. However, each has its own set of advantages that it offers to the user. For example, when we are talking about development processes or IT operations, Python is always a better option. But when it comes to statistical tasks or analytics, R can prove to be a much more suitable alternative.

This is because R has been created for Statisticians and for statistical projects. Skilled programmers can, however, employ any of the two languages to perform any task. For instance, Python can be used for using statistical techniques like Regression Analysis or Bayesian inference on datasets. Similarly, R can also be used for building new Data Science tools and data models.

Python is quite easy to learn with simple syntax while R has various language boundaries. One can easily learn Python online with the help of a course. However, R is far superior in graphical and statistical procedures. R is more scientific in nature and will be much easier for statisticians or professionals who have worked with MATLAB, another scientific programming language.

Python is more focused on running algorithms and makes it easy to build programs in general. In Data Science, however, both are equally important and competent languages. R and Python can work with massive databases and are equally good at Machine Learning projects. 

R in Data Science

R is used in Data Science for graphical and statistical purposes. This language can help users create advanced visualisations and high-quality graphics as well as dynamic graphics. R is also great for data mining and statistical computing in general.

R has a set of functions that allows datasets to be loaded into the memory with program statements, but that is only required if you are going to be building R programs that will keep getting used for various functions. Otherwise, you can simply use the data import function in R Studio (IDE for R). R is able to work with data in two different formats in R studio, CSV and TXT. Once you select the dataset you wish to be working with, R will load the dataset and you can then work on it.

Python in Data Science

Python is a high-level programming level that does not require compilation before running code. This makes it very fast to use for all kinds of projects, including Data Science and Data Analytics. Python is extremely flexible and is a multi-paradigm language, thus, allowing Data Scientists to use different approaches and keep improvising the language with extensions and plugins.

Before loading a dataset in Python, you must first import pandas (library) and install it in Python. After that, a custom function must be created that loads the dataset for you. By creating a load_csv function, you can fundamentally provide an argument for your dataset’s file path. Also, the readlines() is used in order to ensure that the program returns a list containing the lines inside the .csv dataset. You can also additionally present the data in a more visually appealing way by returning the dataset in a dataframe format. This makes it easier to view the data as compared to the native list format or NumPy arrays.

Conclusion

Both the languages also are open-source and have huge communities behind them to keep providing new libraries. There are also a good number of updated tools and powerful IDEs (Integrated Development Environments) available for both R and Python.

If you wish to learn Python or R for Data Science, you should definitely opt for a solid Post Graduate Program in Data Science. You can also choose to take up a Machine Learning course with placement or a Data Analytics course with placement as well. 

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Why Should You Learn Python For Data Analytics and Artificial Intelligence?

Posted on by Imarticus Learning

2022 has seen a huge increase in both the number of data science applicants and also future aspirants all around the world. In India alone, LinkedIn, the global job search leader, announced a 25% spike in data science professionals as compared to 2021, and that is a huge number by a number of standards.

But one of the most common questions that ponders the mind of data science aspirants most often is why they should learn Python to get an edge in this profession?

Thus if you are planning for a career in data analytics and artificial intelligence in the future, and too have this question in your mind, in this article, we will answer exactly that.

Let’s get started.

Why Is Python Important for Data Science?

At the present moment, there are more than 35 different programming languages that are actively used by developers and coders all around the world. But among all of these, Python is undeniably one of the most versatile and well equipped, especially in the field of data science.

The reason behind this is simple; if you take a look at some of the most common tasks that are executed by a data scientist on an everyday basis, starting from data extraction and ELT (Extraction, Loading, and Transformation) all of them require a solid knowledge of Python coding and operation.

Along with this, another important reason why Python is so often relied upon as the go-to programming language for data science professionals is the fact that it comes with a suite of different packages, starting from SciPy, NumPy and pandas, which make complicated and time-consuming tasks easier, more efficient and effortless.

For instance, Matplotlib, which is one of the leading Python packages, is often used by data science professionals when they want to include visualizations or any sort of graphics in their simulations.

If all of this is not enough good reason, as to why you should learn Python

Learn Python ProgrammingTo get an edge for your career in artificial intelligence, here are some of the biggest advantages of the standalone programming language in itself.

Advantages of Python

Easy to Learn

If you are a beginner at coding and you are looking for a programming language that is easy and fast to learn, then Python should be your choice.

The programming language has been around since the late 1980s and has thus undergone several developments and improvements in the last couple of years, which has made the current version extremely user and beginner friendly to learn and execute.

Since data science is such a fast-paced career, where developments are literally happening overnight, the need for a language that is easy and fast to learn is a no-brainer, thus making Python one of the most obvious choices.

Scalable

When you would have spent a considerable amount of time coding, one of the first things you will realize is the fact that Python as a programming language is immensely scalable. Compared to programming languages like Matlab and Stata, which are industry leaders in their own might, Python makes it very simple and straightforward for anyone to scale. The language comes inbuilt with a suite of features that allows you to have multiple approaches and perspectives to solve the same problem, along with having a wide range of flexibility to try out new things as and when they are developed.

Learn Python ProgrammingThe scalability aspect of Python is the second reason why companies prefer their data science professionals to know Python. Scalability is immensely important in both data science and artificial intelligence, mainly because of the fact that new discoveries are being made on the regular.

Conclusion

Thus, if you want to give your career in data science an edge, now is the time to start enrolling for an Artificial Intelligence Training that comes packed with a course in Python.

We offer analytics and artificial intelligence courses at our centers in Mumbai, Thane, Pune, Jaipur, Delhi, Gurgaon and Bangalore.

Supply chain analytics certification fundamentals – Best practices

Posted on by Imarticus Learning

Supply chain analytics is one of the most critical aspects of business in the 21st century. More companies now realize the importance of supply chain analytics and implement programs to improve their efficiency every day. If you want to advance in this field, it’s crucial to understand supply chain analytics fundamentals. This guide will discuss the best practices for supply chain analytics in 2022.

What is supply chain analytics?

Supply chain analytics is the process of gathering, analyzing, and reporting data from across the supply chain to help improve performance. You can use it to make a good decision about allocating resources, identifying areas for improvement, and optimizing processes.

The scope of supply chain analytics varies depending on the organization but typically includes:

  • Forecasting and demand planning
  • Inventory management
  • Capacity planning
  • Transportation and logistics
  • Supplier management

Fundamental practices that will be important in 2022

Supply chain analytics will continue to be one of the most critical aspects of business in the 21st century. Here are some fundamental practices that will be important for companies looking to get ahead:

  • Data collection and analysis: Data is one of the essential aspects of supply chain analytics because it provides insight into how your business operates.
  • Demand forecasting: The ability to forecast demand for products and services will help companies optimize inventory levels and avoid overstocking or understocking items that aren’t selling as well as expected. As we get closer to 2022, more demand forecasting tools will be available for businesses looking to improve their efficiency in this area.
  • Inventory management: Identify the best path forward by looking at what your competitors have done to stay competitive and profitable over time, whether that’s inventory control or pricing strategy.
  • Capacity planning (warehouse space): Warehouses are becoming increasingly important due to the growth of e-commerce. As the market for online purchases grows, it’s essential to have enough warehouse space to accommodate all of your products.
  • Transportation and logistics: It is critical for supply chain analytics; by understanding how different modes of transportation impact delivery times and costs, you can identify opportunities to improve efficiency while reducing overall expenses.
  • Supplier management: It’s essential to have a good understanding of your supplier base. Analyzing their performance will help you understand what needs improvement so that they can continue providing high levels of service (and profits) for years to come.
  • Customer service: Customer service is a vital part of supply chain analytics. It allows companies to analyze data about their customers, which you can then use for marketing or other initiatives to improve delivery times and costs.

Discover Supply Chain Management Course with Imarticus Learning

Hands-on training with top professionals in logistics and supply chain management is available through our partnerships. The Supply Chain Management course with analytics offers an experience beyond just classroom learning – it’s the perfect preparation for those students looking to enter one of today’s most lucrative fields!

Course Benefits For Learners:

  • We give students a thorough understanding of the sector through six real-world projects, which will help them prepare for their rewarding career and become Supply Chain Analyst.
  • This IIT Supply Chain Management online course will prepare students to take on competitive jobs such as Demand Planner or Data Scientist.
  • Supply Chain Management course with Analytics is the key to making data-driven strategic, operational, and tactical decisions.

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

AI, Data Science, Machine Learning Terms You Need to Know in 2022!

Posted on by Imarticus Learning

In the present paradigm of technical knowledge, it is imperative to be aware of certain concepts to survive and thrive. Whether you are pursuing a career in artificial intelligence (AI), have a cursory interest in data analytics, or simply wish to broaden your horizons, here are some artificial intelligence, data science, and machine learning terms you need to know in 2021. Read on…

  1.     Natural language processing: 

Both humans and computational devices use their own modes of language to communicate and share ideas to the extent of imparting and debating on the information. The languages, however, are different in their basic forms and formats. Using natural language processing, or NLP, artificial intelligence can decipher many human languages to suit specific functions that may range from the academic study of linguistics to providing utility to hearing-impaired people.

  1.   Data warehouse: 

A data warehouse, as the name suggests, contains a large ensemble of data pertaining to businesses and learnings from past successes and failures to provide better services. One who is not entirely proficient in data architecture may yet take the advantage of data warehouses to gather business analytics courses and make far better decisions. This method allows one to find new ways to process old data and change future iterations of that data with his/her actions. 

Career In Data Analytics   Data journalism: 

This is a mode of journalism that is slowly gaining greater prominence and is proving its necessity in combating the ever-growing trend of fake news. In this form of news reporting, one focuses on proving his/her assertions through the collection and presentation of reliable data. This may be done through human and/or AI collection and calculations. Soon, we may be able to have a collated base of data obtained through AI learning. This will make it very hard for individuals and/or groups to spread misinformation.

  1.   Deep learning:

This uses artificial intelligence to construct structures that mimic the human neural network – starting from simple problems to finding layers of hidden information. Meanwhile, it makes errors and learns from them with the program often ending up with a different solution than what was expected by its programmers and set parameters. Using this process, we can identify and solve possibly any real-world problem. The degree of human supervision in this process can be ascertained at various levels of this process.

  1.   Cybersecurity: 

Both defenders and attackers of databases are getting smarter, escalating the never-ending battles between cybersecurity and hackers. Often, the strategies used by either group are similar to the point of being indistinguishable. Here, any large organization employs AI and/or deep learning to be one step ahead of the threats that plague them.

The above-mentioned terms are only the tip of the iceberg when it comes to talking about new technology-related topics. Hopefully, they have provided you with new avenues to look into as per your interests, or at least recapitulated some of the basic terminologies.

How to succeed in your supply chain management and analytics job search

Posted on by Imarticus Learning

Introduction

The field of supply chain management is exploding. Several new technologies are coming into the space, making it a very lucrative industry. From e-commerce companies to manufacturing companies, all of them use supply chain planning and management. If you want to make a successful career in supply chain management, you can take a supply chain management course with analytics and make yourself ready for the industry. 

There are hundreds of opportunities in the supply chain management area. You can either become a supply chain analyst or a manager in the area based on your experience. Let’s check out how you can make a career in supply chain management.

Making a Career in Supply Chain Management

If you are willing to succeed in supply chain management, you will have to brace yourself for an incredibly high-paced environment. You’ll have to figure out how to save money on inventory and shipping. However, you must prioritize customer service in everything you do. You’ll also have to deal with rising manufacturing costs and monitor the entire process. To summarise, to get the supply management career of your dreams, you’ll need a diverse set of talents.

Understand the career paths

The tasks of supply chain management are numerous. It all depends on the particular career route you choose. You must take some time and do your study to find a course that you can stick with. You can go for a supply chain analytics course training to make yourself aware of the job.

You can also work as a supply chain analyst, which entails tracking and analyzing a portion of the supply chain. You’ll also need to concentrate on process streamlining and ongoing improvement. Working as a buying manager, you’d be in charge of supplier interactions and contract negotiations. You could also work as a Demand Planner, Supply Planner, Transportation Analyst, or Inventory Specialist.

Highlight your skills on your resume

You’ll need to demonstrate softer qualities in addition to your great data analysis and financial prowess. Update your resume to reflect a commitment to effective communication. Include anything that has to do with relationship management and bargaining.

You should also demonstrate that you pay close attention to details. This is crucial because you’ll almost certainly have to undertake site audits. You can also compare and contrast various modes of transportation. To kickstart your supply chain management career, you may need to develop some of these talents. Look through internet job postings and recruitment websites to learn more about these changes.

Compensate for your lack of experience

It will be challenging to transition directly from a logistics course or a profession in a related field to supply chain management. Still, there are methods to compensate for the lack of experience. Internships are one way to get into a supply chain management field that requires prior experience, but they’re generally out of reach for experienced professionals wishing to change careers.

Training and certificate courses, which involve a minimal time commitment and provide a résumé, are an alternate kind of education offered.

Conclusion

We at Imarticus are known for our supply chain management courses. Several SCM courses will help you crack the industry code and then perform well in the industry. We also offer a Professional Certification in Supply Chain Management and Analytics by IIT Roorkee.

This course highlights everything that is there to know about supply chain management, and it also covers the application of analytics in the course. You can take up this course if you are interested in having a career in supply chain management. You will also get a certificate on the completion of the course, which you can showcase in front of the recruiters. It’s time to enroll in the course!

Overcoming privacy challenges in supply chain planning

Posted on by Imarticus Learning

Introduction

Supply Chain Planning is a crucial step in the supply chain management process. When you plan the several factors that can affect your supply chain process, you need to consider the privacy concerns that may arise out of it. Preserving any data involved in the supply chain planning process is crucial, and you should take all the necessary actions to ensure that privacy is maintained.

If you aspire to have a career in supply chain management, you can take up a supply chain analytics course to understand how privacy can be maintained in the entire process. 

A supply chain analytics certification will help you understand the privacy concerns but will also aid in understanding the entire supply chain division with both the advanced and the basic concepts. 

Ways in which privacy challenges can be tackled 

There are several ways to overcome the privacy concerns you might face while supply chain planning. Some of these ways are:

 

  • Go Digital

 

Switching from traditional processes and technology like fax, phone, and email is slow but necessary. Your organization establishes secure data transfer within your group and external trading partners, suppliers, and customers by upgrading to contemporary technologies.

Updating your software and procedures offers you access to data security methods such as:

  • Encryption
  • Tokenization
  • Monitors and alarms for file access
  • Preventing data loss.

You can train your people on how fraud can be prevented by using a digital system and how cybersecurity risks can be reduced if the company’s focus shifts to the digital growth and digitization of the entire supply chain process.

 

  • Encryption and Identification of Data

 

The National Institute of Standards and Technology (NIST) recommends businesses build defenses on the assumption that a breach would occur. As a result, you must protect all forms of data you store or communicate. You can utilize discovery tools to locate and classify files holding confidential information, financial data, or personally identifiable information. 

With this comprehensive view of all your data, you can implement current encryption standards to safeguard your most precious assets. As firms become increasingly reliant on online transactions, enhanced controls such as digital signatures, session breaks, and multi-factor authentication can help to strengthen supply chain security.

 

  • Risk Management through third-party

 

More and more organizations in the supply chain are collaborating to store, transport, and use data. This necessitates more comprehensive risk management, including end-to-end security.

Shared risk assessment among stakeholders is the foundation of effective third-party risk management. You’ll need to break down barriers between your technical and business teams, as well as enlist the help of vendors and partners. You can protect the supply chain’s most valuable assets by banding together. After that, you can determine the extent of any potential operational damage. If data is poorly managed, compliance issues, or any data breach, interference would be required immediately.

Conclusion

We at Imarticus aim to provide the best courses that help make all the students seasoned industry experts. You can enroll yourself in the Professional Certification in Supply Chain Management and Analytics by IIT Roorkee and Imarticus learning. This course in the SCM domain will make you ready for all the challenges that you will come across in the supply chain industry. 

Also, with a certification in supply chain management, you can get into high-paying jobs where you can prove to your recruiters that you are trained to work in the industry. Handling privacy issues is vital in the supply chain industry, and a course will help you understand and manage these issues. Enroll in this course today and reap all the benefits that the course has to offer.

Here’s how to develop a NLP model in Python

Posted on by Imarticus Learning

NLP or Natural Language Processing is one of the most focused upon learning models in modern times. This is especially due to how popular chatbots, sentiment analytics, virtual assistants, and translation tools have become. NLP empowers machines with the ability to process, understand and get meaning out of textual data, speech, or human language in general.

NLP allows other applications or programs to use human language. For example, the NLP model that powers Google understands what the user is searching for and fetches the results accordingly. Python online training can definitely help when one wishes to delve into NLP.

NLP models go much further than just finding the exact type of information and can also understand the context of the search or the reason and fetch similar or related results as well. NLP-powered machines can now identify the intent and sentiment behind the human language.

Developing Learning Models in Python

Python is a great language to use for NLP models as one can take the help of the NLTK package. The Natural Language Toolkit is an NLP package for Python. Additionally, you can also install the Matplotlib and NumPy libraries in order to create visualizations.

First, you need to have Python 3.5 or any of the later versions installed. After this, you must use pip install for installing packages such as NLTK, LXML, sklearn. If you decide to work with random data, you must first preprocess the data. You can use the NLTK library for text preprocessing and then carry on with analyzing the data. 

Here are the 4 steps involved in developing a learning model using Python:

  • Loading and data preprocessing
  • Model definition
  • Model training
  • Model evaluation

How to Develop an NLP Model using Python

Let us learn how to develop an NLP Model in Python by creating a model that understands the context of a web page. Once you have installed the NLTK library, you should run this code to install the NLTK packages:

import nltk

nltk.download()

After this, you will be asked to choose the packages you wish to install, since all of them are of very small size, you can install all of them.

Then, you must find a web page that you want to process. Let us take the example of this page on computers. Now, you must use the urllib module for requesting websites:

import urllib.request

response =  urllib.request.urlopen(‘https://computer.fandom.com/wiki/Main_Page)

html = response.read()

print(html)

Now, we can use the Beautiful Soup library for pulling the data out of the XML and HTML files. Also, this will help us clean the text of HTML tags.

Once, this is done, we can go ahead with converting the text into tokens using this:

tokens = [t for t in text.split()]

print(tokens)

Once the output is returned as tokens, we can use the FreqDist() function in the NLTK library for removing unnecessary words such as (for, the, at, a, and etc.) from our text and then plot a graph for the words that occur the most number of times. After this, the model identifies the most relevant words and then the context of the web page.

Conclusion

The auto-completion suggestions that we are given, the voice searches that our devices carry out for us are all possible with the advancements we have made in NLP. The PG in Data Analytics and Machine Learning offered by Imarticus is a great Data Analytics course with placement and can definitely help you delve deeper into concepts such as Deep Learning and ANN (Artificial Neural Network).