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Tag: Machine Learning Skills

Here’s how to create your own plagiarism checker with the help of python and machine learning

Posted on by Imarticus Learning

Although plagiarism is not a legal concept, the general idea behind it is rather simple. It is about unethically taking credit for someone else’s work. However, plagiarism is considered dishonest and might lead to a penalty. 

It is possible for coders to build their plagiarism checker in Python with the help of Machine Learning. Thus, it is advisable to undertake a python course to get a comprehensive idea about this programming language. 

Here, you will get an idea of creating your own plagiarism checker. Once finished, individuals can check students’ assessments to compare them with each other.  

Python Is Perfect for AI and Machine Learning
Python Is Perfect for AI and Machine Learning

Pre-requisites

To develop this plagiarism checker, individuals will need knowledge in python and machine learning techniques like cosine similarity and word2vec.

Apart from these, developers must have sci-kit-learn installed on their devices. Hence, if anyone is not comfortable with these concepts, then they can opt for an artificial intelligence and machine learning course. 

Installation    

How to Analyse Text 

It is not unknown that computers only understand binary codes. So, before computation on textual data, converting text to numbers is mandatory. 

Embedding Words  

Word embedding is the process of converting texts into an array of numerical. Here, the in-built feature of sci-kit-learn will come into play. The conversion of textual data into an array of numbers follows algorithms, representing words as a position in space. 

How to recognize the similarities between the two documents? 

Here, the basic concept of dot product can be used to check the similarity between two texts by computing the cosine similarity between two vectors. 

Now, individuals need to use two sample text files to check the model. Make sure to keep these files in the same directory with the extension of .txt.

Here is a look at the project directory – 

Now, here is a look at how to build the plagiarism checker 

  • Firstly, import all necessary modules. 

Firstly, use OS Module for text files, in loading paths, and then use TfidfVectorizer for word embedding and cosine similarity to check plagiarism. 

  • Use List Comprehension for reading files. 

Here, use the idea of list comprehension for loading all path text files of the project directory as shown –

  • Use the Lambda function to compute stability and to vectorize. 

In this case, use two lambda functions, one for converting to array from text and the next one to compute the similarity between two texts. 

  • Now, vectorize textual data. 

Add this below line to vectorize files.

  • Create a function to compute similarity 

Below is the primary function to compute the similarities between two texts.

  • Final code

During compilations of the above concept, an individual will get this below script to detect plagiarism.

  • Output 

After running the above in app.py, the outcome will look as – 

But, before you create this plagiarism checker, you might need to enroll for a python course or an artificial intelligence and machine learning course, as this programming needs concepts from python and machine learning. 

But, if you are willing to take programming as a career, a machine learning certification might be ideal for you. Nevertheless, to create a plagiarism checker of your own, make sure to use the steps mentioned above to detect similarities between the two files. 

Level 1
Copyscape Premium Verification 100% passed
Grammarly Premium Score 95
Readability Score 41.5
Primary Keyword Usage Done
Secondary Keyword Usage Done
Highest Word Density  To – 5.17%
Data/Statistics Validation Date 15/12/21
Level 2
YOAST SEO Plugin Analysis 5 Green, 2 Red
Call-to-action Tone Integration NA
LSI Keyword Usage NA
Level 3
Google Featured Snippet Optimization NA
Content Camouflaging NA
Voice Search Optimization NA
Generic Text Filtration Done
Content Shelf-life NA

How digitization through artificial intelligence and machine learning technologies has gained momentum post COVID-19?

Posted on by Imarticus Learning

In just a few months, the COVID-19 pandemic has managed to do what normal times would have taken years to achieve – a paradigm shift in the way companies in every industry and sector do business. Artificial intelligence and machine learning have been at the forefront during these challenging times. 

As the world gradually finds its way back to usual ways of life, it is interesting to see how the global crisis has paved the way for behavioral shifts, learning, and innovation. 

AI and ML in the Post-Covid-19 World

With the acceleration of digitization through Artificial Intelligence (AI) and Machine Learning (ML), digital sales have seen a boost, and businesses have focused their tech investments on cloud-based products and services. From online grocery stores and EdTech sites to online pharmacies and OTT players, the post-COVID-19 world looks very different through the AI and ML lens.

So, here are some examples to show how AI and ML technologies have gained momentum post-COVID-19:

  • AI and ML have been impacting the healthcare industry since long before the pandemic hit. AI algorithms have and continue to help in quickly sifting through large datasets to help identify similar diseases and their possible cures to accelerate the COVID-19 research work. 
  • AI and automation technology have also eased the healthcare sector’s administrative load by automating various processes. For example, data processing algorithms to extract data from internal systems and automatically generate medical reports and necessary audit trails have gained momentum post-pandemic. 
  • Also, advancements in ML will continue to help create new revenue streams. For example, scientists, drug researchers, and pharma companies are increasingly turning to AI and ML data processing algorithms to facilitate vaccine and drug discovery and their possible impacts on people. 
  • Lockdowns and social distancing norms have boosted online markets and the digital economy. However, even when the pandemic is gradually ebbing, customers are expected to continue using doorstep services as they did during the peak crisis. Hence, technologies like Augmented Reality (AR) and Virtual Reality (VR) have increased among eCommerce platforms to deliver a better customer experience. 
  • Talking about customer experience, the online retail industry has ramped up its use of AI chatbots and smart assistants to attend to the ever-increasing numbers of digital customers. Hence, the use of AI has helped streamline digital services, online ordering, and delivery systems. 
  • The pandemic has given rise to a digital workforce. To this end, the use of AI to quickly process applications, scan for eligibility and qualifications and perform other mandatory hiring checks has become the norm and is only expected to increase in the near future. 
  • The financial sector has also seen a dramatic rise in the use of AI and automation to serve its customers better and quicker during challenging times. For instance, banks leverage AI to help customers safely upload documents, categorize them and expedite processes without any delay. 
  • Lastly, greater digitization has also increased the risks of cybersecurity threats during the pandemic. While conventional cybersecurity risk management systems have failed to keep up with evolving cyber threats, AI offers innovative defenses. The pandemic has only nudged organizations to adopt holistic approaches to cybersecurity through AI and ML and create an integrated security system. 

How to Find the Best Artificial Intelligence Course?

If you want to learn AI and get a certification in AI and ML, opting for an online course can be the best call. But before you sign up for the course, ensure that it offers hands-on experience with real-world projects and has a curriculum with extensive coverage of concepts related to machine learning, NLP, deep learning, data science, and computer vision. 

Artificial Intelligence skilling has to start from a young age! How? Explore…

Posted on by Imarticus Learning

The chasm between machines and living things is shrinking. Artificial intelligence (AI) is deeply rooted in all aspects of technology, from robots to social networks. India has the potential to skyrocket in the domain of Artificial Intelligence and surpass USA and China, largely owing to:

  • It’s deep-rooted IT &ITeS infrastructure
  • Innovation ( India ranked among the top 50 countries in the Global Innovations Index 2020)
  • Accessibility to large datasets

These have pioneered more than a handful of start-ups and private investments in this sector. For AI to flourish further, there needs to be a nationwide upskilling of the younger generation in Artificial Intelligence Training. The GenZ needs to be acquainted with the theoretical and practical aspects of AI application to increase its scope of innovation and entrepreneurship.

Artificial Intelligence CareerIn the future, the interaction between humans and AI will define in a lot of ways the structure and functioning of a modern-tech society.

Thus it becomes imperative to lay down the basis of friendship for the years to come by exposing the young ones to AI.

While a lot of minds will wander to an Artificial Intelligence Career it is also important that others are no less familiar with the upsides and downsides of such a powerful technology.

Here is how we can ensure the frontiers of the same:

  • Introduce young people to the concepts of AI and machine learning through education curriculum. In India, the Central Board of Secondary Education (CBSE) announced the integration of AI in partnership with IBM for the academic year 2020-21
  • Encourage learning through hands-on projects so that student can make better, informed and critical use of these technologies
  • Enrolling young minds on various Edu-tech platforms specializing in the field of Machine Learning and AI which help them gauge interest and real-life applications of such technologies using intuitive software

Some of these websites include- Scratch, App Inventor, Cognimates etc

  • Experiments with Google is an easy-access, affordable, and user-friendly tool to explore artificial intelligence training at a young age with exciting experiments on AI, VR, AR, Chrome, Voice, Android etc to apply creativity and technological dexterity at the same place. One of these fun-filled learnings includes MixLab that uses voice commands to create music
  • Engage in the practice of cultural inquiry – like what is the goal of You tube’s recommendations or how do my Amazon purchases reflect on my Instagram feed
  • Lastly, before introducing your children to the world of AI and machine learnings, self-education of the same is very crucial

Apart from exploring the possibilities of AI, these junior minds also need to know the limitations of AI to have a balanced approached. That is to say, AI is not the ultimate machine as it is created by humans and will improve along the way by errors made and rectified by humans.

Artificial Intelligence CareerIn recent studies, a scientist is experimenting to teach AI to learn like a kid. They want to inoculate the eager learning attitude and swift skills of young people into the algorithms of machines.

And, AI does not create everything. It is the innovation and vision of responsible human beings that will introduce, implement, and maintain the technological structure in human society.

7 Key Skills Required For Machine Learning Jobs!

Posted on by Imarticus Learning

Overall, 2017 saw an upward trend in talent acquisition across Machine Learning. This will further increase in 2018.
With technology such as Machine learning, AI, and predictive analytics reshaping the business landscape, software products, aggregators, Fintech, and E-commerce will drive the demand for technology professionals in India.

Machine Learning is usually associated with Artificial Intelligence (AI) that provides computers with the ability to do certain tasks, such as recognition, diagnosis, planning, robot control, prediction, etc., without being explicitly programmed. It focuses on the development of algorithms that can teach themselves to grow and change when exposed to new data.

Now, are you trying to understand some of the skills necessary to get a Machine Learning job? A great candidate should have a deep understanding of a broad set of algorithms and applied math, problem-solving and analytical skills, probability and statistics, and programming languages.

Here is a list of key skill sets in detail:

Programming Languages like Python/C++/R/Java

If you want a job in Machine Learning, you will probably have to learn all these languages at some point. C++ can help in speeding code up. R works great in statistics and plots, and Hadoop is Java-based, so you probably need to implement mappers and reducers in Java.

Probability and Statistics

Theories help in learning about algorithms. Great samples are Naive Bayes, Gaussian Mixture Models, and Hidden Markov Models. You need to have a firm understanding of Probability and Stats to understand these models. Use statistics as a model evaluation metric: confusion matrices, receiver-operator curves, p-values, etc.

Data Modeling & Evaluation

A key part of this estimation process is continually evaluating how good a given model is. Depending on the task at hand, you will need to choose an appropriate accuracy/error measure (e.g. log-loss for classification, sum-of-squared-errors for regression, etc.) and an evaluation strategy (training-testing split, sequential vs. randomized cross-validation, etc.)

Machine Learning Algorithms

Having a firm understanding of algorithm theory and knowing how the algorithm works, you can also discriminate models such as SVMs. You will need to understand subjects such as gradient descent, convex optimization, quadratic programming, partial differential equations, and alike.

Distributed Computing

Most of the time, machine learning jobs entail working with large data sets these days. You cannot process this data using a single machine, you need to distribute it across an entire cluster. Projects such as Apache Hadoop and cloud services like Amazon’s EC2 makes it easier and cost-effective.

Advanced Signal Processing Techniques

Feature extraction is one of the most important parts of machine-learning. Different types of problems need various solutions, you may be able to utilize really cool advanced signal processing algorithms such as wavelets, shearlets, curvelets, contourlets, bandlets.

Other skills:

  1. Update yourself:

    You must stay up to date with any up and coming changes. It also means being aware of the news regarding the development of the tools (changelog, conferences, etc.), theory, and algorithms (research papers, blogs, conference videos, etc.).

  2. Read a lot:

    Read papers like Google Map-Reduce, Google File System, Google Big Table, The Unreasonable Effectiveness of Data.

The next question you would have is, “What can I do to develop these skills?” Unless you already have a strong quantitative background, the road to becoming a Machine Learning Specialist will be a bit challenging – but not impossible.

However, if it’s something you’re sincerely interested in and have a passion for Machine Learning and lifelong learning, don’t let your background discourage you from pursuing Machine Learning as a career.

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