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Tag: Python course

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 To Create An Image Dataset and Labelling By Web Scraping?

Posted on by Imarticus Learning

Every data science project starts with data. We need to acquire a huge amount of data to train our machine learning models. There are various ways to collect data. Surfing websites and downloading the structured datasets present on them is one of the most common methods for data collection. But there are times when this data is not enough. Certain problem statement datasets are not easily available on the web. And to deal with this situation, we need to create our own datasets.

In this article, we will discuss the method of creating a custom image dataset and labeling it using Python. First, let us talk about acquiring images through web scraping.

Web Scrapping

Web Scraping refers to the process of data scraping from websites. It surfs the world wide web and stores the extracted data in the system. Beautifulsoup is one of the most popular Python libraries for image scraping. The requests library requests the essential webpage.

How To:

When we go to the developer tool by clicking on a picture on the webpage, there displays a format starting with images.pexel.com/photos after which a number is listed, which is unique for every photo. One can get a similar image using the regex (regular expression).

Using this method, our images get scrapped. We can also print the links if we want to see those links and make a directory of them. After this, we will download the images. Once the process is complete, you can see the scrapped images through the specified path where images are stored.

Labeling

After scrapping and storing the images, we need to classify them through labeling. Labeling software is used for this purpose. It is a pip installable annotation tool. It provides two annotations YOLO and PASCAL VOC.

How To:

You can open the labeling software using the command: (base) C:\Users\Jayita\labeling

There will be specified options on the left-hand side of the screen. On the right-hand side, you will see the image file information. Select ‘Open dir’ to see all images. Press ‘a’ to view the previous image and ‘d’ to view the next image.

To get the annotations, draw a rectangular box and press ‘w’. A window will pop up to store the image’s class name. Once you are done with drawing the box and labeling the image, it’s time to save it. To generate the annotations, you need to store the image in PASCAL VOC or YOLO format.

One can learn about this in detail in a data science course. Web scrapping and labeling is not a hard process once you understand the basics of it. You need to be careful while scrapping a website and obey the rules so that you do not harm the website you are scrapping. Take time to consider your requirements and research accordingly to find a suitable website for this process. For example, if you plan to develop a model for fashion, then online shopping websites should be on your scrapping list.

Learning web scraping and labeling is important if you want to build a data science career in the future. It will provide you with a deep understanding of image datasets. You can use these techniques to increase the data in situations where available data for a project is less. You can apply this process to multiple classes if they share the same folder and get the desired results.

How Do You Start Applying Deep Learning For My Problems?

Posted on by Imarticus Learning

Deep Learning helps machine learn by example via modern architectures like Neural Networks. A deep algorithm processes the input data using multiple linear or non-linear transformations before generating the output.
As the concept and applications of Deep Learning are becoming popular, many frameworks have been designed to facilitate the modeling process. Students going for Deep Learning, Machine Learning course in India often face the challenge of choosing a suitable framework.
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Following list aims to help students understand the available frameworks in-order to make an informed choice about, which Deep Learning course they want to take.
1.    TensorFlow 
TensorFlow by Google is considered to be the best Deep Learning framework, especially for beginners. TensorFlow offers a flexible architecture that enabled many tech giants to embrace it on a scale; for example Airbus, Twitter, IBM, etc. It supports Python, C++, and R to create models and libraries. A Tensor Board is used for visualization of network modeling and performance. While for rapid development and deployment of new algorithms, Google offers TensorFlow which retains the same server architecture and APIs.
2.    Caffe 
Supported with interfaces like C, C++, Python, MATLAB, in addition to the Command Line Interface, Caffe is famous for its speed. The biggest perk of Caffe comes with its C++ libraries that allow access to the ‘Caffe Model Zoo’, a repository containing pre-trained and ready to use networks of almost every kind. Companies like Facebook and Pinterest use Caffe for maximum performance. Caffe is very efficient when it comes to computer vision and image processing, but it is not an attractive choice for sequence modeling and Recurrent Neural Networks (RNN).
3.    The Microsoft Cognitive Toolkit/CNTK
Microsoft offers Cognitive Toolkit (CNTK) an open source Deep Learning framework for creating and training Deep Learning models. CNTK specializes in creating efficient RNN and Convoluted Neural Networks (CNN) alongside image, speech, and text-based data training. It is also supported by interfaces like Python, C++ and the Command Line Interface just like Caffe. However, CNTK’s capability on mobile is limited due to lack of support on ARM architecture.
4.    Torch/PyTorch
Facebook, Twitter and Google etc have actively adopted a Lua based Deep Learning framework PyTorch. PyTorch employs CUDA along with C/C++ libraries for processing. The entire deep modeling process is simpler and transparent given PyTorch framework’s architectural style and its support for Python.
5.    MXNet
MXNet is a Deep Learning framework supported by Python, R, C++, Julia, and Scala. This allows users to train their Deep Learning models with a variety of common Machine Learning languages. Along with RNN and CNN, it also supports Long Short-Term Memory (LTSM) networks. MXNet is a scalable framework making it valuable to enterprises like Amazon, which uses MXNet as its reference library for Deep Learning.
6.    Chainer
Designed on “The define by run” strategy Chainer is a very powerful and dynamic Python based Deep Learning framework in use today. Supporting both CUDA and multi GPU computation, Chainer is used primarily for sentiment analysis speech recognition etc. using RNN and CNN.
7.    Keras
Keras is a minimalist neural network library, which is lightweight and very easy to use while stocking multiple layers to build Deep Learning models. Keras was designed for quick experimentation of models to be run on TensorFlow or Theano. It is primarily used for classification, tagging, text generation and summarization, speech recognition, etc.
8.    Deeplearning4j
Developed in Java and Scala, Deeplearning4j provides parallel training, micro-service architecture adaption, along with distributed CPUs and GPUs. It uses map reduce to train the network like CNN, RNN, Recursive Neural Tensor Network (RNTN) and LTSM.
There are many Deep Learning, Machine Learning courses in India offering training on a variety of frameworks. For beginners, a Python-based framework like TensorFlow or Chainer would be more appropriate. For seasoned programmers, Java and C++ based frameworks would provide better choices for micro-management.

What is the Learning Curve for Python Language?

Posted on by Imarticus Learning

What is the Learning Curve for Python Language?

Most people will tell you that Python is the easiest language to learn and should be one of the first languages that you should learn when considering a career in Python programming. Well, they are mostly right, parting with a good piece of advice. And most probably you should take these comments seriously.

However, before you kick start with unclear expectations, you should be clear about what does it truly mean by ‘learning the language’, is it being a pro and acquiring absolute knowledge of Python, or to begin with, working knowledge, that helps you start with the basics, while you can continue to learn and gain additional knowledge on the go. Python is an awesome choice, with a relatively faster learning curve, which is determined by various factors and disclaimers.

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For starters, Python should be your first programming language, simply because not only will you be able to pick up the basics quickly you will also be able to adapt to the mindset of a programmer. Python is easy to learn with a steady learning curve.

Especially when compared to other programming languages that have a very steep learning curve. Mainly because Python is very readable, with considerably easy syntax, thus a new learner will be able to maintain the focus on programming concepts and paradigms, rather than memorizing unfathomable syntax.

For those thinking that Python is said to be too easy to learn, perhaps it might not be sufficient, and hence while it could have a gradual learning curve, in terms of applicability it might not be adequate, don’t be misguided. Python is not easy because it does not have deep programming capabilities, on the contrary, Python is superefficient, so much so that NASA uses it.

So as a beginner, when you start adapting Python to your daily work, you will notice that with a combination of theoretical learning and practical applicability of the same at work, one will be able to accomplish almost anything they desire to, through its use. With the right intent, applicability, and ambition one can even perhaps design a game or perform a complex task, without prior knowledge of the language.

The learning curve for Python also depends on certain obvious factors like your prior knowledge, exposure to the concepts of programming, etc…

If you are a beginner, devoting a couple of hours on understanding the language, then say in a month, you will be able to get a good feel of the language, mostly so if Python is your first language. If you have previous knowledge of programming, Javascript, C++, or if you understand the concepts of variables, control loops, etc…, then your hold on the language is even faster.

Either way, when learning is combined with practical real-life applicability, within a few days or a month you will be able to write programs, mostly expected out of a new learner. If the same method of learning is adapted for a month or two, along with exposure in programming, one will gain knowledge of the built-in functions and general features of the language. This will help and build confidence in the new learner to enhance their capabilities in programming.

Once the basics are in place, a new learner can then delve further to leverage the power of Python’s libraries and modules which are available as an open-source.

To conclude, it is a fact that Python is designed to be used in complex programming, yet at the same time, it is easy to learn and is truly a lightweight language. And once the basics are in place you can take up tutorials and advanced courses, to enhance your understanding.