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Tag: Python tutorial

 Learn The Basics Of Python In Six Minutes

Posted on by Imarticus Learning

Learn The Basics Of Python In Six Minutes

Learning to program or begin with a new language is not easy: We’re sure your mind is racing with all of the new concepts you need to absorb, and the onslaught of knowledge might be daunting at times.

Python is currently one of the most popular—and well-paid—programming languages globally.

This guide will teach you the fundamentals of Python. This Python training is ideal if you are new to Python programming. You will be familiar with Python programming after finishing this guide.

Why learn Python? 

It’s a programming language that’s been around for almost 30 years, and millions of individuals worldwide use it. Python is simple to learn, making it an excellent choice for beginning programmers. It also increases your productivity when writing code, so this guide will assist you in wanting to get started with coding or enhancing your current skill set!

Python installation is relatively simple, and many Linux and UNIX systems now contain a modern Python. Some Windows machines (particularly those from HP) now come pre-installed with Python. 

Here we start with the basics of Python Programming:

Syntax

There are no required statement termination characters in Python. To begin a block, indent it, and to end it, dedent it. Statements that need an indentation level get followed by a colon (:). Comments are single-line strings that begin with the pound (#) sign; multi-line strings are for multi-line comments. The equals sign (“=”) to assign values (in actuality, objects with names), whereas the equality sign (“==”) test equality. Using the += and -= operators, you may increase or decrease numbers by the right-hand amount. This works with a variety of data kinds, including strings. 

Data types: 

Python data structures include lists, tuples, and dictionaries. The sets library contains sets (but are built-in in Python 2.5 and later). Lists are one-dimensional arrays, and dictionaries are associative arrays (commonly known as hash tables). Tuples are immutable one-dimensional arrays (Python “arrays” can be of any type so that you can combine integers, texts, and so on). 

Strings

Its strings can contain either single or double quotation marks, and you can put one type of quotation mark inside another (for example, “He said ‘hello.'” is legitimate). Strings with many lines are in _triple, and double-quotes.

Flow control statements

If, for, and while are flow control statements. Instead of using if, there is no switch. Use to iterate over the members of a list. Use range(number>) to get a list of numbers you can iterate through.

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

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

Tips and tricks in AI/ML with python to avoid data leakage

Posted on by Imarticus Learning

Data science has emerged as an essential field of work and study in recent times. Thus, a machine learning course can help interested candidates learn more and land lucrative jobs. However, it is also essential to protect data to ensure proper automation.

Now, beginner courses in machine learning and artificial intelligence only teach students to split data or feed the relevant training data to the classifier. But Imarticus Learning’s AI/ML program helps gain the necessary in-depth knowledge. 

Best Ways to Avoid Data Leakage when Using AI/ML with Python

A Python certification from a reputable institute can help one gain proper insight and learn the tricks of using AI or ML with Python. This will enable interested candidates to know about real-world data processing and help them prevent data leakage.

Following are some tips that advanced courses like an artificial intelligence course by E&ICT Academy, IIT Guwahati will teach students. 

  • No Data Preprocessing Before Train-Test Split

There will be a preprocessing method fitted on the complete dataset at times. But one should not use it before the train-test split. If this method transforms the train or test data, it can cause some problems. This will happen because the information obtained from the train set will move on to the test set after data preprocessing. 

  • Use Transform on Train and Test Sets

It is essential to understand where one can use Transform and where one needs to use fit_transform. While one can use Transform on both the train set and the test set, fit_transform cannot be used for a test set. Therefore, it is wise to choose to Transform for a test set and fit_transform for a train set. 

  • Use Pickle and Joblib Methods

The Python Pickle module serializes and deserializes an object structure. However, the Pickle module may not work if the structure is extensive with several numpy arrays. This is when one needs to use the Joblib method. The Joblib tools help to implement lightweight pipelining and transparent disk-caching. 

Following are a few more tricks that help in automation and accurate data analytics when using AI/ML with Python.

  • Utilize MAE score when working on any categorical data. It will help determine the algorithms’ efficiency as the most efficient one will have the lowest case score. 
  • Utilize available heat maps to understand which features can lead to leakage. 
  • When using a Support Vector Machine (SVM), it is crucial to scale the data and ensure that the kernel cache size is adequate. One can regularise and use shrinking parameters to avoid extended training times. 
  • With K-Means and K-Nearest Neighbour algorithms, one should use a good search engine and base all data points on similarities. The K-value should be chosen through the Elbow method, and it should be relevant. 

Learn AI/ML with Python 

A Python certification will be beneficial for those who wish to pursue a career in data science and analytics. However, it is best to choose a course that will offer advanced training. Imarticus Learning’s Certification in Artificial Intelligence & Machine Learning includes various recent and relevant topics. Apart from using AI/ML with Python, students will also get to work on business projects and use AI Deep Learning methods.

The course curriculum is industry-oriented and developed by IIT Guwahati and the E&ICT Academy. Students can interact with industry leaders, build their skills in AI and Ml through this machine learning course. This course is ideal for understanding the real-world challenges in data science and how AI/ML with Python can help provide solutions. 

The IIT artificial intelligence course from Imarticus Learning helps students become data scientists who excel in their fields of interest. The course offers holistic education in data science through live lectures and real business projects. It is therefore crucial for a rewarding job in the industry. 

How To Use Logistic Regression in Python?

Posted on by Imarticus Learning

Logistic regression is amongst the most popular machine learning (ML) algorithms. If you are learning about machine learning and its implementation, then you must be well-acquainted with this algorithm as it forms the basis for many advanced algorithms.

Python tutorialIn the following Python tutorial, we will discuss what logistic regression is, and how you can use this machine learning algorithm through Python while using Python for data science.

What is logistic regression?

Logistic regression refers to a machine learning algorithm used for classifying data points. It is a supervised learning algorithm, which means that it maps inputs to output according to the example pairs of the input and output.

Even though logistic regression is a simple algorithm, it has many applications in various sectors such as detecting spam, identifying cancer, and predicting diabetes.

The reason why it is called logistic regression is that it operates quite similarly to a linear regression algorithm. Notably, linear regression is another simple and highly popular machine learning algorithm.

How to use logistic regression in Python?

To perform logistic regression in Python, you will need to follow several steps. The first prerequisite is to be familiar with the algorithm and programming in Python. You should know the fundamental theorem behind logistic regression to use it effectively.

The steps for using logistic regression in Python are:

  • Installing the required Python packages (Matplotlib, NumPy, scikit-learn, and StatsModels)
  • Getting the data to train and test the model
  • Preparing the data, including cleaning it and fixing missing values
  • Transforming the data into the required form
  • Making the classification model
  • Training your model with the available data
  • Testing the model to check its accuracy
  • Optimizing the model until it has reached the required accuracy

You only need to follow such a small list of steps while using Python for this algorithm.

How to pursue a career in data science?

Data science is a broad field and the machine learning algorithm we discussed above is only a small fraction of it. If you are interested in pursuing a career in data science, then we recommend taking a data science course in India.

Investment Banking CoursesTaking such a course will help you learn the various concepts present in this subject including several machine learning algorithms and the use of artificial intelligence (AI). You can get a data science certification India-based that teaches you the latest in-demand skills for this field quickly and efficiently.

A well-reputed data science certification India-based would teach you big data, data visualization, SQL, statistics, R, Apache Spark, and many relevant skills necessary to become an expert. Moreover, having a certification will make it easier for you to stand out from your peers and become a preferred choice among the recruiters.

Learning about machine learning algorithms can be very interesting. If you are keen on learning about Python for data science through a Python tutorial, then it would be best to complete a data science course in India. And, to learn logistic regression effectively, you should practice it in different use cases. You can check out our data analytics course here.