Python tutorials

September 25, 2020 Python

Python for NLP: Developing an Automatic Text Filler using N-Grams

This is the 15th article in my series of articles on Python for NLP. In my previous article, I explained how to implement TF-IDF approach from scratch in Python. Before that we studied, how to implement bag of words approach from scratch in Python. Today, we will study the N-Grams approach and will see how the N-Grams approach can be used to create a simple automatic text filler or suggestion engine. Automatic text filler is a very useful application and […]

September 25, 2020 Python

Gradient Boosting Classifiers in Python with Scikit-Learn

Introduction Gradient boosting classifiers are a group of machine learning algorithms that combine many weak learning models together to create a strong predictive model. Decision trees are usually used when doing gradient boosting. Gradient boosting models are becoming popular because of their effectiveness at classifying complex datasets, and have recently been used to win many Kaggle data science competitions. The Python machine learning library, Scikit-Learn, supports different implementations of gradient boosting classifiers, including XGBoost. In this article we’ll go over […]

September 25, 2020 Python

Python List Sorting with sorted() and sort()

In this article, we’ll examine multiple ways to sort lists in Python. Python ships with two built-in methods for sorting lists and other iterable objects. The method chosen for a particular use-case often depends on whether we want to sort a list in-place or return a new version of the sorted list. Assuming we want to sort a list in place, we can use the list.sort() method as follows: >>> pets = [‘Turtle’, ‘Cat’, ‘Fish’, ‘Dingo’] >>> pets.sort() >>> pets […]

September 25, 2020 Python

Python for NLP: Word Embeddings for Deep Learning in Keras

This is the 16th article in my series of articles on Python for NLP. In my previous article I explained how N-Grams technique can be used to develop a simple automatic text filler in Python. N-Gram model is basically a way to convert text data into numeric form so that it can be used by statisitcal algorithms. Before N-Grams, I explained the bag of words and TF-IDF approaches, which can also be used to generate numeric feature vectors from text […]

September 25, 2020 Python

Creating Python GUI Applications with wxPython

Introduction In this tutorial, we’re going to learn how to use wxPython library for developing Graphical User Interfaces (GUI) for desktop applications in Python. GUI is the part of your application which allows the user to interact with your application without having to type in commands, they can do pretty much everything with a click of the mouse. Some of the popular Python alternatives for developing a GUI include Tkinter, and pyqt. However, in this tutorial, we will learn about […]

September 25, 2020 Python

Serverless Python Application Development with AWS Chalice

Introduction In software development, we are constantly building solutions for end-users that solve a particular problem or ease/automate a certain process. Therefore, designing and building the software is not the only part of the process, as we have to make the software available to the intended users. For web-based applications, deployment is a very important aspect and part of the process since the application not only needs to work, but also needs to work for many users concurrently and be […]

September 25, 2020 Python

Python for NLP: Movie Sentiment Analysis using Deep Learning in Keras

This is the 17th article in my series of articles on Python for NLP. In the last article, we started our discussion about deep learning for natural language processing. The previous article was focused primarily towards word embeddings, where we saw how the word embeddings can be used to convert text to a corresponding dense vector, which can be subsequently used as input to any deep learning model. We perform basic classification task using word embeddings. We used custom dataset […]

September 25, 2020 Python

Image Classification with Transfer Learning and PyTorch

Introduction Transfer learning is a powerful technique for training deep neural networks that allows one to take knowledge learned about one deep learning problem and apply it to a different, yet similar learning problem. Using transfer learning can dramatically speed up the rate of deployment for an app you are designing, making both the training and implementation of your deep neural network simpler and easier. In this article we’ll go over the theory behind transfer learning and see how to […]

September 25, 2020 Python

Rounding Numbers in Python

Using a computer in order to do rather complex Math is one of the reasons this machine was originally developed. As long as integer numbers and additions, subtractions, and multiplications are exclusively involved in the calculations, everything is fine. As soon as floating point numbers or fractions, as well as divisions, come into play it enormously complicates the whole matter. As a regular user, we are not fully aware of these issues that happen behind the scenes and may end […]

September 25, 2020 Python

Python Docstrings

As already pointed out in a previous article titled Commenting Python Code you have learned that documentation is an essential, and a continuous step in the process of software development. The article mentioned above briefly introduced the concept of docstrings which is a way to create documentation for your Python code from within the code. This in-code documentation works for modules, classes, methods, and functions, and it is the preferred way to document all Python code. There is a lot […]

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