Category Archives: Uncategorized

Computer Vision, or CV for short, is the art of teaching a computer to analyze digital images such as photographs and videos to find differences or similarities to extract a conclusion. Acquiring images can be a heavy load on its own. Images can be created from sound waves, image files can be altered to provideContinue reading “Get started in CV”

Expand hashtags with upper() and lower() letters for Natural Language Processing The infamous hashtag can present issues when processing text data for NLP. The annoyances begin with “those” people who overuse hashtags on a regular basis. The bothersome nature continues when millennials use them in casual conversation, or when the entire purpose is muted, suchContinue reading “#ExpandHashtags for NLP”

Out of the plethora of libraries and packages available to use with Python to process data for Natural Language Processing, there is only one that assists with contractions, and it is insufficient. I won’t call it out by name, but it was useless in my endeavors. Due to this, and the wide array of contractionsContinue reading “Contractions in NLP”

Natural language processing, also known as NLP, combines computer science and linguistics to understand and process the relationships contained within communication languages. Words, characters, documents, sentences, and punctuation can play a factor in how humans understand language, and using this information, computers are capable of also learning and understanding how humans communicate by analyzing theseContinue reading “NLP Preprocess Tweets”

The mel cepstral frequency coefficients, also known as MFCCs, are set of features derived from a digital signal, consisting of 12-20 digits per sample, used to describe the overall shape of a spectral envelope. One can obtain more or fewer MFCCs depending on their application. The MFCCs are taken per sample, so if the sampleContinue reading “Easy MFCCs”

When it comes to processing audio files for classification in Python, having a .WAV format is critical to creating visualizations and extracting features from the data/audio file. Common audio file formats include .FLAC, .MP3, and several other codecs. To convert files from their original format to .wav formatted files, the Pydub library provides a convenientContinue reading “Audio File Conversion”

An audio file, or any sound really, has the following properties: Frequency, Wavelength, Amplitude, Speed, Direction These are characteristics that can be used to distinguish sounds from each other in neural networks, not unlike in the human brain. Visualizing audio files is an important task in data science, due to the means of processing classificationContinue reading “Visualizing Sound in Python”

Creating and utilizing web scraping and connection simulation bot for L-i-n-k-e-d-I-n Building on the previous installments on web scraping, this is a culmination of the previous work on this script. First, the automated login function is as follows: The PATH variable depends on the browser used. In this case, I used Chrome, therefore the codeContinue reading “Web Scraping #7”

Using automation and web scraping on LinkedIn to build your network. To begin building a bot to interact with LinkedIn, enabling a person to be far more active and contact more people without over analyzing every word, a bot can be super helpful to start some interactions, as not everyone will respond on the site,Continue reading “Web Scraping #6”

Importing data into machine learning projects differs depending on how the data is delivered. For data that is split beforehand, into train, test, and validation folders, there are often subfolders, sometimes within subfolders containing data, whether it be image, audio, or video files, labels are necessary for supervised learning models. If the data is organizedContinue reading “Label by Subfolder”