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Create interactive bubble charts with custom filters and menu to filter selected data and custom configurations.

Learn how to read and send an image as a base64 string to an API using Python with this step-by-step guide.

Introduction to using the Requests library in Python for making HTTP requests with examples

Use the Threadpool Executor to execute multiple tasks concurrently and improve application performance.

Create a heatmap and confusion matrix, including how to format the data, customize the color scheme, and add annotations. Get step-by-step instructions to help readers create their own visualizations in Python using Plotly.

Explore how to use Python and the pydicom library to read and process DICOM files, which are a standard format for medical images and related data. The post provides an overview of DICOM files and step-by-step instructions for using Python to extract and manipulate data from DICOM files.

Create a basic flask micro server using python and perform different operations like request data fetching and different responses including string, json and xml responses.

Render data in flask web application using Jinja2 and perform different operations to render different kind of data from python in HTML templates.

Read audio files and get audio metadata and also perform different audio operations like cropping, playback speed and other operations easily in python.

Create Interactive Pie and Donut charts using Plotly with plotly express and plotly figure. Also add different data and chart viewing options.

Automatic Speech Recognition using OpenAI's whisper model in python and create transcription from audio files locally and from websites like YouTube using python

Semantic Segmentation using pretrained transformer segformer models usign Pytorch in python and using models with different parameters and input shapes.

Add images to python docx and create multiple views using python-docx features to create Grid, table images and image alignment.

Use Opencv mouse events to draw different shapes like circle, rectangle, polylines and polygon on images using mouse and keyboard.

Read csv and xlsx files using pandas and create tables in word documents using python docx with different styles and document configurations.

Create tables in docx using python docx and use different features like cell styling, nested tables and other paragraph features for tables and also add images to table objects.

Perform Optical character recognition for 80+ languages using easy ocr easily in python and using different libraries.

Create Bullet and Numbered lists in documents using python docx and implement nested lists and styles options easily to lists in documents.

Work with python docx to crate word documents using python with different styles and options and implement headings, paragraphs and other document operations.

Create deep learning models application using flask as web service and deploy to google cloud run using docker and continuous deployment using Github.

Hand Landmark detection using mediapipe to get 21 landmarks for each hand, hand handedness and bbox coordinates with lower latency and high accuracy and process on images and videos.

Use Yolov5 architecture to train model with pytorch backend for different dataset and convert dataset from one format to other for training of yolov5 object detection models.

Tesseract is a open-source OCR engine owened by Google for performing OCR operations on different kind of images. Use pytesseract to process different type of images and localize text location with confidence.

Tensorflow callbacks are very important to customize behaviour of Keras Tensorflow models in training or evaluation. We can either use predefined callbacks from tensorflow or can write our own callbacks to do some process.

We have different triditional and Machine Learning based methods for increasing our training data by changing data shape, adding noise or creating nearly similar data using current dataset. NLPAug is an open source python package for data augmentation using different methods and pretrained Deep Learning models.

AzureML is a machine learning cloud-based service from Azure cloud used for development, training and deployment of Machine Learning models and solutions

Emotion Classification is a Natural Language Processing task in Machine Learning where we can process text data and classify into different classes and can detect sentiment of given text.

Use Azure Cognitive face service to detect faces in images and also match faces to recognize person in images. We can also use it to group faces and face recognition.

Connect a custom domain from any domain provider to Google cloud compute engine

Train a Deep learning image classification model with no or very less code and serve using an endpoint using azure cognitive services.

Image classification is a very basic example in computer vision yet used widely for a lot of tasks. In image classification, we can input and image to deep learning model and get related label for class which image belongs to. We can use image classification for classifying an object from different classes, checking quality of a manufactured objects(good or not),

Optimize tensorflow deep learning models using TensorRT by nvidia to speed up inference of models with good accuracy.

For any model, we train for a specific purpose, we need to deploy it on some cloud or local device to use it for new data, so here we will learn about cloud deployment as API

Face Detection and Recognition is a very used part in Deep Learning. We use face detection and recognition for different tasks like login on applications, person recognition and different attendence systems.