Skip to main content

Python Basics - know about libraries

Python's popularity for data science is largely due to the strength of its core libraries, high productivity for prototyping and building small and reusable systems, and its strength as a general purpose programming language.

python library

For importing any Python library take the reference from the below code snippet in your notebook-


Some core libraries that we will use in data science are as below-

  1. pandas
  2. NumPy
  3. Scipy
  4. matplotlib

Comments

Post a Comment

Popular posts from this blog

How to deploy your ML model as Fast API?

Another post starts with you beautiful people! Thank you all for showing so much interests in my last posts about object detection and recognition using YOLOv4. I was very happy to see many aspiring data scientists have learnt from my past three posts about using YOLOv4. Today I am going to share you all a new skill to learn. Most of you have seen my post about  deploying and consuming ML models as Flask API   where we have learnt to deploy and consume a keras model with Flask API  . In this post you are going to learn a new framework-  FastAPI to deploy your model as Rest API. After completing this post you will have a new industry standard skill. What is FastAPI? FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints. It is easy to learn, fast to code and ready for production . Yes, you heard it right! Flask is not meant to be used in production but with FastAPI you can use your model in production wi

How to install and compile YOLO v4 with GPU enable settings in Windows 10?

Another post starts with you beautiful people! Last year I had shared a post about  installing and compiling Darknet YOLOv3   in your Windows machine and also how to detect an object using  YOLOv3 with Keras . This year on April' 2020 the fourth generation of YOLO has arrived and since then I was curious to use this as soon as possible. Due to my project (built on YOLOv3 :)) work I could not find a chance to check this latest release. Today I got some relief and successfully able to install and compile YOLOv4 in my machine. In this post I am going to share a single shot way to do the same in your Windows 10 machine. If your machine does not have GPU then you can follow my  previous post  by just replacing YOLOv3 related files with YOLOv4 files. For GPU having Windows machine, follow my steps to avoid any issue while building the Darknet repository. My machine has following configurations: Windows 10 64 bit Intel Core i7 16 GB RAM NVIDIA GeForce GTX 1660 Ti Version 445.87

How to use opencv-python with Darknet's YOLOv4?

Another post starts with you beautiful people 😊 Thank you all for messaging me your doubts about Darknet's YOLOv4. I am very happy to see in a very short amount of time my lovely aspiring data scientists have learned a state of the art object detection and recognition technique. If you are new to my blog and to computer vision then please check my following blog posts one by one- Setup Darknet's YOLOv4 Train custom dataset with YOLOv4 Create production-ready API of YOLOv4 model Create a web app for your YOLOv4 model Since now we have learned to use YOLOv4 built on Darknet's framework. In this post, I am going to share with you how can you use your trained YOLOv4 model with another awesome computer vision and machine learning software library-  OpenCV  and of course with Python 🐍. Yes, the Python wrapper of OpenCV library has just released it's latest version with support of YOLOv4 which you can install in your system using below command- pip install opencv-python --up