Skip to main content

Getting started by installing Anaconda


Our first step to start with basics is to install the Anaconda ( What is anaconda? ) in your machine.
In this blog I am using the 4.2.0 version of Anaconda but of course you can use the latest version and you will find the download link here downloading preferred anaconda

Installation of Anaconda is quite easy.
After downloading the zip file of the installation file extract the zip file in your system.
Now double click on the unzip file and follow the instruction.

After the successful installation go to the start icon on windows and click on the Anaconda Navigator icon.
Based on your system configuration it will take some time and following screen will be open-



Now in the navigator click on the Jupytor notebook to start with Python.


Please comment if you are facing any issue while installation.

Comments

Post a Comment

Popular posts from this blog

How to convert your YOLOv4 weights to TensorFlow 2.2.0?

Another post starts with you beautiful people! Thank you all for your overwhelming response in my last two posts about the YOLOv4. It is quite clear that my beloved aspiring data scientists are very much curious to learn state of the art computer vision technique but they were not able to achieve that due to the lack of proper guidance. Now they have learnt exact steps to use a state of the art object detection and recognition technique from my last two posts. If you are new to my blog and want to use YOLOv4 in your project then please follow below two links- How to install and compile Darknet code with GPU? How to train your custom data with YOLOv4? In my  last post we have trained our custom dataset to identify eight types of Indian classical dance forms. After the model training we have got the YOLOv4 specific weights file as 'yolo-obj_final.weights'. This YOLOv4 specific weight file cannot be used directly to either with OpenCV or with TensorFlow currently becau...

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 you...

Detecting Credit Card Fraud As a Data Scientist

Another post starts with you beautiful people! Hope you have learnt something from my previous post about  machine learning classification real world problem Today we will continue our machine learning hands on journey and we will work on an interesting Credit Card Fraud Detection problem. The goal of this exercise is to anonymize credit card transactions labeled as fraudulent or genuine. For your own practice you can download the dataset from here-  Download the dataset! About the dataset:  The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. Let's start our analysis with loading the dataset first:- As per the  official documentation -  features V1, V2, ... V28 are the principal compo...