Coding interview preparation Published on A quick guide to Coding interview preparation in Python 1. Coding Interview Preparation Kaiyu Zheng…www.slideshare.net 💭 Try it out yourself 💭 Click on the “CLAP” icon to exhibit how useful this post was to you 💭 Make sure to “FOLLOW” AND “COMMENT” to keep learning more programming or data science-related concepts with me.

How does it work? 1. Train model on a batch of labelled data 2. Use the trained model to predict labels on a batch of unlabeled data 3. Use the predicted labels to calculate the loss on unlabeled data 4. Combine labelled loss with unlabeled loss and backpropagate(repeat). To know more, refer https://towardsdatascience.com/pseudo-labeling-to-deal-with-small-datasets-what-why-how-fd6f903213af #AI #ml #datascience #semisupervisedlearning #letthedataconfess #dataanalyst #DL #deeplearning #tutorials

#datascienceshots #machinelearnning #classification #regression 💭 Try it out yourself 💭 Click on the “CLAP” icon to exhibit how useful this post was to you 💭 Make sure to “FOLLOW” AND “COMMENT” to keep learning more programming or data science-related concepts with me.

Derived from the confusion matrix, the following metrics are commonly used to assess the performance of models! 💭 Try it out yourself 💭 Click on the “CLAP” icon to exhibit how useful this post was to you 💭 Make sure to “FOLLOW” AND “COMMENT” to keep learning more programming or data science-related…

Source: DATAcated #datavisualisation #dataanalytics #datascience #businessintelligence 💭 Try it out yourself 💭 Click on the “CLAP” icon to exhibit how useful this post was to you 💭 Make sure to “FOLLOW” AND “COMMENT” to keep learning more programming or data science-related concepts with me.

Suppose you have a set of .csv files that you need to combine into one file, while keeping the header information. Luckily, using the pandas package in Python, you can do this relatively easily. In this example, you have two .csv files. homes.csv and homes1.csv homes.csv

Just three lines of code to get the IP address of any website!

🗺 Kepler is an amazing data visualization tool for geospatial data. ⚡ It is a web application that allows you to explore geospatial data. But besides being a web application, Kepler also provides a Jupyter notebook friendly library that you can use to custom make your own geospatial models based on your geolocation data. ✅ Kepler website — https://kepler.gl/ ✅ GitHub - https://github.com/keplergl/kepler.gl

It presents the first effort that automatically generates visualizations by applying deep neural translation to a set of visualization examples. The problem formulation and the Data2Vis model in the paper hold great potential to facilitate the future development of learning-based approaches for visualization generation. By the time of award nominations, this article already had 18 citations (now 31), more than any other paper nominated for the award, attesting to its potential large impact in the field.