has made the Practical Deep Learning for Coders is a course from available .

This course was created to make deep learning accessible to as many people as possible. The only prerequisite for this course is that you know how to code (a year of experience is enough), preferably in Python, and that you have at least followed a high school math course.

This course was developed by Jeremy Howard and Sylvain Gugger. Jeremy has been using and teaching machine learning for around 30 years. He is the former president of Kaggle, the world’s largest machine learning community. Sylvain Gugger is a researcher who has written 10 math textbooks.

According to the findings of Kaggle’s State of Data Science and Machine Learning report,  Python, SQL, and R continue to be the top programming languages for data science professionals,

The annual survey is noteworthy due to the large number of participants – it received responses from almost 20,000 data professionals from 171 countries and territories this time.

An analysis published on Business Broadway looked at the raw data from the Kaggle survey, and concluded that data professionals used an average of three languages in 2019. The top programming language was Python (87%), followed by SQL (44%) and R (31%). The other languages in the top 10 list include Java, C and C++, JavaScript, Bash, MATLAB, and TypeScript.

There are an abundance of ML tools available today.

For beginners, this can be overwhelming.

This article from Analytics India Magazine asks the top Kagglers for their favorite toolsets.

In the next section, we look at the top tools, frameworks, cloud services, libraries used by the Kaggle masters and Grand Masters, which they revealed to us in our exclusive interviews. That said, we have to admit that all these top Kagglers are of the opinion that one should not fall in love with tools, and it is all right as long any tools get the job done right!

The Career Force goes through her top 5 free dataset resources in this video.

  1. is a large dataset aggregator and the home of the US Government’s open data.
  2. FiveThirtyEight: This is a great resource to not only see datasets, but also see how a well-respected analytics organization provides meaningful insights and commentary on the data.
  3. Kaggle:  is a great resource not only for free datasets, but for data science topics in general.
  4. Data.World: There are hundreds of thousands of free datasets for anyone that sets up an account on
  5. Google Dataset Search: By accessing thousands of different repositories across the web, Google Dataset Search provides access to almost 25 million different publicly available datasets.

Here’s an interesting tutorial for Keras and TensorFlow that predicts employee retention.

In this tutorial, you’ll build a deep learning model that will predict the probability of an employee leaving a company. Retaining the best employees is an important factor for most organizations. To build your model, you’ll use this dataset available at Kaggle, which has features that measure employee satisfaction in a company. To create this model, you’ll use the Keras sequential layer to build the different layers for the model.