Python is one of the world’s most popular computer languages, with over 8 million developers, according to research from SlashData.

The creator of Python is Guido van Rossum, a computer scientist and academic.

Back in the late 1980s, he saw an opportunity to create a better language and also realized that the open source model would be ideal for bolstering innovation and adoption (by the way, the name for the language came from his favorite comedy, the Monty Python’s Flying Circus).

“Python is a high-level programming language, easy for beginners and advanced users to get started with,” said Jory Schwach, who is the CEO of “It’s forgiving in its usage, allowing coders to skip learning the nuances that are necessary in other, more structured languages like Java. Python was designed to be opinionated about how software should be created, so there’s often just a single appropriate way to write a piece of code, leaving developers with fewer design decisions to deliberate over.”

A way to get started with the language is to use a platform like Anaconda, which handles the configurations and installs various third-party modules. But there are cloud-based editors, such as REPL (I also have my own course on Python, which is focused on the fundamentals).

The use of AI is growing at an exponential rate. Businesses are using AI to leverage benefits such as lower costs, increased productivity, and reduced manual errors.

30% of all companies worldwide are using AI for at least one of their sales processes.

To that end, there are a lot of people looking to reskill. Where the rubber meets the road is what language to learn?

In a match up that no one saw coming in the AI world, folks are starting to ponder Python or JavaScript.

So, it’s only natural if you’re a developer interested in getting into the field. But it’s also natural to ask yourself which language you should choose for programming AI algorithms. After a little digging, you’ll surely find that Python and JavaScript are two top contenders. They are both object-oriented languages that have a host of features with their strengths and weaknesses. So, which one should you choose? Let’s have a look at both.

Here’s an interesting session from the SciPy 2020 virtual conference.

As a foundational tutorial in statistics and Bayesian inference, the intended audience is Pythonistas who are interested in gaining a foundational knowledge of probability theory and the basics of parameter estimation. Knowledge of `numpy`, `matplotlib`, and Python are prerequisites for this tutorial, in addition to curiosity and an excitement to learn new things!

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.

deeplizard demonstrates how to create a confusion matrix, which will aid us in being able to visually observe how well a neural network is predicting during inference.


  • 00:00 Welcome to DEEPLIZARD – Go to for learning resources
  • 00:34 Plotting a Confusion Matrix
  • 02:48 Reading a Confusion Matrix
  • 04:56 Collective Intelligence and the DEEPLIZARD HIVEMIND