I know that some of you reading these lines already hate me. Some of you could and actually spend every working hour in front of a terminal window; you love it. There is nothing wrong with it, and some Command Line Interface (CLI) tools are so well designed that even if you had a graphical user interface, you would most probably never use it. I hear you! I actually love working inside a terminal w

IntroductionMachine learning models are exciting and powerful, but they aren’t very useful by themselves. Once a model is complete, it likely has to be deployed before it can deliver any sort of value. As well, being able to deploy a preliminary model or a prototype to get feedback from other stakeholders is extremely useful. Recently, there has been an emergence of several tools that Data Scienti

Distance Measures. Image by the author.Many algorithms, whether supervised or unsupervised, make use of distance measures. These measures, such as euclidean distance or cosine similarity, can often be found in algorithms such as k-NN, UMAP, HDBSCAN, etc.

Jupyter Notebooks in Microsoft Excel. Image by the author.It used to be an “either/or” choice between Excel and Python Jupyter Notebooks. With the introduction of the PyXLL-Jupyter package now you can use both together, side by side. In this article I’ll show you how to set up Jupyter Notebooks running inside Excel. Share data between the two and even call Python functions written in your Jupyter

FastAPI Logo — https://fastapi.tiangolo.comWhat do you like best about being a data scientist? It’s definitely modeling and fine-tuning for optimal results. But what does it mean to be a good model if it’s never used or never deployed? To produce a machine learning model, the typical approach is to wrap it in a REST API and use it as a…

120+ Data Scientist Interview Questions and Answers You Should Know in 2021 Interview Questions from Facebook, Yelp, Amazon, Google, Apple, Netflix, and More

Image by Mudassar Iqbal from Pixabay, Edited using PixlrExploratory data analysis (EDA) is an approach to analyze the data and find patterns, visual insights, etc. that the data set is having, before proceeding to model. One spends a lot of time doing EDA to get a better understanding of data, that can be minimized by using auto visualizations tools such as Pandas-profiling, Sweetviz, Autoviz, or

Introductionaltair is an interactive visualization library. It offers a more consistent API. This is how the authors describe the library. Declarative statistical visualization library for Python What does it mean is that it focuses on what to plot instead of how to plot, and you can easily combine different components like ggplot. Before I jump into the library, I want to quickly introduce two gr

Building search systems is hard. Preparing them to work with machine learning is really hard. Developing a complete search engine framework integrated with AI is really really hard. So let’s make one. ✌️ In this post, we’ll build a search engine from scratch and discuss on how to further optimize results by adding a machine learning layer using Kubeflow and Katib. This new layer will be capable of

Python is slow. I bet you might encounter this counterargument many times about using Python, especially from people who come from C or C++ or Java world. This is true in many cases, for instance, looping over or sorting Python arrays, lists, or dictionaries can be sometimes slow. After all, Python is developed to make programming fun and easy. Thus, the improvements of Python code in succinctness

Storytelling is an essential skill for us data scientists. To convey our ideas and be persuasive, we need effective communication. And aesthetic visualisations are a great tool for that. In this post, we’ll cover 5 visualisation techniques beyond the classics that can make your data story more aesthetic and effective. We’ll be using Plotly graphic library in python (also available in R), which pro

IntroductionPandas is an amazing library in the Python ecosystem for data analytics and machine learning. They form the perfect bridge between the data world, where Excel/CSV files and SQL tables live, and the modeling world where Scikit-learn or TensorFlow perform their magic. A data science flow is most often a sequence of steps — datasets must be cleaned, scaled, and validated before they can b

Learn the basics of AI and Deep Learning with TensorFlow and Keras in this Live Training Session hosted by Me. “Wisdom is not a product of schooling but of the lifelong attempt to acquire it.” — Albert Einstein IntroductionThe ability to understand information produced by the individuals at the cutting edge of research within Artificial Intelligence and the Machine learning domain is a skill that

Photo by Chris Ried on Unsplash1. F-StringsF-Strings provide a concise and convenient way to embed Python expressions inside string literals for formatting. First, let’s define two variables name and age that you want to include in our print statement. name = "Pavel" age = 23To not deal with string concatenation or using commas inside the print statement, you can use Python’s improved String forma

I used to have an Apple laptop as my daily driver. I could do almost everything there; development, proposal writing, music composition etc. But the fear of vendor lock-in, the concern that I am depended on Apple’s whims and vices — which are arguably very expensive — led me to seek a new solution. See Part II here:

Streamlit Is The Game Changing Python Library That We’ve Been Waiting For Developing a user-interface is not easy. I’ve always been a mathematician and for me, coding was a functional tool to solve an equation and to create a model, rather than providing the user with an experience. I’m not artsy and nor am I actually that bothered by it. As a result of this, my projects always remained, well, pro

Although on the surface Python might appear to be a language of simplicity that anyone can learn, and it is, many might be surprised to know just how much mastery one can obtain in the language. Python is one of those things that is rather easy learn, but can be difficult to master. In Python, there are often multiple ways of doing things, but it can be easy to do the wrong thing, or reinvent the

SourceThis Post will provide you a detailed end to end guide for using Pytorch for Tabular Data using a realistic example. By the end of this post, you will be able to build your Pytorch Model. A few things before we start:Courses: I started with both fast.ai courses and DeepLearning.ai specialization (Coursera). They gave me the basic knowledge about DeepLearning. The great Stanford cs231n is als

Good news to all data scientists and researchers🎉🎉 Springer has announced to give hundreds of expensive books on Science and technology worth thousands of dollars available for free download during this COVID-19 lockdown. Over more than 500 textbooks are available! If you are interested only in some of these books, you may download them one by one. But this chance won’t happen often, what about