Yuli Vasiliev is a programmer, freelance author, and consultant with more than two decades of experience. He began as a developer of database-driven applications, using Oracle database technology. The need for data analysis led him eventually to the field of ML and AI. His present professional interests are in the area of natural language processing (NLP). He runs the @stocknewstip_bot in Telegram, which is available at https://t.me/stocknewstip_bot.

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Explainable AI or shortly XAI is a domain that deals with maintaining transparency to the decision making capability of complex machine learning models and algorithms. In this article, we will take a look at such a tool that is built for the purpose of making AI explainable. A simple way to understand this concept is to compare the decision-making process of humans with that of the machines. How do we humans come to a decision? We often make decisions whether they are small insignificant decisions like what outfit to wear for an event, to highly complex decisions that involve risks such as investments or loan approvals.

Python leads as a top machine learning solution – thanks largely to its extensive battery of powerful open source machine learning libraries. Python provides a great way for machine learning newcomers to begin their hands-on practice, or for experienced practitioners to augment their growing battery of tools. Python's popularity has recently grown even further since it is the most common way to access leading deep learning solutions such as TensorFlow. Note that this workshop day does not cover deep learning, since it serves first-time users by covering a broader, foundational range of traditional machine learning methods. However, this training does provide helpful groundwork for the "Hands-On Deep Learning in the Cloud" workshop scheduled for later in the same week.

What could potentially be the use of doing that? These are just some of the questions you must have had when you read the title of this article. And it's only fair – I had the exact same thoughts when I first came across this concept! The time series data most of us are exposed to deals primarily with generating forecasts. Whether that's predicting the demand or sales of a product, the count of passengers in an airline or the closing price of a particular stock, we are used to leveraging tried and tested time series techniques for forecasting requirements.

In the last decade, Artificial Intelligence (AI) has stepped firmly into the public spotlight, in large part owing to advances in Machine Learning (ML) and Artificial Neural Networks (ANNs). But with promising new technologies comes a whole lot of buzz, and there is now an overwhelming amount of noise in the field. That's why I thought it would be useful to get back to basics and actually implement a single neuron from scratch using Python. Before we dive in, I just wanted to quickly talk about what a neuron is in the first place. Early proponents of artificial intelligence noticed that the biological neuron was capable of conceptualizing and learning from large volumes of data, and postulated that modelling this neuron in a machine might allow for a similar capability.¹