Pollution forecasting using Time series and LSTM with MXnet

Time series analysis is a statistical technique that deals with time series data, or trend analysis. Time series data means that data is in a series of particular time periods or intervals. TSA(Time series analysis) applications:
• Pattern recognition
• Earthquake prediction
• Weather forecast
• Financial statistics
• and many more…


Natural Language Processing with Tensorflow

Hey all! In this post I attempt to summarize the course on Natural Language Processing in TensorFlow by Deeplearning.ai.


Do You Need Synthetic Data For Your AI Project?

Data is an issue in most AI projects. I have failed several projects due to the lack of good data… Since then, I relied way more on a relatively new approach called synthetic data. I hope that this article will help you better understand how synthetic data can help you with your AI projects. For large tech firms like Google, Apple, and Amazon, gathering data is less of an issue compared to other companies. Indeed, they have an almost limitless supply of diverse data streams through their products/services, creating the perfect ecosystem for data scientists to train their algorithms. For smaller companies, access to these datasets is limited, expensive, or non-existent.


Getting started in AI and computer vision with Nvidia Jetson Nano. Hands-on approach to the JetBot project


Updated Text Preprocessing techniques for Sentiment Analysis

Let’s discuss the shortcoming’s of some techniques and how to improve them. People follow numerous text preprocessing techniques but how many of these are actually useful? I’ve been working with text data for almost 6 months and I feel there are many challenges when you working on a product that will be used by many people.


Time Series Prediction – A short introduction for pragmatists

Are you trying to predict time series but don’t know where to start? This blog post will provide a comparison of the most prominent techniques and show you how to implement them.


Building and deploying a machine learning model with automated ML on Azure.

This article goes through the basics of using Azure’s Automated Machine learning functionality. We will train and deploy a machine learning model that is operational. After this article you will be able to build an end to end solution for deploying a machine learning model, without writing a single line of code. The goal is to help you understand what goes into the basics of building and deploying machine learning models. It won’t cover concepts such as modelling techniques/ best practices, security/authorisation, but functions as a starting point for those who: (1) want to build ML models without coding or (2) want to understand what is possible with automated machine learning and how this can be of value to you and your organisation. We will use Microsoft Azure’s Automated Machine Learning platform for this exercise. We set up the environment/resources, train multiple models on the dataset and deploy this model on Azure so we can actually use it.


Building a Machine Learning model in 3 lines of code? Yes you can.

Machine Learning as a subject is not easy. It is indeed a set of tools (mainly algorithms and optimization procedures) whose comprehension involves, inevitably, a deep understanding of Maths and Stats. Nevertheless, the implementation of an ML model to a real scenario might be easier than expected. Indeed, once you got familiar with theoretical concepts, you will be able to use pre-built packages and utilities available in Python. In other words, to build a basic model, you don’t have to be a ninja in Python: the most important thing is understanding the underlying problem and develop a theory to solve it. Then Python will do the hard job for you. In this article, I’m going to show you how to build an ML pipeline step by step, and then I’ll show you how to ‘envelope’ all the steps in 3 lines of codes. For this purpose, I’m going to use the Red Wine Quality dataset, available on Kaggle.


Evolutionary Machine Learning: The Next Deep Learning?

So much of what Engineers design and build today takes inspiration from nature. Boeing added flaps on the wings of their planes mimicking Eagles, the shape of Whale fins helped reduce drag on wind turbines and the noses of bullet trains look suspiciously like a Kingfisher’s beak. Nature has often already found elegant solutions to problems that our best and brightest work on every day. Nature finds these solutions through a process of natural selection where the genes of the best performing organisms are passed on to successive generations. It’s a brutal but extremely efficient method of finding incredible solutions to the problems life faces.


The Geometric and Harmonic Means

In this article I looked at some basic measures of central tendency. But I didn’t cover them all. Two that I didn’t cover are the geometric and harmonic means.