How to prepare for Google Data Engineer Certification

Google’s data engineering certification is one of the new and emerging certification in the industry right now for Data Engineering and Machine Learning. The exam will test on your ability to design, build and maintain data processing system and also analyze data, to gain insight into business outcomes and enable machine learning. I was able to pass the data engineer exam on Jan 2018. Through this blog I would like to share my experience and the preparation guide, because at the time of writing I did not have enough materials to guide me on the exam.

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Building a table top hologram projector

Recently I was interested in building a hologram projector to view the restaurant menu in 3D. This quest began when I ordered some fancy named food in the restaurant and ended up having rice and some boiled vegetables for dinner. 😀 . So basically that got me thinking is there a more cool way to display the food in 3D and make the experience more appealing.

The below image is one of the commercial grade hologram projectors in the markets priced from (2000$- 8000$). In technical terms in not a true hologram projector but an illusion of reflection.

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In this article I’ll talk about how to make something similar and the challenges on the way.

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Building a simple Neural Network with Keras and Tensorflow

Neural networks have been a hot topic after the surfacing of deep learning. In these series of tutorials we will go in depth into building complex networks with Keras and Tensor Flow.

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TensorFlow is an open-source library for machine learning introduced by Google. Keras provides a high level api/wrapper around TensorFlow.

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Linear Regression with pure Tensorflow

In this tutorial we will implement a simple linear regression with tensorflow. So what is linear regression ? In a nutshell it’s an efficient way to calculate the correlation between two variables. These two variables are identified as independent and dependent variables. We all use regression on an intuitive level every day. For this tutorial we can use real estate data(house area and price). Although real estate prices depends on all sort of variables for this example we assume that house prices increases with house area.

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Optical Chracter Recognition on Super market bills

This was a problem I was working for a while. It is to do a proof of concept and see, with how much accuracy we can recognize a supermarket receipt. The scope is to try with 2 major supermarkets. One is Tesco and the other one is Sainsbury which are two of the leading super markets in UK. The data set had over 2000 images and was a very challenging data set. Almost 60% for the receipts had below 20-30% accuracy (bad lighting conditions/angles/low resolution/crumpled), when the receipts are processed through the Tesseract OCR.

  1. Okay, Now let’s start with a reasonable receipt. This receipt is readable for the human eye. Let see how the Tesseract reads this. (Hmm Not bad at-least we can get the super market name, less than 5% accuracy)


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