What helped me get a data science job that fits my ambitions? [part 2]

What helped me get a data science job that fits my ambitions? [part 2]

Around ambitions, challenges, learning
Many of the aspiring data scientists wonder how to take the path and get the job as a data scientist. Fortunately,  nowadays, you don't have to start a costly bootcamp or a university degree to become a data scientist because there are many resources available, and a lot of them are free and accessible to everyone. In the first part of this post, I shared the resources that helped me land my Data Scientist job. However, without a strategy, it's harder to achieve it. I personally joined a community that helped me know my purpose, shape my dream career and define my goals and action steps. It's Classy Career Girl. It was my main resource for time management tools and organizing myself. I also was inspired by some other recommended techniques like…
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What helped me get a data science job that fits my ambitions – part 1: resources

What helped me get a data science job that fits my ambitions – part 1: resources

Around ambitions, challenges, learning
Almost 2 years ago, I took the decision to quit my job as a software engineer and to start looking for a job in the machine learning field. Right away after quitting my job, I wrote an article in my blog Up to my new Tech challenges and from there the journey started. In this article, I'm happy to share how I landed the job I dreamt of. Yeah, I got it! I've been working as a Data Scientist for Remerge, in Berlin,  for one year. Choose the right courses First off, it's important to assimilate core concepts and techniques in machine learning / Data Science, so I started on some main courses online: Machine Learning Specialization, university of Washington, Coursera: This specialization, taught by two Amazon Professors, is a good…
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From OpenCV and Tesseract to exploring recent research results in Computer Vision [Challenge 2]

From OpenCV and Tesseract to exploring recent research results in Computer Vision [Challenge 2]

challenges, Computer Vision, Deep Learning, learning, Technology
I've been away for a while... Actually, I didn't notice as the time was going on.. even though I felt like I've been running all days since my last article :D .. I was all in a new dimension of machine learning..one that is so huge and with many many possible doors to knock! -What is it that you called "new dimension"? Okay, let me tell you. If you followed my previous post about OpenCV and Tesseract, you may already know that I've been working on a computer vision project, involving Object Character Recognition (OCR) for texts in a natural scene image - By the way, I really like the problem that I'm trying to solve, that's why I accepted working on the project despite the fact that I don't have a previous…
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Starting with OpenCV and Tesseract OCR on visual studio 2017 [Challenge 1]

Starting with OpenCV and Tesseract OCR on visual studio 2017 [Challenge 1]

challenges, Computer Vision, learning, projects
I have recently started working on a Freelance project where I need to use text scene recognition based on OpenCV and Tesseract as libraries. I was so motivated to hit the Wolrd of computer vision combined with machine learning and experience developing applications in the field, so I welcomed challenges that come with! Here I'll be talking about the first challenge and how I tackled it. found myself with multiple new things to prepare in order to start coding, without mentioning that it's been a long time before when I last coded with C++ (back to my university time)! At first, I was asked to use OpenCV 3.0 and Tesseract 3.02 in order to run the project's part which is already available. So I installed OpenCV 3.0 and Tesseract 3.02…
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Is cracking the coding interview the only benefit of learning algorithms?

Is cracking the coding interview the only benefit of learning algorithms?

challenges, learning, My activities, Technology
Often, algorithms are considered only when someone is looking for a new job. This tight perception of algorithms use puts us away from what algorithms can allow us to achieve! Actually, algorithms are everywhere! Algorithms are involved in each aspect of computer science! Not only that but also used in a wide range of fields: recommendations, social media, medicine, psychology, transportation and the list is longer still! Anything you do, can be broken down into small steps and that, is an Algorithm. Imagine you wake up the morning to go to work and you can’t remember where are your car keys, how would you find them? One approach might be to apply an algorithm, which is a step by step logical procedure. First, you look for places where you used…
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