My name is John and I work as a Tech Support Engineer in Consumer Electronics.
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Electronics Repair and Manufacturing with 6 years of professional experience
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Loves working with numbers, making process diagrams, solving technical/business problems, and learning new skills. I self-taught myself SQL, Python and Machine Learning techniques
- Tools: SQL (PostgreSQL), Python (Pandas, Numpy, Matplotlib), Excel (Vlookup, PowerQuery) ReactJS
- Skills: Web Development, Data Cleaning, Exploratory Data Analysis, Data Visualisation, Machine Learning skills
This is based on the curriculum from Daniel Bourke - an inspiring Machine Learning Engineer and writer. These are Coursera's Specilisations which helping me to become a Machine Learning Engineer.
- Learning How to Learn Course — If I'm going to be learning online, I might as well learn how to learn.
- Python for Everybody Specialization — A large amount of machine learning and data science is done in Python. But before I start writing data science code, I'll need to get familiar with the Python programming language.
- Applied Data Science with Python Specialization — After going through the Python for Everybody Specialization, my next move should be to start writing data science specific code with Python.
- Machine Learning Specialization — The Applied Data Science with Python taught me how to visualize and manipulate data, now it's time to find out how to find patterns in it using machine learning algorithms.
- Mathematics for Machine Learning — All of the code I'll be writing triggers mathematics under the hood. In order to understand what's happening behind the scenes, I'll go through the Mathematics for Machine Learning specialization.
- Google Data Analytics Certificate, issued by Coursera in Sep 2021