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Hallo,
My name is Steve Edgar, and I'm passionate about web and AI technologies. After obtaining my Master's degree in Linguistics and my Secondary and High School Education Teacher's Diplom in computer science, second level, I taught computer science in High School for 4 years. During this period I developed a great passion for Frontend Web Development. Today, I live my passion by creating web environments that offer a user-friendly and intuitive experience, as well as developing and integrating Chatbots. I'm proficient in HTML, CSS, JavaScript, React Js, Bootstrap and Chatbot development platforms such as Dialogflow, Chatfuel and ManyChat. From responsive web design to AI-powered chatbots, I offer customized solutions tailored to your needs.

My Achievements

My Skills

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My strengths

  • High analytical skills

  • Versatile

  • Autonomy

  • IT thinking

  • Resilient

skil

My Frontend skills

  • JavaScript

  • React JS

  • PHP

  • CSS

  • Bootstrap Framework

  • HTML

  • Rest API

challenges

My challenges

  • To be a Fullstack web developer

  • To develop a CMS

  • To Lead web application development projects

  • To build a learning community in web and webApp development

Tutorbot

Fezebot is a chatbot to help first year high school students to learn the basic hardware and software architecture of a computer. This chatbot was developed with Dialogflow and deployed on Telegram in order to be closer to the students.

FAQbot

This chatbot will be an information tool on the documents to be gathered to constitute a career file. This conversational agent is intended for the staff of the Ministry of Secondary Education of Cameroon, student teachers at the end of their training and any other user wishing to constitute or help a relative to constitute a career file. The chatbot is currently deployed on Telegram for a test phase. This chatbot is developed with the help of Dialogflow.

conceptionIMG

PHD project

The proposed model uses Spacy to extract grammatical data from the source text words, then it performs word embedding of this data and concatenates it with the corresponding source word vectors to obtain deep vectors, and finally these vectors are passed to the Neural machine translation model

Linguistics data extraction

I have written a program to tokenise a sentence and extract all the grammatical data of each word (lemma, part of speech, gender, number, verbal form and tense, syntactic dependencies) and then I build a parse tree of the sentence. To do this, we use Python, the Spacy library and notebook of Google Colaboratory

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My Frontend Skills

HTML
CSS
Bootstrap
JavaScript
React
Figma
85%
75%
70%
75%
70%
85%


My Backend Skills

Node JS
MongoDB
Express
45%
30%
30%