Name: Kiese Diangebeni Reagan
Type: User
Company: Mbote.Tech
Bio: Blockchain | Artificial Intelligence | Data Science
Twitter: ReaganKiese
Location: Kinshasa, DR Congo
Blog: https://rekidiang2.github.io/kiese.tech/
Kiese Diangebeni Reagan's Projects
Solving challenge from rosalind, a platform for learning bioinformatics and programming through problem solving.
Material of computer science course thought by David Malan from Harvard University
Materials and code on deep learning for audio data
Learning material and projects for statistic, python, machine learning, deep learning and data science
Classification of handwritten digit using machine learning and deep learning techniques.
performed exploratory data analysis using Python on music related datasets.
This course presents how to design a relational database from requirements gathering, to conceptual and logical modeling. We also cover how to query databases using SQL, the ACID properties of a relational database management system, and fundamentals of database programming using triggers, stored procedures, functions, and events. Advanced topics include indexing, transactions, concurrency and recovery. The course will also provide an introduction to non-relational (NoSQL) databases.
Update and publish resume
Well functioning website build with django python's framework. it content blog and user interaction functionality. with
This app apply CRUD operation by making a to do app
🚀✨ Help beginners to contribute to open source projects
Extract keyword from speech
My first GitHub repo!
My game make with kivy
Applying machine learning technique to predict if patient is pre-diabetic stage or not.
Analyse sale data for drug store
Predicting customer churn for a digital music service using big data tools and cloud computing services
Audio data preprocessing to classify music genre using deep learning techniques.
Audio data preprocessing to extract keyword from speech using deep learning techniques.
Audio data preprocessing to classify urban sound using deep learning techniques.
comparing stand up comedians using natural language processing
Extract sand-up comedy transcript, calculate polarity and sensitivity the text to determine its sentiment (positive or negative or neutral)
Topic modeling
text generation