Zoumana Keita

⚡ Previously I worked as Machine Learning Engineer at Lincoln for a couple of weeks before moving to the US for my Master in Business & Data Science at Texas Tech University, Rawls College of Business. Before that, I was Data Scientist for 2 years at Axionable, first Sustainable AI startup in France and Canada. Also I spent 2 years and 6 months at IBM as Machine Learning Consultant.
❤️ I love Data Science, Natural Language Processing, Cloud Computing & MLOps
🩺 What keeps me in shape
- When I was in France, I had Taekwondo classes 🥋 on Tuesday, Thursday, Friday & Saturday at Mudo Club Argenteuil
- Daily morning runner 🏃🏾
- Occasional football player ⚽️ with friends
- Attiéké, Yassa, Mafé, Thieb, etc. 😋
🌱 I’m addicted to continuous learning, which makes me grow on a regular basis
🌏 I'm sharing my knowledge through my blog in order to make good impact on others life
📫 How to find me
- 🌐 ZoumDataScience
- 📹 YouTube Channel
- 💡 Medium Articles
- 🏢 LinkedIn

🏆 My Github Stats:

This is the collection of all the resources I have created, organized by topics.

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Title	Article Link	Video
A simple way to understand Association Rule from the Customer Basket Analysis Use Case	🔗
Different Metrics to Evaluate Binary Classification Models and Some Strategies to Choose the Right One	🔗
Introduction to Mito: Spreadsheet for Data Scientists That Also Generates Python Codes	🔗
When R Meets SQL to Query Dataframes	🔗
5 Essential Tools to Start a Career in Data Science and Data Analytics	🔗
4 Types of SQL JOIN Every Data Scientist Should Know: Visual Representation	🔗
Data Preprocessing Using Pipeline in Pandas	🔗	🔗
The guide to choosing the right database for my project: MongoDB vs. MySQL	🔗
How to Run SQL Queries On Your Pandas DataFrames With Python	🔗	🔗
Algorithmic Bias in Healthcare and Some Strategies for Mitigating It	🔗
Which One of These 2 Open-Source Libraries Is Better for Processing Gigabytes of Data?	🔗	🔗
ChatGPT for Data Scientists, Data Analysts, and Programmers	🔗	🔗
Tableau Data Blending Tutorial — A Step-By-Step Guide For Beginners	🔗
Fundamentals of Statistics All Data Scientists & Analysts Should Know — With Code — Part 1	🔗	🔗
Everything You Need to Know About Heatmap — Tutorial With PowerBI	🔗
Top Techniques to Handle Missing Values Every Data Scientist Should Know	🔗
An Introduction to Hierarchical Clustering in Python	🔗
Multiple Linear Regression in R: Tutorial With Examples	🔗
NoSQL Databases: What Every Data Scientist Needs to Know	🔗

Title	Article Link	Video
Transfer Learning: Understand the Big Picture & Make the Right Choices for Your Use Case	🔗
Overview Of 4 Model Validation Approaches to Mitigate Overfitting Problem	🔗
eXplainable AI (XAI): LIME & SHAP, Two Great Candidates to Help You Explain Your Machine Learning Models	🔗
Using Gradio To Create Apps For Your Machine Learning Models	🔗	🔗
How to Perform KMeans Clustering Using Python	🔗	🔗
Classification in Machine Learning: An Introduction	🔗

Title	Article Link	Video
Create An Awesome Streamlit App & Deploy it With Docker	🔗
Machine Learning models monitoring made easy with Mlfow, a concrete use case with Python API	🔗
When Your Machine Learning model teams up with Django REST API, A successful deployment into production	🔗
NLP MLops Project With DagsHub — Multi-Language Sentiment Classification Using Transformers — Part 1	🔗
NLP MLops Project With DagsHub — Deploy Your Streamlit App On AWS EC2 Instance — Part 2	🔗
Step-by-step Approach to Build Your Machine Learning API Using Fast API	🔗
Data And Model Versioning With DVC And Azure Blob Storage	🔗
GitHub Actions for Machine Learning: Train, Test and Deploy Your ML Model on AWS EC2.	🔗
CI/CD for Machine Learning Model Training with GitHub Actions	🔗
Speed Up Your Model Training with DagsHub Direct Data Access on AWS	🔗
Git Reset and Revert Tutorial for Beginners	🔗

Title	Article Link	Video
Do You Want To Cluster Unlabeled Text Data? Try Out Topic Modeling	🔗
Financial Text Classification With Deep Learning Using FinBERT	🔗
Named Entity Recognition with Spacy and the Mighty roBERTa	🔗	🔗
Scientific Documents Similarity Search With Deep Learning Using Transformers (SciBERT)	🔗
Meet BERTopic— BERT’s Cousin For Advanced Topic Modeling	🔗	🔗
Unsupervised Multilingual Text Classification With Zero-Shot Approach	🔗
Semantic Keywords And Keyphrases Extraction With KeyBERT	🔗
4 NLP Libraries for Automatic Language Identification of Text Data In Python	🔗
Data Augmentation in NLP Using Back Translation With MarianMT	🔗	🔗
Social Media Sentiment Analysis In Python With VADER — No Training Required!	🔗	🔗
Stemming, Lemmatization— Which One is Worth Going For?	🔗
VADER Vs. TextBlob — Which One Is Better For Social Media Sentiment Analysis?	🔗
Most Common Text Processing Tasks In Natural Language Processing	🔗	🔗
How to Perform Speech-to-Text and Translate Any Speech to English With OpenAI’s Whisper	🔗	🔗
Plagiarism Detection Using Transformers	🔗	🔗
Text-to-Image and Image-to-image search Using CLIP	🔗
A Step-by-step Guide to Solving 4 Real-life Problems With Transformers and Hugging Face	🔗	🔗
Text data representation with one-hot encoding, Tf-Idf, Count Vectors, Co-occurrence Vectors and Word2Vec	🔗
Fine-Tuning GPT-3 Using the OpenAI API and Python	🔗

Title	Article Link	Video
How I Built A Video Recommendation System Using Large Language Models and Vector Database	🔗
A Framework For Efficiently Serving Your Large Language Models	🔗	🔗
How To Scrape a Web Page With ChatGPT — No Coding Required!	🔗	🔗
How to Chat With Any PDFs and Image Files Using Large Language Models — With Code	🔗	🔗

Title	Article Link	Video
5 Python open-source tools to extract text and tabular data from PDF Files	🔗
When Should You Consider Using Datatable Instead of Pandas to Process Large Data?	🔗
Convert Any Type of Document to Text With Apache Tika Using Python API	🔗
Collect Data From Reddit and Twitter— 600+ Million Monthly Active Users Platforms	🔗
Knockknock — Probably The Best Python Library For Notifications	🔗
Extract Text Written in Different Languages from Images with Python	🔗
Introduction to Twint: Say Goodbye to Twitter Rate Limitations — Also No Need for A Twitter API!	🔗
Avoid Using “pip freeze” — Use “pipreqs” instead	🔗
Extract Tweets Without Limitations in a Few Lines of Code Using Python	🔗	🔗
Collect Data from Twitter: A Step-by-Step Implementation Using Tweepy	🔗
How to Create a Virtual Environment and Use it on Jupyter Notebook	🔗	🔗

Title	Article Link	Video
Pandas and Python Tips and Tricks for Data Science and Data Analysis	🔗	🔗
Pandas & Python Tricks for Data Science & Data Analysis — Part 2	🔗	🔗

Title	Article Link	Video
Five Simple Image Data Augmentation Techniques to Mitigate Overfitting In Computer Vision	🔗
YOLO Object Detection Explained	🔗
How to Measure Model Performance in Computer Vision: A Comprehensive Guide	🔗