My name is
- ⚡ 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
This is the collection of all the resources I have created, organized by topics.
Subscribe to:
- My YouTube channel for videos related to Python and data sience
- My Medium newsletter for updates of my blogs in your mailbox
- ZoumDataScience for Pandas and Python tips and trics in your mailbox
- Data Science
- Machine Learning
- MLOps
- Natural Language Processing
- Large Language Models
- Retrieval Augmented Generation
- Python
- Pandas & Python Tricks
- Computer Vision
| 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 |
|---|---|---|
| Multimodal Retrieval Augmented Generation Applied To Real World Case — With Code | 🔗 | 🔗 |
| 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 | 🔗 | 🔗 |
| Multimodal Retrieval Augmented Generation Applied To Real World Case — With Code | 🔗 | 🔗 |
| Document Parsing Using Large Language Models — With Code | 🔗 | 🔗 |
| How to Build Anything With AI Agents - With Code | 🔗 |
| Title | Article Link | Video |
|---|---|---|
| How I Built A Video Recommendation System Using Large Language Models and Vector Database | 🔗 | |
| How to Build RAG based Chatbot: 5 Steps with Amazon Bedrock | 🔗 |
| 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 | 🔗 |