My Data Science Notes

XGBoost: Everything You Need to Know

Math of Gradient Boosting Explained

Dimensionality Reduction

PCA and Clustering - sklearn

Clustering

Measuring Similarity

Recommendation

A Comprehensive Review of Recommender Systems (in progress)

RecSys Review + TorchRec + TFRS

Vinija's Notes: RecSys List of Papers

Entity Resolution

Market Basket Analysis

How to avoid machine learning pitfalls: a guide for academic researchers

Introduction to ML - Class Notes - CMU

A Primer to the 42 Most Commonly Used ML Algorithms

Pen and Paper Exercises in ML

List of Next Topics

MLOps - Building ML Platform in Retail and eCommerce
MLOps - How to Build an End-To-End ML Pipeline
MLOps - How to Build a CI/CD MLOps Pipeline [Case Study]
MLOps - Data Science Project Management [The New Guide For ML Teams]
Recommender Systems - Recommender Systems: Lessons From Building and Deployment
MLOps - Machine Learning Model Management: What It Is, Why You Should Care, and How to Implement It
MLOps - Deploying ML Models: How to Make Sure the New Model Is Better Than the One in Production? [Practical Guide]
Recommender Systems - Recommender Systems: Machine Learning Metrics and Business Metrics
Recommender Systems - How to Test a Recommender System
MLOps - Building a Machine Learning Platform [Definitive Guide]
ML Model Development - How to Build an Experiment Tracking Tool [Learnings From Engineers Behind Neptune]
ML Model Development - How to Version and Organize ML Experiments That You Run in Google Colab
ML Model Development - 15 Best Tools for ML Experiment Tracking and Management
ML Model Development - Experiment Tracking in Kubeflow Pipelines
ML Model Development - Switching from Spreadsheets to Experiment Tracker and How It Pushed My Model Development Process to the Next Level
MLOps - Experiment Tracking vs Machine Learning Model Management vs MLOps
ML Model Development - The Best Tools to Monitor Machine Learning Experiment Runs
MLOps - Model Deployment Strategies
ML Model Development - Hyperparameter Tuning in Python: a Complete Guide
ML Model Development - Optuna vs Hyperopt: Which Hyperparameter Optimization Library Should You Choose?
ML Model Development - Performance Metrics in Machine Learning [Complete Guide]
NLP - Exploratory Data Analysis for Natural Language Processing: A Complete Guide to Python Tools
ML Model Development - ML Model Packaging [The Ultimate Guide]
ML Application - Knowledge Graphs With Machine Learning [Guide]
Time Series - How to Select a Model For Your Time Series Prediction Task [Guide]
MLOps - MLOps at a Reasonable Scale [The Ultimate Guide]
MLOps - A Comprehensive Guide on How to Monitor Your Models in Production
MLOps - How to Build MLOps Pipelines with GitHub Actions [Step by Step Guide]
ML Model Development - Distributed Training: Guide for Data Scientists
ML Application - How to Implement Customer Churn Prediction [Machine Learning Guide for Programmers]
Tools - Installing TensorFlow 2 GPU [Step-by-Step Guide]
ML Application - How to Work with Autoencoders [Case Study Guide]
ML Model Development - In-depth Guide to ML Model Debugging and Tools You Need to Know
MLOps - ML from Research to Production – Challenges, Best Practices and Tools [Guide]
MLOps - MLOps Architecture Guide
NLP - Vectorization Techniques in NLP [Guide]
ML Model Development - Model Debugging Strategies: Machine Learning Guide
MLOps - How These 8 Companies Implement MLOps: In-Depth Guide
NLP - The Ultimate Guide to Word Embeddings
Tools - Feature Stores: Components of a Data Science Factory [Guide]
Neural Net - A Comprehensive Guide to the Backpropagation Algorithm in Neural Networks
Vision - Guide to Building Your Own Neural Network [With Breast Cancer Classification Example]
NLP - Recurrent Neural Network Guide: a Deep Dive in RNN
ML Model Development - A Comprehensive Guide to Data Preprocessing
Tools - Streamlit Guide: How to Build Machine Learning Applications
Neural Net - Transfer Learning Guide: A Practical Tutorial With Examples for Images and Text in Keras
NLP - Conversational AI Architectures Powered by Nvidia: Tools Guide
ML Model Development - Monte Carlo Simulation: A Hands-On Guide
ML Model Development - Optuna Guide: How to Monitor Hyper-Parameter Optimization Runs
Neural Net - How to Use Google Colab for Deep Learning – Complete Tutorial
Neural Net - Gumbel Softmax Loss Function Guide + How to Implement it in PyTorch
ML Model Development - The Ultimate Guide to Evaluation and Selection of Models in Machine Learning
Time Series - Model Monitoring for Time Series
ML Model Development - Exploratory Data Analysis for Tabular Data
ML Model Development - Overfitting vs Underfitting in Machine Learning: Everything You Need to Know
ML Application - Predicting Stock Prices Using Machine Learning
ML Model Development - How to Scale ML Projects – Lessons Learned from Experience
MLOps - MLOps Engineer and What You Need to Become One?
ML Model Development - ML Model Interpretation Tools: What, Why, and How to Interpret
ML Model Development - Developing AI/ML Projects For Business – Best Practices
ML Model Development - How to Organize Your ML Development in an Efficient Way
Vision - TensorFlow Object Detection API: Best Practices to Training, Evaluation & Deployment
Vision - How to Train Your Own Object Detector Using TensorFlow Object Detection API
Neural Net - Deep Dive Into TensorBoard: Tutorial With Examples
ML Model Development - How to Manage, Track, and Visualize Hyperparameters of Machine Learning Models?
ML Application - Tabular Data Binary Classification: All Tips and Tricks from 5 Kaggle Competitions
ML Model Development - Understanding LightGBM Parameters (and How to Tune Them)
Tools - Feature Stores: Components of a Data Science Factory [Guide]
Vision - Image Segmentation: Architectures, Losses, Datasets, and Frameworks
Neural Net - 8 Creators and Core Contributors Talk About Their Model Training Libraries From PyTorch Ecosystem
Neural Net - Keras Metrics: Everything You Need to Know
ML Model Development - How to Do Hyperparameter Tuning on Any Python Script in 3 Easy Steps
Tools - Feature Stores: Components of a Data Science Factory [Guide]
ML Model Development - Scikit Optimize: Bayesian Hyperparameter Optimization in Python
ML Model Development - Machine Learning Experiment Management: How to Organize Your Model Development Process
NLP - What Does GPT-3 Mean For the Future of MLOps? With David Hershey
ML Model Development - How to Build ETL Data Pipeline in ML
ML Model Development - Distributed Training: Errors to Avoid
ML Model Development - Good Design in ML Applications With Konrad Piercey
MLOps - Building MLOps Pipeline for NLP: Machine Translation Task [Tutorial]
NLP - Hugging Face Pre-trained Models: Find the Best One for Your Task
NLP - Building a Search Engine With Pre-Trained Transformers: A Step By Step Guide
Vision - Building Deep Learning-Based OCR Model: Lessons Learned
NLP - How to Deploy NLP Models in Production
NLP - Transformer Models for Textual Data Prediction
NLP - MLOps Tools for NLP Projects
NLP - 10 NLP Projects to Boost Your Resume
NLP - Tokenization in NLP: Types, Challenges, Examples, Tools
NLP - How to Code BERT Using PyTorch – Tutorial With Examples
NLP - Natural Language Processing with Hugging Face and Transformers
NLP - Best Tools For NLP Projects That Every Data Scientist and ML Engineer Should Try
NLP - Comprehensive Guide to Transformers
NLP - Unmasking BERT: The Key to Transformer Model Performance
NLP - Building Machine Learning Chatbots: Choose the Right Platform and Applications
NLP - 10 Things You Need to Know About BERT and the Transformer Architecture That Are Reshaping the AI Landscape
NLP - Latent Dirichlet Allocation (LDA) Tutorial: Topic Modeling of Video Call Transcripts (With Zoom)
NLP - Wasserstein Distance and Textual Similarity
NLP - Training, Visualizing, and Understanding Word Embeddings: Deep Dive Into Custom Datasets
NLP - pyLDAvis: Topic Modelling Exploration Tool That Every NLP Data Scientist Should Know
NLP - Text Classification: All Tips and Tricks from 5 Kaggle Competitions
NLP - Document Classification: 7 Pragmatic Approaches for Small Datasets
ML Model Development - Training Models on Streaming Data [Practical Guide]
Time Series - ARIMA vs Prophet vs LSTM for Time Series Prediction
Tools - Argo vs Airflow vs Prefect: How Are They Different
Time Series - Time Series Projects: Tools, Packages, and Libraries That Can Help
ML Model Development - Best Machine Learning Model Management Tools That You Need to Know
ML Model Development - How to Make Your Sacred Projects Easy to Share and Collaborate On
ML Application - Best ML Model Registry Tools
ML Application - Implementing Customer Segmentation Using Machine Learning [Beginners Guide]
ML Model Development - Random Forest Regression: When Does It Fail and Why?
ML Model Development - Tabular Data Binary Classification: All Tips and Tricks from 5 Kaggle Competitions
Vision - Building MLOps Pipeline for Computer Vision: Image Classification Task [Tutorial]
Vision - Building and Deploying CV Models: Lessons Learned From Computer Vision Engineer
ML Model Development - Classification in ML: Lessons Learned From Building and Deploying a Large-Scale Model
Vision - Object Detection with YOLO: Hands-on Tutorial
Vision - Imbalanced Data in Object Detection Computer Vision Projects
Vision - Deploying Computer Vision Models: Tools & Best Practices
Vision - Pix2pix: Key Model Architecture Decisions
Vision - Deploying Your Next Image Classification on Serverless (AWS Lambda, GCP Cloud Function, Azure Automation)
Vision - Object Detection Algorithms and Libraries
Vision - 15 Computer Visions Projects You Can Do Right Now
Vision - Applications of AI in Drone Technology: Building Machine Learning Models That Work on Drones (With TensorFlow/Keras)
Vision - How to Build a Lightweight Image Classifier in TensorFlow / Keras
Vision - Understanding Few-Shot Learning in Computer Vision: What You Need to Know
Vision - Dive Into Football Analytics With TensorFlow Object Detection API
Vision - Image Classification: Tips and Tricks From 13 Kaggle Competitions (+ Tons of References)
Vision - Image Processing in Python: Algorithms, Tools, and Methods You Should Know
ML Model Development - When to Choose CatBoost Over XGBoost or LightGBM [Practical Guide]
ML Model Development - XGBoost vs LightGBM: How Are They Different
ML Model Development - K-Means Clustering Explained
ML Model Development - Exploring Clustering Algorithms: Explanation and Use Cases
ML Model Development - How to Deal With Imbalanced Classification and Regression Data
ML Model Development - 7 Cross-Validation Mistakes That Can Cost You a Lot [Best Practices in ML]
ML Model Development - Cross-Validation in Machine Learning: How to Do It Right
Tools - AutoML Solutions: What I Like and Don’t Like About AutoML as a Data Scientist

Machine Learning