Practical Machine Learning
Practical machine learning notebook & articles cover the machine learning life cycle.
Overview of Machine Learning Project Life Cycle
Setting Performance Baseline & Success Metrics
- How Data Science is Helping Businesses Stay Ahead of the Game: 9 Inspiring Use Cases
- How to Set Performance Baseline for Your Machine Learning Project Effectively?
Data Collection
Data Preprocessing & Feature Engineering
- How To Split Data Effectively for Your Data Science Project [Code | Article | Kaggle Notebook]
- Six Reasons Why Your Model Gives Bad Results
Modeling
Supervised Machine Learning Modeling
- Practical Guide to Support Vector Machine in Python [Code | Article]
- Practical Guide to Boosting Algorithms In Machine Learning [Code | Article]
Unsupervised Machine Learning Modeling
- Overview of Unsupervised Machine Learning Tasks & Applications
- Practical Guide to Dimesnioality Reduction in Python [Code | Article]
- How to Find the Optimal Number of Clusters Effectively [ Code | Article | Kaggle Notebook ]
Deep Learning Modeling
- Maximizing the Impact of Data Augmentation: Effective Techniques and Best Practices
- Building Complex Models Using Keras Functional API [Code | Article | Kaggle Notebook]
- A Quick Setup for Neural Networks Hyperparameters for Best Results
- Building A Recurrent Neural Network From Scratch In Python
Model Evaluation
Why Should You Not Completely Trust In Test Accuracy?
Machine Learning Explainability
MLOps & Model Deployment
- Step-by-Step Guide on Deploying Yolo3 Model on Fast API Article | Code
- Common Machine Learning Deployment Patterns & Their Applications
- Key Challenges of Machine Learning Model Deployment
- From Detection to Correction: How to Keep Your Production Data Clean and Reliable
- A Comprehensive Introduction to Machine Learning Experiment Tracking