Fenix0817/Practical-Machine-Learning

Practical machine learning notebook & articles covers the machine learning end to end life cycle.

Practical Machine Learning

Practical machine learning notebook & articles cover the machine learning life cycle.

Overview of Machine Learning Project Life Cycle

• End-to-End Machine Learning Workflow [Part 1]
• End-to-End Machine Learning Workflow [Part 2]

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

• Brief Guide for Machine Learning Model Selection

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

• Machine Learning Models Are No Longer A Black Box

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