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Repost from Machine Learning with Python
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Repost from Machine Learning with Python
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Repost from Machine Learning
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Repost from Machine Learning
🚀 Machine Learning Workflow: Step-by-Step Breakdown
Understanding the ML pipeline is essential to build scalable, production-grade models.
👉 Initial Dataset
Start with raw data. Apply cleaning, curation, and drop irrelevant or redundant features.
Example: Drop constant features or remove columns with 90% missing values.
👉 Exploratory Data Analysis (EDA)
Use mean, median, standard deviation, correlation, and missing value checks.
Techniques like PCA and LDA help with dimensionality reduction.
Example: Use PCA to reduce 50 features down to 10 while retaining 95% variance.
👉 Input Variables
Structured table with features like ID, Age, Income, Loan Status, etc.
Ensure numeric encoding and feature engineering are complete before training.
👉 Processed Dataset
Split the data into training (70%) and testing (30%) sets.
Example: Stratified sampling ensures target distribution consistency.
👉 Learning Algorithms
Apply algorithms like SVM, Logistic Regression, KNN, Decision Trees, or Ensemble models like Random Forest and Gradient Boosting.
Example: Use Random Forest to capture non-linear interactions in tabular data.
👉 Hyperparameter Optimization
Tune parameters using Grid Search or Random Search for better performance.
Example: Optimize max_depth and n_estimators in Gradient Boosting.
👉 Feature Selection
Use model-based importance ranking (e.g., from Random Forest) to remove noisy or irrelevant features.
Example: Drop features with zero importance to reduce overfitting.
👉 Model Training and Validation
Use cross-validation to evaluate generalization. Train final model on full training set.
Example: 5-fold cross-validation for reliable performance metrics.
👉 Model Evaluation
Use task-specific metrics:
- Classification – MCC, Sensitivity, Specificity, Accuracy
- Regression – RMSE, R², MSE
Example: For imbalanced classes, prefer MCC over simple accuracy.
💡 This workflow ensures models are robust, interpretable, and ready for deployment in real-world applications.
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Repost from Python Courses & Resources
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Repost from Machine Learning with Python
💛 Top 10 Best Websites to Learn Machine Learning ⭐️
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---
🧠 Google’s ML Course
🔗 https://developers.google.com/machine-learning/crash-course
📈 Kaggle Courses
🔗 https://kaggle.com/learn
🧑🎓 Coursera – Andrew Ng’s ML Course
🔗 https://coursera.org/learn/machine-learning
⚡️ Fast.ai
🔗 https://fast.ai
🔧 Scikit-Learn Documentation
🔗 https://scikit-learn.org
📹 TensorFlow Tutorials
🔗 https://tensorflow.org/tutorials
🔥 PyTorch Tutorials
🔗 https://docs.pytorch.org/tutorials/
🏛️ MIT OpenCourseWare – Machine Learning
🔗 https://ocw.mit.edu/courses/6-867-machine-learning-fall-2006/
✍️ Towards Data Science (Blog)
🔗 https://towardsdatascience.com
---
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Repost from Machine Learning with Python
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