✅ *Machine Learning Learning Checklist* 🤖📘
📚 *Fundamentals*
– [ ] Math Essentials (Linear Algebra, Calculus, Probability, Statistics)
– [ ] Python Programming
– [ ] Data Structures & Algorithms
– [ ] Jupyter / Google Colab for experiments
📊 *Data Preprocessing*
– [ ] NumPy & Pandas
– [ ] Handling Missing Data & Outliers
– [ ] Feature Engineering
– [ ] Feature Scaling & Encoding
– [ ] Train-Test Split & Cross-Validation
🧠 *Core ML Concepts*
– [ ] Supervised Learning
– [ ] Unsupervised Learning
– [ ] Overfitting vs Underfitting
– [ ] Bias-Variance Tradeoff
– [ ] Evaluation Metrics (Accuracy, Precision, Recall, F1)
📈 *Key ML Algorithms*
– [ ] Linear & Logistic Regression
– [ ] K-Nearest Neighbors (KNN)
– [ ] Decision Trees & Random Forest
– [ ] Support Vector Machines (SVM)
– [ ] Naive Bayes
– [ ] Clustering (K-Means, Hierarchical)
– [ ] Dimensionality Reduction (PCA, t-SNE)
🛠️ *Libraries & Tools*
– [ ] Scikit-learn
– [ ] XGBoost / LightGBM
– [ ] Statsmodels
– [ ] MLflow (Experiment Tracking)
– [ ] Git & GitHub
📂 *Projects to Build*
– [ ] House Price Prediction
– [ ] Spam Email Classifier
– [ ] Loan Approval Predictor
– [ ] Customer Segmentation
– [ ] Fraud Detection System
🚀 *Practice & Growth*
– [ ] Kaggle Competitions
– [ ] Study ML Case Studies
– [ ] Learn from Notebooks & Blogs
– [ ] Read Research Papers (optional)
– [ ] Document Projects in Portfolio
💬 *Tap ❤️ for more!*