β
Machine Learning Engineer Roadmap
π Fundamentals
- Mathematics
β’ Linear Algebra
β’ Calculus
β’ Probability & Statistics
- Programming
β’ Python (main)
β’ SQL
β’ Data Structures & Algorithms
π Core Machine Learning
- Supervised Learning
β’ Linear & Logistic Regression
β’ Decision Trees, Random Forests
β’ SVM, KNN, Naive Bayes
- Unsupervised Learning
β’ K-Means, DBSCAN
β’ PCA, t-SNE
- Model Evaluation
β’ Precision, Recall, F1-Score
β’ ROC, AUC
β’ Cross-validation
π§ Deep Learning
- Neural Networks
β’ Feedforward, CNN, RNN
β’ Optimizers, Loss Functions
- Transformers
β’ Attention
β’ BERT, models
- Frameworks
β’ TensorFlow
β’ PyTorch
π Data Handling
- Data Cleaning & Preprocessing
- Feature Engineering
- Handling Imbalanced Data
π Tools & Workflow
- Jupyter, VS Code
- Git & GitHub
- Docker & MLflow
βοΈ Deployment
- APIs (Flask/FastAPI)
- CI/CD Basics
- Deployment on AWS / GCP / Azure
π Real-World Projects
- End-to-End ML Pipelines
- Model Serving & Monitoring
- Performance Tuning
π§βπΌ Soft Skills & Ethics
- Communication with stakeholders
- Data Privacy & AI Ethics
- Explainable AI
π Platforms to Learn
- Kaggle
- Coursera
- fast.ai
- Hugging Face
- Papers with Code
π Tap β€οΈ for more!
Post #2018
6.61K
- β€ 15