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Machine Learning Engineer
A comprehensive guide for Machine Learning Engineer
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Linux & Shell
Scripting
Math & Statistics
Linear Algebra
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Probability &
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Optimization Basics
Information Theory
A/B Testing &
Hypothesis
Python for Data Science
NumPy & Pandas
Data Visualization
EDA & Data Cleaning
Polars & Dask
Jupyter & Notebooks
Data Leakage
Prevention
Classical Machine Learning
Linear Regression
Logistic Regression
Decision Trees
Random Forest
XGBoost / LightGBM
SVM & Kernel Trick
k-Nearest Neighbors
K-Means
DBSCAN
Hierarchical
Clustering
Gaussian Mixture
Models
PCA / t-SNE / UMAP
Feature Engineering
Model Evaluation &
Metrics
Ensemble Methods
Hyperparameter Tuning
Regularization &
Bias-Variance
Scikit-learn Pipelines
Deep Learning
Perceptron & MLP
Activation Functions
Backpropagation
Loss Functions
BatchNorm & Dropout
PyTorch & TensorFlow
CNNs & Computer Vision
Self-Attention
Multi-head Attention
BERT / RoBERTa
GPT / Decoder-only
Positional Encoding
Transfer Learning &
Fine-tuning
Training at Scale
Optimizers &
Regularization
RNNs, LSTMs & Seq2Seq
Specializations
Tokenization &
Embeddings
BERT / RoBERTa / T5
GPT & Seq-to-Seq
NER, QA, Summarization
Fine-tuning PLMs
Computer Vision (YOLO,
ViT)
Recommender Systems
Prompt Engineering
RAG Pipeline
LoRA / QLoRA (PEFT)
Vector DBs (FAISS,
Pinecone)
Agentic Systems & LLM
APIs
Time Series
Forecasting
Reinforcement Learning
Anomaly Detection
Multi-modal Models
(CLIP)
Practical ML Engineering
Experiment Tracking
(MLflow, W&B)
Model Interpretability
(SHAP, LIME)
Data Validation (Great
Expectations)
Config-driven
Experiments (Hydra)
Data Versioning (DVC)
Feature Stores (Feast)
Reproducible Pipelines
Custom Transformers
(sklearn)
MLOps & Deployment
FastAPI / Flask Model
Serving
Docker & Kubernetes
CI/CD for ML Pipelines
Cloud ML (AWS / GCP /
Azure)
Monitoring & Data
Drift
Pipeline Orchestration
(Airflow)
Model Registries &
Versioning
Triton & Serving
Frameworks
Advanced & GenAI Engineering
LLM Pretraining & RLHF
/ DPO
RAG & Vector Databases
Agentic Systems & Tool
Use
Model Compression &
Quantization
AI Safety, Fairness &
Ethics
Distributed Training
(FSDP)
Causal Inference &
Uplift
Federated Learning
Capstone: End-to-End ML System
Curated Resources
Hugging Face Courses
Andrej Karpathy — Zero to Hero
Papers With Code
Start Your ML Journey