Topic: Machine Learning
The ML landscape is evolving rapidly. This 2026 checklist ensures you master the skills that matter for top roles like ML Engineer and AI Researcher.
This checklist covers core ML fundamentals, advanced neural architectures, essential tools like PyTorch and Hugging Face, and critical deployment skills for production systems.
Check off skills you've mastered. Use the scoring guide to assess your level and the next steps to build a targeted learning plan.
Core Fundamentals & Mathematics
Foundational knowledge in statistics, linear algebra, and core ML algorithms required for all roles.
- essential
Probability & Statistics
Can apply concepts like distributions, hypothesis testing, and Bayesian inference to model evaluation and uncertainty quantification.
How to build it: University courses, 'Introduction to Statistical Learning' book
- essential
Linear Algebra & Calculus
Understands matrix operations, eigenvectors, and gradients for implementing and debugging neural network training.
How to build it: Khan Academy, 3Blue1Brown YouTube series
- essential
Classical ML with Scikit-learn
Can implement, evaluate, and tune models like Random Forests, SVMs, and Gradient Boosting for tabular data.
How to build it: Scikit-learn documentation, Kaggle micro-courses
- essential
Model Evaluation & Validation
Proficient in cross-validation, bias-variance tradeoff, and using metrics like precision-recall, F1, and ROC-AUC.
How to build it: ML course modules on validation, 'Hands-On ML' book
- essential
Data Preprocessing & Feature Engineering
Can clean data, handle missing values, encode categorical variables, and create informative features.
How to build it: Pandas tutorials, Kaggle feature engineering competitions
Deep Learning & Neural Networks
Skills in designing, training, and tuning modern neural network architectures using key frameworks.
- essential
Neural Network Fundamentals
Can build and train feedforward, convolutional, and recurrent networks from scratch or using high-level APIs.
How to build it: Deep Learning Specialization (Coursera), PyTorch tutorials
- essential
PyTorch/TensorFlow Proficiency
Can efficiently use tensors, autograd, and modules to build custom models and training loops.
How to build it: Official PyTorch/TensorFlow guides, 'Deep Learning with PyTorch' book
- important
Hyperparameter Tuning & Optimization
Experienced with tools like Optuna or Ray Tune to systematically optimize learning rates, architectures, and more.
How to build it: Documentation for Optuna, Hyperopt, or Weights & Biases
- important
Computer Vision with CNNs
Can implement architectures like ResNet for tasks like image classification, object detection, and segmentation.
How to build it: Fast.ai course, OpenCV tutorials, torchvision
- important
Natural Language Processing (NLP)
Can build models for text classification, named entity recognition, and sentiment analysis using embeddings and RNNs/CNNs.
How to build it: Hugging Face Course, 'Speech and Language Processing' book
- important
Transformer Architectures
Understands and can implement attention mechanisms and fine-tune pre-trained models like BERT or GPT for specific tasks.
How to build it: The Illustrated Transformer blog, Hugging Face Transformers library
- nice-to-have
Reinforcement Learning Fundamentals
Can implement Q-learning, policy gradients, or use stable-baselines3 for simple environments.
How to build it: Spinning Up in Deep RL, OpenAI Gym documentation
MLOps & Production Deployment
Skills for versioning, deploying, monitoring, and maintaining ML models in production environments.
- important
Model Deployment Patterns
Can deploy models as REST APIs using Flask/FastAPI or containerize them with Docker for cloud services.
How to build it: FastAPI tutorials, Docker for ML courses
- important
Experiment Tracking with MLflow
Uses MLflow to log parameters, metrics, and artifacts to reproduce and compare model runs.
How to build it: MLflow quickstart, Databricks community edition
- important
Model Versioning & Registry
Can manage model versions, stage transitions (staging to production), and lineage using MLflow or similar.
How to build it: MLflow model registry guide, DVC tutorials
- nice-to-have
GPU Acceleration with CUDA
Understands CUDA basics to leverage GPU acceleration in PyTorch/TensorFlow for faster training and inference.
How to build it: NVIDIA CUDA toolkit docs, PyTorch CUDA semantics guide
- nice-to-have
CI/CD for ML Pipelines
Can set up automated testing, building, and deployment of ML models using GitHub Actions or Jenkins.
How to build it: GitHub Actions for ML, MLOps Zoomcamp
- nice-to-have
Model Monitoring & Drift Detection
Implements logging and alerting for performance degradation and data/concept drift in live systems.
How to build it: Evidently AI, Amazon SageMaker Model Monitor
Advanced Topics & Research
Cutting-edge skills for pushing boundaries, reading research, and contributing to the field.
- important
Reading & Implementing Research Papers
Can read recent ML papers from arXiv/NeurIPS and implement key algorithms or reproduce results.
How to build it: Papers With Code, ML conference proceedings
- important
Advanced NLP with Hugging Face
Proficient in using and fine-tuning state-of-the-art models from the Hugging Face Hub for complex NLP tasks.
How to build it: Hugging Face advanced tutorials, transformer model documentation
- nice-to-have
Generative Models (GANs, Diffusion)
Understands and can implement generative models like GANs or Diffusion Models for image/text generation.
How to build it: GAN specialization (Coursera), Denoising Diffusion Probabilistic Models paper
- nice-to-have
Large Language Model (LLM) Fine-tuning
Can adapt large pre-trained LLMs using techniques like LoRA or prompt tuning for specific applications.
How to build it: Hugging Face PEFT library, OpenAI fine-tuning guide
- nice-to-have
Efficient Model Training & Inference
Applies techniques like quantization, pruning, and knowledge distillation to optimize model size and speed.
How to build it: PyTorch quantization, TensorFlow Model Optimization Toolkit
Where you stand
| Level | Skills checked | What it means |
|---|---|---|
| Beginner | 0-30% | You're starting out. Focus on Core Fundamentals and basic Deep Learning skills. |
| Intermediate | 31-60% | You have a solid base. Deepen your expertise in a specialization and start learning MLOps. |
| Advanced | 61-85% | You're highly skilled. Master production deployment and contribute to advanced research areas. |
| Job ready | 86-100% | You are competitive for roles like ML Engineer or Applied Scientist. Showcase projects and prepare for interviews. |
Next steps
Audit Your Skills
Check off skills you have and identify your biggest gaps using the scoring guide above.
Build a Specialization Project
Choose one area (e.g., NLP with Transformers) and create a detailed project that demonstrates full pipeline mastery.
Engage with the Community
Join ML Discord servers, attend local meetups or virtual conferences to network and learn.
Prepare for Technical Interviews
Practice coding (LeetCode), system design for ML, and explaining your project choices.
Tips that make the difference
- Build a portfolio of end-to-end projects (from data to deployed model) on GitHub.
- Contribute to open-source ML libraries (e.g., on Hugging Face or PyTorch) to gain visibility.
- Stay current by following key researchers and labs on X/Twitter and reading arXiv daily.
- Practice explaining complex ML concepts simply, critical for interviews and collaboration.
- Participate in Kaggle competitions or hackathons to solve real-world problems under constraints.
Track Your ML Mastery Journey
Use Edirae to log your progress, set goals, and get personalized recommendations to land your target role by 2026.
Start learning free