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 fundamentals, advanced neural architectures, key tools, and deployment skills needed for modern machine learning careers, with a focus on practical mastery indicators.
Check off skills you've mastered. Use the scoring guide to assess your level, then follow the next steps to fill gaps and build your portfolio.
Core Fundamentals & Mathematics
Foundational knowledge in statistics, linear algebra, and core ML algorithms necessary for understanding and building models.
- 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, enabling comprehension of model internals and optimization.
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 clustering algorithms for tabular data.
How to build it: Scikit-learn documentation, Kaggle tutorials
- essential
Data Preprocessing & Feature Engineering
Proficient in cleaning data, handling missing values, and creating informative features for model training.
How to build it: Pandas/NumPy tutorials, 'Feature Engineering for ML' book
- essential
Model Evaluation & Validation
Can design robust train/test splits, use cross-validation, and interpret metrics like precision-recall and ROC-AUC.
How to build it: ML courses on Coursera, practice on Kaggle 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 and convolutional networks from scratch, understanding backpropagation.
How to build it: Deep Learning Specialization (Coursera), PyTorch/TensorFlow tutorials
- essential
PyTorch/TensorFlow Proficiency
Can efficiently build, train, and debug models using one primary framework (PyTorch or TensorFlow).
How to build it: Official PyTorch/TensorFlow guides, fast.ai course
- important
Hyperparameter Tuning & Optimization
Can use tools like Optuna or Ray Tune to systematically optimize model performance.
How to build it: Documentation for Optuna, Hyperopt, or Keras Tuner
- important
Computer Vision with CNNs
Can implement architectures like ResNet for tasks like image classification and object detection.
How to build it: CS231n (Stanford), PyTorch Vision tutorials
- important
Natural Language Processing (NLP)
Can build models for text classification, named entity recognition, or sentiment analysis using embeddings.
How to build it: Hugging Face Course, 'Speech and Language Processing' book
- important
Transformer Architectures
Understands and can implement or fine-tune transformer models (e.g., BERT, GPT variants) for NLP tasks.
How to build it: Hugging Face Transformers library, 'The Illustrated Transformer' blog
- nice-to-have
Reinforcement Learning Fundamentals
Can implement basic algorithms like Q-Learning or policy gradients in simulated environments.
How to build it: Spinning Up in Deep RL (OpenAI), 'Reinforcement Learning: An Introduction'
MLOps & Production Deployment
Skills for taking models from experimentation to reliable, scalable production systems.
- important
Model Deployment & Serving
Can containerize a model with Docker and deploy it as an API using Flask/FastAPI or cloud services.
How to build it: Docker documentation, FastAPI tutorial, AWS SageMaker/Google AI Platform
- important
Experiment Tracking with MLflow
Can log experiments, parameters, metrics, and models to track and reproduce ML projects.
How to build it: MLflow quickstart, Databricks community tutorials
- important
Version Control for ML (DVC/Git)
Uses Git for code and DVC for data/model versioning to ensure project reproducibility.
How to build it: DVC documentation, Git tutorials
- important
Model Monitoring & Maintenance
Can set up monitoring for model performance drift and data quality in production.
How to build it: Evidently AI, Prometheus/Grafana for metrics
- nice-to-have
GPU Acceleration with CUDA
Understands CUDA basics to leverage GPU acceleration for training and inference in frameworks.
How to build it: NVIDIA CUDA toolkit guides, PyTorch CUDA documentation
- nice-to-have
Cloud ML Services (AWS/GCP/Azure)
Can use managed services for training, deployment, and pipeline orchestration on a major cloud platform.
How to build it: Cloud provider certifications, official tutorials
Advanced & Research Skills
Capabilities for pushing boundaries, reading research, and adapting to new advancements.
- important
Reading & Implementing Research Papers
Can read recent ML papers from conferences (NeurIPS, ICML) and implement key algorithms.
How to build it: Papers With Code, arXiv, replication projects on GitHub
- important
Advanced NLP with Hugging Face
Can fine-tune and deploy state-of-the-art transformer models for complex tasks using the Hugging Face ecosystem.
How to build it: Hugging Face documentation, advanced NLP courses
- nice-to-have
Generative Models (GANs, Diffusion)
Understands and can experiment with generative architectures for image or text synthesis.
How to build it: GAN tutorials, Stable Diffusion/DALL-E guides
- nice-to-have
Model Compression & Optimization
Can apply techniques like quantization, pruning, or knowledge distillation for efficient deployment.
How to build it: PyTorch Mobile, TensorFlow Lite, research papers on efficiency
- nice-to-have
Ethical AI & Bias Mitigation
Can assess models for fairness, explainability, and societal impact, applying mitigation strategies.
How to build it: Fairlearn, IBM AI Fairness 360, relevant literature
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 foundation. Deepen expertise in one advanced area and start learning MLOps. |
| Advanced | 61-85% | You're highly skilled. Strengthen production deployment skills and explore research frontiers. |
| 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 on this list. Identify your biggest gaps in essential and important categories.
Plan a Learning Sprint
Pick one gap area (e.g., MLOps) and dedicate 2-3 weeks to complete a focused course or tutorial series.
Build a Capstone Project
Create a project that uses a skill from each major section (e.g., train a transformer model and deploy it with MLflow).
Network & Seek Feedback
Share your project on LinkedIn or at a local meetup. Ask for code reviews from experienced practitioners.
Tips that make the difference
- Build a portfolio with 2-3 end-to-end projects (data to deployed model) on GitHub.
- Contribute to open-source ML projects on GitHub to gain real-world collaboration experience.
- Stay updated by following key researchers and labs on Twitter/X and reading arXiv daily.
- Practice explaining your projects and model choices clearly, as communication is critical in interviews.
- Use cloud credits (e.g., Google Colab Pro, AWS Educate) to gain hands-on experience with scalable tools.
Track Your Progress to ML Mastery
Use Edirae to log your skills, set goals, and build a personalized learning roadmap for your target 2026 role.
Start learning free