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, and deployment skills. It's designed to guide your learning from foundational concepts to production-ready expertise.
Check off skills you've mastered. Use the scoring guide to assess your level. Focus on 'essential' items first, then build depth in your chosen specialization.
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'
- 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 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 appropriate metrics (Precision, Recall, AUC-ROC).
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, feature engineering blogs
Deep Learning & Neural Networks
Skills in designing, training, and tuning modern neural network architectures.
- essential
Neural Network Fundamentals
Can build and train feedforward networks from scratch, understanding activation functions, loss, and backpropagation.
How to build it: Deep Learning Specialization (Coursera), PyTorch/TF tutorials
- important
Convolutional Neural Networks (CV)
Can design CNN architectures (e.g., ResNet) for image classification, object detection, or segmentation tasks.
How to build it: CS231n (Stanford), OpenCV tutorials, torchvision
- important
Recurrent Networks & NLP Basics
Understands RNNs, LSTMs, and can implement sequence models for tasks like text classification or generation.
How to build it: CS224n (Stanford), NLP with PyTorch book
- essential
Transformer Architectures
Can explain and implement transformer components (attention, embeddings) and use pre-trained models from Hugging Face.
How to build it: Hugging Face course, 'The Illustrated Transformer' blog
- important
Hyperparameter Tuning & Optimization
Proficient in using tools like Optuna or Ray Tune to systematically optimize model performance.
How to build it: Framework documentation, research papers on optimization
Tools, Frameworks & Deployment
Practical skills with industry-standard tools for development, experimentation, and production deployment.
- essential
PyTorch Proficiency
Can build custom models, datasets, and training loops using PyTorch's tensor operations and autograd.
How to build it: PyTorch official tutorials, 'Deep Learning with PyTorch' book
- important
TensorFlow/Keras Proficiency
Can develop models using Keras APIs and leverage TensorFlow for production pipelines and SavedModel format.
How to build it: TensorFlow certification prep, Keras documentation
- essential
Model Deployment (MLOps)
Can containerize a model with Docker and deploy it as a REST API using FastAPI or Flask, or via cloud services.
How to build it: MLOps Zoomcamp, cloud provider labs (AWS SageMaker, GCP AI Platform)
- important
Experiment Tracking with MLflow
Can log parameters, metrics, and artifacts to track and compare experiments for reproducibility.
How to build it: MLflow quickstart, Databricks community tutorials
- nice-to-have
GPU Acceleration with CUDA
Understands basic CUDA concepts to leverage GPU acceleration in PyTorch/TensorFlow for faster training.
How to build it: NVIDIA DLI courses, framework guides on CUDA
- important
Hugging Face Ecosystem
Can fine-tune and deploy pre-trained transformer models for NLP or vision using the Transformers library.
How to build it: Hugging Face documentation, model hub examples
Advanced Topics & Specialization
Cutting-edge areas and specialized skills for roles like AI Researcher or Applied Scientist.
- nice-to-have
Reinforcement Learning
Can implement Q-learning or policy gradient methods (e.g., with Gymnasium) for simple control tasks.
How to build it: Spinning Up in Deep RL, 'Reinforcement Learning: An Introduction'
- important
Reading & Implementing Research Papers
Can read recent ML papers from arXiv, understand novel architectures, and replicate core results in code.
How to build it: Papers With Code, online reading groups, blog summaries
- nice-to-have
Model Compression & Optimization
Can apply techniques like quantization, pruning, or knowledge distillation to optimize models for edge deployment.
How to build it: TensorFlow Lite/PyTorch Mobile guides, research papers
- nice-to-have
Generative AI & Diffusion Models
Understands the principles behind generative models like GANs, VAEs, or Stable Diffusion for content creation.
How to build it: Generative AI courses, Hugging Face diffusion tutorials
- important
Large Language Model (LLM) Fine-tuning
Can adapt large pre-trained LLMs for specific tasks using techniques like LoRA or prompt tuning.
How to build it: Hugging Face PEFT library, LLM fine-tuning blogs
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 tool proficiency and start a specialization project. |
| Advanced | 61-85% | You're highly skilled. Target Advanced Topics and complex deployment scenarios. |
| Job ready | 86-100% | You are competitive for ML Engineer/Researcher roles. Showcase projects and master your niche. |
Next steps
Audit Your Skills
Check off every skill you're confident in. Be honest to identify precise knowledge gaps.
Plan a Capstone Project
Choose a project combining 2-3 skill areas (e.g., fine-tune a Hugging Face model and deploy it with MLflow).
Join a Community
Engage with ML communities on Discord, Reddit (r/MachineLearning), or local meetups for feedback and networking.
Schedule Regular Reviews
Revisit this checklist quarterly to track progress and adjust your learning plan based on industry trends.
Tips that make the difference
- Build a portfolio with 2-3 end-to-end projects (data to deployed API) rather than many tutorials.
- Contribute to open-source ML projects on GitHub; it's a powerful signal to employers.
- Stay current by following key researchers on X/Twitter and reading top conference papers (NeurIPS, ICML, CVPR).
- Practice explaining complex ML concepts simply; this is critical for interviews and collaboration.
- Automate your workflow early: use scripts for data prep, training, and evaluation to save time.
Track Your Journey to an ML Career
Use Edirae's skill tracker to monitor your progress, set goals, and get personalized learning recommendations for 2026.
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