Machine Learning Skills Checklist for Job Seekers (2026)

Can apply concepts like distributions, hypothesis testing, and Bayesian inference to model evaluation and uncertainty quantification.

University courses, 'Introduction to Statistical Learning' book

Understands matrix operations, eigenvectors, and gradients, enabling comprehension of model internals and optimization.

Khan Academy, 3Blue1Brown YouTube series

Can implement, evaluate, and tune models like Random Forests, SVMs, and clustering algorithms for tabular data.

Scikit-learn documentation, Kaggle tutorials

Proficient in cleaning data, handling missing values, and creating informative features for model training.

Pandas/NumPy tutorials, 'Feature Engineering for ML' book

Can design robust train/test splits, use cross-validation, and interpret metrics like precision-recall and ROC-AUC.

ML courses on Coursera, practice on Kaggle competitions

Can build and train feedforward and convolutional networks from scratch, understanding backpropagation.

Deep Learning Specialization (Coursera), PyTorch/TensorFlow tutorials

Can efficiently build, train, and debug models using one primary framework (PyTorch or TensorFlow).

Official PyTorch/TensorFlow guides, fast.ai course

Can use tools like Optuna or Ray Tune to systematically optimize model performance.

Documentation for Optuna, Hyperopt, or Keras Tuner

Can implement architectures like ResNet for tasks like image classification and object detection.

CS231n (Stanford), PyTorch Vision tutorials

Can build models for text classification, named entity recognition, or sentiment analysis using embeddings.

Hugging Face Course, 'Speech and Language Processing' book

Understands and can implement or fine-tune transformer models (e.g., BERT, GPT variants) for NLP tasks.

Hugging Face Transformers library, 'The Illustrated Transformer' blog

Can implement basic algorithms like Q-Learning or policy gradients in simulated environments.

Spinning Up in Deep RL (OpenAI), 'Reinforcement Learning: An Introduction'

Can containerize a model with Docker and deploy it as an API using Flask/FastAPI or cloud services.

Docker documentation, FastAPI tutorial, AWS SageMaker/Google AI Platform

Can log experiments, parameters, metrics, and models to track and reproduce ML projects.

MLflow quickstart, Databricks community tutorials

Uses Git for code and DVC for data/model versioning to ensure project reproducibility.

DVC documentation, Git tutorials

Can set up monitoring for model performance drift and data quality in production.

Evidently AI, Prometheus/Grafana for metrics

Understands CUDA basics to leverage GPU acceleration for training and inference in frameworks.

NVIDIA CUDA toolkit guides, PyTorch CUDA documentation

Can use managed services for training, deployment, and pipeline orchestration on a major cloud platform.

Cloud provider certifications, official tutorials

Can read recent ML papers from conferences (NeurIPS, ICML) and implement key algorithms.

Papers With Code, arXiv, replication projects on GitHub

Can fine-tune and deploy state-of-the-art transformer models for complex tasks using the Hugging Face ecosystem.

Hugging Face documentation, advanced NLP courses

Understands and can experiment with generative architectures for image or text synthesis.

GAN tutorials, Stable Diffusion/DALL-E guides

Can apply techniques like quantization, pruning, or knowledge distillation for efficient deployment.

PyTorch Mobile, TensorFlow Lite, research papers on efficiency

Can assess models for fairness, explainability, and societal impact, applying mitigation strategies.

Fairlearn, IBM AI Fairness 360, relevant literature