Machine Learning Skills: What to Learn and How to Get Them
Big claim: top ML hires can build a working model in days. You can too if you pick the right skills and practice smart. This page shows what skills matter, how to learn them fast, and which projects prove you know your stuff.
Core technical skills
Python is non-negotiable. Learn lists, dicts, functions, classes, and how to read library docs. Next, get comfortable with NumPy and pandas for data handling—most ML work is cleaning and reshaping data, not math theory. Study linear algebra basics (vectors, matrices), probability, and calculus for gradients; you only need practical intuition, not full proofs.
Learn model building with scikit-learn: regression, classification, cross-validation, pipelines. For deep learning pick one framework—TensorFlow or PyTorch—and build a feedforward network, then try CNNs for images and RNNs or transformers for sequences. Practice model evaluation: confusion matrix, precision/recall, ROC, and proper train/validation/test splits to avoid overfitting.
Data and engineering skills
Data cleaning beats fancy models. Master missing value strategies, feature engineering, and simple scaling. Learn SQL to pull data and basic command-line tools to handle big files. Learn how to version code and models with Git and experiment tracking (a CSV or MLflow). Knowing how to package a model into a REST API or a small Flask app makes you far more hireable.
Learn to read a dataset and ask key questions fast: who generated it, what bias exists, what the target really means. Explainability matters—use SHAP or LIME for feature importance and be ready to describe model limits to non-technical people.
How to practice and show results
Build three concrete projects: a cleaned tabular model with feature notes, an image classifier with clear augmentation choices, and an end-to-end app that serves a model. Keep each project short, documented, and on GitHub with a README showing data origin, preprocessing steps, metric results, and how to run the code.
Use Kaggle or small real datasets; public leaderboards teach trade-offs. But don’t chase top scores—focus on reproducible work you can explain in an interview. Share notebooks, short blog posts, or a 2-minute demo video to make your work easy to evaluate.
Start with a 90-day plan: 2 weeks of Python basics, 2 weeks of NumPy/pandas and SQL, 2 weeks of ML fundamentals, 4 weeks of a deep learning framework and one guided project, then use the remaining time to build and polish three portfolio projects. Pair study with weekly goals and review your code with someone else.
Resources: follow scikit-learn docs, fast.ai deep learning course, Coursera’s ML and deep learning tracks, and freeCodeCamp tutorials for Python. Read short blog posts that explain real trade-offs—practical experience beats theory for early hires. Keep projects focused, and show the work clearly on GitHub or a personal site.
Want a quick checklist? Python, NumPy/pandas, scikit-learn, one deep learning framework, SQL, Git, deployed app, three documented projects, and a 90-day practice plan. Do that and you’ll have Machine Learning skills employers can actually use soon and confidently.
