# Machine Learning Skills Test and Verified Score > A machine learning skills test with model debugging scenarios, plus live model reviews and connected GitHub projects. See what proves ML skills. Source: https://getlemma.io/skills/machine-learning Many people have trained a model on a clean tutorial dataset. Fewer can explain why a model that scored well offline fails in use. Lemma records that judgement through live reviews, assessments and projects you connect. How machine learning is assessed ## What machine learning competence looks like A competent ML practitioner asks whether a model is needed before building one, and sets a simple baseline first. They split data so nothing leaks from the future or from duplicate records, choose a metric that matches the cost of each kind of error and look closely at where the model is wrong. They debug models methodically. When performance drops, they check the data pipeline, label quality and distribution shift before tuning hyperparameters. They can explain the bias and variance trade-off in their own model, document its limits and plan how it will be monitored once real inputs start arriving. ### Problem framing and baselines Deciding whether ML fits, defining the target and beating a simple baseline before adding complexity. ### Data splits and leakage Train, validation and test splits that respect time and groups, and spotting leakage early. ### Model evaluation Choosing metrics that match real error costs, calibration and error analysis by segment. ### Feature engineering Building informative features, handling categorical and missing data, and avoiding target leakage. ### Training and tuning Regularisation, cross-validation and tuning with a budget, without overfitting to the validation set. ### Deployment and monitoring Serving models, tracking drift and deciding when a model needs retraining or retiring. ## What counts as evidence of machine learning skills? In machine learning, a polished result can hide a shaky method. Lemma records the evidence behind the method: sessions where someone followed your reasoning, an adaptive test and the repositories you link, notebooks included. Each source is listed below. See the [evidence methodology](https://getlemma.io/methodology#evidence-types) for how each source is weighed. [Live session](https://getlemma.io/product/sessions) ### Reviewing a model that underperforms A practitioner presents a model with a hidden problem, such as leakage or a bad split. In a live session you diagnose it and explain the fix, and they rate your reasoning. [Adaptive assessment](https://getlemma.io/product/assessments) ### ML debug challenges Adaptive scenario questions cover evaluation, overfitting and data issues. At higher difficulty you fix broken training or evaluation code, and the fix and explanation are scored against a rubric. [GitHub connector](https://getlemma.io/product/passport) ### An ML project on GitHub Connect a repository with a training pipeline, evaluation and write-up. Lemma checks it exists and that your account has access, and re-checks it periodically. [Live session](https://getlemma.io/product/sessions) ### Teaching an ML concept Explaining cross-validation or evaluation metrics to another learner with a worked example shows depth. Teaching sessions count for more than learning ones in your record. ## How is a machine learning skills test run on Lemma? The machine learning test asks 15 to 25 questions and moves up or down a level with each answer, focusing on judgement calls such as leakage, skew and bad joins. It feeds your machine learning SCI together with sessions and linked work, and the score weighs how fresh that evidence is. [How the SCI score is calculated](https://getlemma.io/product/sci)[How adaptive skills assessments work](https://getlemma.io/product/assessments)[Practise machine learning in live skill swap sessions](https://getlemma.io/product/sessions)[Which evidence types count](https://getlemma.io/methodology#evidence-types)[What your Skills Passport shows](https://getlemma.io/product/passport) 1. ### Practise core concepts Work through AI tutor exercises on evaluation, regularisation and feature handling, with explanations in your own words. The tutor adapts to you. Drill results add only a small, capped amount to your score. 2. ### Review models with a practitioner In live sessions, present a model or diagnose one together. A practitioner probes your choices of split, metric and features, then rates the session as evidence for machine learning. 3. ### Pass the ML assessment Scenario questions on evaluation and data problems adjust as you answer. Higher difficulty brings code-level debugging. Failed attempts slightly lower your proof score, so preparation pays off. ## Machine learning skills: common questions ### How do I prove machine learning skills without industry experience? Build evidence that shows judgement. Diagnose and present models in live sessions with practitioners, pass the adaptive assessment with its debug challenges, and connect a project that includes honest evaluation and error analysis. That record shows how you work, which is what a hiring team is trying to learn. ### What does a machine learning skills assessment cover? Lemma's ML assessment focuses on problems that break real models: leakage, poor splits, mismatched metrics, overfitting and distribution shift. Questions are scenario-based and adapt to your level. At higher difficulty you fix broken training or evaluation code and explain what was wrong. ### Is a Kaggle-style project enough for an ML portfolio? Competition work shows you can optimise a metric on a fixed dataset. Teams also want to see problem framing, data cleaning, baselines and how you would monitor a model in use. Add those to your project write-up, connect the repository and discuss it in a session for a fuller picture. ### Do I need deep learning experience for this skill? No. The skill covers machine learning broadly, and most of it, including evaluation, splits and feature work, applies to classical models and neural networks alike. If deep learning is your focus, bring it into your sessions and projects so the evidence reflects it. ## Skills related to machine learning [Browse the skills directory](https://getlemma.io/skills) [### Python Show how you write, debug and structure Python, not just that you once finished a course. Evidence for Python](https://getlemma.io/skills/python)[### Data analysis Evidence of how you frame questions, clean data and reach conclusions a decision-maker can rely on. Evidence for data analysis](https://getlemma.io/skills/data-analysis)[### Data engineering Evidence that your pipelines are reliable, testable and recoverable, reviewed by practitioners in live sessions. Evidence for data engineering](https://getlemma.io/skills/data-engineering)[### Cloud architecture Show how you design cloud systems that scale, fail gracefully and stay affordable. Evidence for cloud architecture](https://getlemma.io/skills/cloud-architecture) ![Painting of an olive grove in soft sunlight with hills beyond](https://cdn.sanity.io/images/islu4peo/production/613eb3f93566423c9adf45246f2cf1a61437c04c-1680x1120.jpg?w=1080&fm=webp&q=68&fit=max) ## Start your machine learning evidence record. Lemma is in early access. Request an invite and we will email you when your place is ready.