Beginner Machine Learning In-Person Classes
Budget: €2 – €6 EUR
I need an experienced instructor to run a beginner-friendly machine-learning course delivered face to face for a small group of 1–5 learners. The goal is to take complete newcomers from “what is a model?” all the way to training and evaluating simple classifiers and regressors in Python, using tools such as NumPy, pandas, scikit-learn and Jupyter Notebook.
What I expect
• A concise yet structured syllabus covering core ML concepts (supervised vs. unsupervised learning, data preparation, model training, evaluation and basic hyper-parameter tuning).
• Slide deck or equivalent teaching material I can reuse.
• Live coding sessions with hands-on exercises and sample datasets.
• Short homework or mini-projects so students can practise between meetings.
• All source files, notebooks and reference notes handed over at the end.
Logistics
We are open to meeting at our office or a mutually convenient location in [city to be confirmed]. Sessions can be concentrated over a weekend boot-camp or spread across weekly evening classes, so let me know your preferred schedule.
Acceptance criteria
The learners should leave able to:
1. Load and clean a dataset in pandas.
2. Split data, train at least one scikit-learn model and interpret key metrics.
3. Explain, at a high level, how common algorithms (k-NN, decision trees, logistic regression) work.
Please outline your teaching experience, proposed timetable and any example course materials when you bid.
What I expect
• A concise yet structured syllabus covering core ML concepts (supervised vs. unsupervised learning, data preparation, model training, evaluation and basic hyper-parameter tuning).
• Slide deck or equivalent teaching material I can reuse.
• Live coding sessions with hands-on exercises and sample datasets.
• Short homework or mini-projects so students can practise between meetings.
• All source files, notebooks and reference notes handed over at the end.
Logistics
We are open to meeting at our office or a mutually convenient location in [city to be confirmed]. Sessions can be concentrated over a weekend boot-camp or spread across weekly evening classes, so let me know your preferred schedule.
Acceptance criteria
The learners should leave able to:
1. Load and clean a dataset in pandas.
2. Split data, train at least one scikit-learn model and interpret key metrics.
3. Explain, at a high level, how common algorithms (k-NN, decision trees, logistic regression) work.
Please outline your teaching experience, proposed timetable and any example course materials when you bid.
Related categories:
Python
Training
Statistics
Machine Learning (ML)
Data Science
NumPy
Data Analysis