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AI and Machine Learning Internship

8 short weeks. Free. Start any date.

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By Wartens Ltd (UK), delivered through EDWartens.

Three nested boxes: deep learning inside machine learning inside artificial intelligence.
  • Free
  • Online
  • Any start date
  • 1 to 8 weeks

Who it suits

Made for beginners.

You do not need to have built a model. Basic school maths and a little curiosity are enough, and you learn the workflow first, then the code, in small steps.
  • Students of computer science, IT, electronics and data science.
  • Anyone who wants to understand how a model learns from data, and how to tell whether it is any good.
  • Read the notes on a phone, a tablet or a laptop.
A notebook with a text cell, a code cell with a play button and the output below it.

What you will learn

Eight weeks, one picture each

Every week is open from day one. Each lesson takes a few minutes.
  1. Three nested boxes: deep learning inside machine learning inside artificial intelligence.

    Week 1

    What AI and ML are, and the ML workflow

    Tell AI, machine learning and deep learning apart, name the three kinds of learning and list the six steps of an ML project.

  2. A notebook with a text cell, a code cell with a play button and the output below it.

    Week 2

    Python and notebooks for ML

    Run code in a Colab notebook, use basic Python types, build a pandas table and take a first look at a dataset.

  3. A small table with three feature columns and one label column, each row marked as one example.

    Week 3

    Data preparation and features

    Fix missing values and outliers, turn categories into numbers, scale a feature and split the data without leaking the answer.

  4. Two boxes: regression predicts a number, classification predicts a class, each with an example.

    Week 4

    Supervised learning: regression and classification

    Tell a regression task from a classification task, fit both with scikit-learn and read what the model says.

  5. One dataset of 1000 rows split into 800 training rows used to fit the model and 200 test rows kept back to score it.

    Week 5

    Evaluating models

    Split data into train and test sets, read a confusion matrix, work out accuracy, precision and recall, and spot overfitting.

  6. A small decision tree for a motor: if temperature is above 80 degrees ask whether vibration is high, otherwise predict healthy.

    Week 6

    Trees, ensembles and clustering

    Explain how a decision tree and a random forest decide, run a tree and k-means in Colab, and say what clustering can and cannot tell you.

  7. One neuron: inputs 2 and 3 with weights 0.5 and 0.4 and a bias of 0.3 give 2.5, and a sigmoid turns it into 0.92.

    Week 7

    Neural networks and deep learning basics

    Explain a neuron, layers, loss and training, read a loss curve for overfitting and train a tiny neural network in Colab.

  8. Train and test, save the model, serve it in a small web service, and an app sends a request and gets a reply.

    Week 8

    Deploying and monitoring a model, and responsible AI

    Explain how a model is saved and served, what to monitor once it is live, the basics of responsible AI, and you can write a one-page model card for your own mini project.

The shape of one input feature in live data has shifted away from the training data, which should trigger a review or retrain.

Practice, if you like

Try it in a free notebook.

Each week ends with a worked exercise you can run in a free notebook, with the answer or a good result given. Skip them if you prefer.
  • Free tools: Google Colab, Kaggle notebooks and scikit-learn.
  • Small datasets, so every exercise runs quickly.
  • A mini project in week 8: deploy and monitor a small model.
  • Best on a laptop or tablet. A phone works for reading.
A small table with three feature columns and one label column, each row marked as one example.
One dataset of 1000 rows split into 800 training rows used to fit the model and 200 test rows kept back to score it.

Your pace

Choose a start date and a length.

We show your weekly plan. You can change it later.

Any length from 1 to 8 weeks. Start on any date from today. Retry the weekly checks and the final quiz as often as you like.

Plan my internship
Two boxes: regression predicts a number, classification predicts a class, each with an example.

Certificate and documents

Want proof? Add it later.

Free learners keep full access. A verifiable certificate of completion is an optional paid document, issued by Wartens Ltd (UK). Anyone can check it with the QR code or number.
One neuron: inputs 2 and 3 with weights 0.5 and 0.4 and a bias of 0.3 give 2.5, and a sigmoid turns it into 0.92.
How verification works

Questions

Quick answers

Is the AI and Machine Learning Internship really free?

Yes. Joining is free and so are all eight weeks, the weekly checks and the final quiz. You pay only if you choose an optional add-on.

How long can I take?

This internship runs for 1 to 8 weeks. You pick a start date and can change your plan later. All eight weeks are open from day one, so a short plan moves fast and a long one gives you room.

What do I need?

A laptop or tablet to run a notebook, a free Google account for Colab, and a free AI assistant to ask questions of. Every tool used is free or has a free tier, so there is nothing to buy. The notes also read well on a phone.

What will I actually do?

You read short notes with diagrams, watch a few videos and take an easy check each week. Each week ends with a worked exercise, such as cleaning a small table with pandas, training a first classifier or reading a confusion matrix. You can run them in a free notebook and compare with the result given.

What do I get at the end?

A certificate of completion is an optional paid document, issued by Wartens Ltd (UK). It has a QR code and a number, so anyone can verify it at edwartens.com/verification. The lessons stay free, and you can finish without buying anything.

Is this a paid position?

No. It is training, not employment. Nothing is paid to you, and nothing here promises work with any company.

Who wrote the course?

OFORO, the Wartens AI company, wrote the notes and exercises. OFORO builds the AI behind Wartens' engineering and training work. The internship is run by Wartens Ltd (UK) and delivered through EDWartens.

What about privacy and wrong answers?

AI and machine learning make mistakes, and the course says so: a model can look accurate and still fail on new data, and it can repeat the bias in its data. You learn to test, to check outputs and to keep personal data out of public tools. Never paste personal, client or confidential data into a public chatbot or a shared notebook.

Your first lesson is free.

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  • No card to start
  • Start any date
  • Verify with a QR code
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