The Best Free Tools & Platforms to Practise AI Skills in 2025/26
Artificial Intelligence (AI) is one of the fastest-growing career fields in the UK and worldwide. Whether you are a student exploring AI for the first time, a graduate looking to build your portfolio, or an experienced professional upskilling for career growth, having access to free tools and platforms to practise AI skills can make a huge difference.
In this comprehensive guide, we’ll explore the best free resources available in 2025, covering AI coding platforms, datasets, cloud tools, no-code AI platforms, online communities, and learning hubs. These tools allow you to practise everything from machine learning models and natural language processing (NLP) to computer vision, reinforcement learning, and large language model (LLM) fine-tuning—without needing a huge budget.
By the end of this article, you’ll have a clear roadmap of where to start practising your AI skills for free, how to build real-world projects, and which platforms can help you land your next AI job.
Why Practising AI Skills Matters
AI is not a subject you can master by reading theory alone. Employers increasingly look for candidates who can demonstrate hands-on experience—whether through GitHub projects, Kaggle competitions, or applied case studies.
By using free AI tools and platforms, you can:
Experiment safely: Learn by trial and error without financial risk.
Build a portfolio: Showcase your projects on GitHub or LinkedIn.
Stay current: Work with the latest tools, frameworks, and datasets used in industry.
Prepare for interviews: Many technical interviews include problem-solving or coding challenges similar to what you’ll find on these platforms.
1. Google Colab – Free Cloud-Based AI Coding
One of the most popular and accessible tools for practising AI is Google Colab.
Key Features
Free GPU & TPU access: Run deep learning models without expensive hardware.
Python & Jupyter notebooks: Ideal for experimenting with TensorFlow, PyTorch, and Scikit-learn.
Collaboration friendly: Share notebooks with peers, similar to Google Docs.
Integrates with GitHub: Import existing projects directly into Colab.
Why It’s Great for Beginners
Colab is the perfect starting point for those who don’t have a powerful laptop. You can run neural networks, train models, and even fine-tune large models using cloud GPUs for free.
SEO Tip: If you’re searching for “best free AI coding platforms 2025” or “how to practise machine learning without GPU,” Colab is the top answer.
2. Kaggle – Datasets, Competitions & Learning
Kaggle is a household name in data science and AI, known for its competitions, community, and vast dataset library.
Key Features
Free datasets: Millions of public datasets to experiment with.
Kaggle notebooks: Cloud-based Jupyter notebooks with free GPU access.
Competitions: Tackle real-world AI problems and compete against others globally.
Kaggle Learn courses: Short, practical tutorials covering ML, NLP, deep learning, and data visualisation.
Why It’s Great for Portfolio Building
Winning or even participating in Kaggle competitions looks great on a CV. Employers recognise Kaggle experience as proof of problem-solving and collaboration skills.
3. Hugging Face – Playground for NLP & LLMs
For anyone interested in natural language processing or large language models, Hugging Face is an essential platform.
Key Features
Model Hub: Thousands of pre-trained models, including BERT, GPT, and stable diffusion.
Datasets hub: Access to open NLP and multimodal datasets.
Spaces: Deploy AI apps for free using Gradio or Streamlit.
Transformers library: A Python framework for cutting-edge NLP.
Why It’s Great for NLP Enthusiasts
Hugging Face lets you try out powerful models in just a few lines of code. You can even host AI apps for free, making it ideal for demonstrating projects to potential employers.
4. GitHub Copilot Free Student Plan
If you’re a student, GitHub Copilot offers free access through the GitHub Student Developer Pack.
Key Features
AI-powered coding assistant: Helps generate Python, R, and JavaScript code.
Project collaboration: Perfect for open-source AI contributions.
Version control: Keep track of your AI experiments.
Why It’s Great for Practice
Practising AI often involves lots of repetitive coding. Copilot speeds this up, allowing you to focus on problem-solving and model design.
5. Google AI & TensorFlow Playground
If you’re more visually inclined, the TensorFlow Playground is a great tool.
Key Features
Interactive neural network visualisation: Experiment with layers, activations, and learning rates.
Beginner-friendly: Understand how model changes affect performance in real time.
No coding required: Explore AI concepts without programming.
Why It’s Great for Beginners
This tool is excellent for building intuition about how neural networks work before diving into Python coding.
6. Microsoft Azure AI Free Tier
For cloud-based AI experimentation, Microsoft Azure offers a free tier with credits for new users.
Key Features
Azure Machine Learning Studio: Drag-and-drop ML workflows.
Free credits: Train models, deploy apps, and explore cloud AI.
Integration with Python SDKs: Scale projects when you’re ready.
Why It’s Great for Cloud Skills
Many employers use Azure, AWS, or GCP for AI workloads. Practising on free tiers helps you gain practical cloud experience for your CV.
7. OpenAI Playground
OpenAI provides an online Playground where you can interact with models like GPT-4.
Key Features
Text generation & NLP tasks: Practise prompt engineering.
Custom fine-tuning (limited free credits): Explore tailoring LLMs.
Real-time experimentation: Adjust parameters and see output instantly.
Why It’s Great for Job Seekers
Prompt engineering is now a valued skill in many AI job descriptions. The Playground is a free, practical way to hone this skill.
8. Fast.ai – Free Practical Deep Learning Courses
Fast.ai is a non-profit organisation offering completely free AI courses.
Key Features
Hands-on approach: Learn by coding models from day one.
Based on PyTorch: One of the most widely used deep learning frameworks.
Community-driven: Forums, study groups, and open-source projects.
Why It’s Great for Learners
Fast.ai makes deep learning accessible, even if you’re not a maths expert. Their approach focuses on application, not just theory.
9. Deepnote – Collaborative AI Notebook Platform
Deepnote is another cloud-based Jupyter notebook platform designed for collaboration.
Key Features
Free tier with unlimited projects.
Real-time collaboration: Similar to Google Docs for AI notebooks.
Integrates with SQL & Python: Great for data + AI projects.
Why It’s Great for Team Projects
If you want to practise AI as part of a team or study group, Deepnote makes sharing and editing seamless.
10. Papers With Code – Learn from Research
For those interested in the research side of AI, Papers With Code is invaluable.
Key Features
Latest AI research papers.
Code implementations on GitHub.
Benchmarks for comparison.
Why It’s Great for Advanced Practitioners
Instead of just reading academic papers, you can run the actual code and experiment with cutting-edge models for free.
11. AI Dungeon & No-Code AI Platforms
Not everyone wants to code from scratch. No-code AI platforms allow you to explore AI concepts without heavy programming.
Recommended Free No-Code Platforms
Teachable Machine (Google): Train image/audio/text models in minutes.
RunwayML (free tier): Create generative art & AI video projects.
Lobe (Microsoft): Drag-and-drop image recognition models.
Why They’re Useful
No-code tools help non-technical learners experiment with AI and still produce portfolio-worthy projects.
12. Open-Source AI Frameworks
Learning frameworks is essential for AI career growth. Luckily, most are open source and free to use.
Top Frameworks to Practise On
PyTorch – Industry standard for deep learning.
TensorFlow – Widely used in production AI.
Scikit-learn – Ideal for beginners in machine learning.
JAX – Popular for research and high-performance ML.
Why They’re Essential
By mastering one or more frameworks, you can confidently tackle real-world AI job tasks.
13. Free Datasets for AI Projects
AI models are only as good as the data they’re trained on. Thankfully, there are thousands of free datasets online.
Popular Free Dataset Sources
UCI Machine Learning Repository – Classic datasets for beginners.
ImageNet – Huge dataset for computer vision.
COCO Dataset – Widely used for object detection.
OpenML – Collaborative platform for sharing ML datasets.
Gov.uk Open Data – Free UK datasets for applied AI projects.
Why They’re Useful
Working with real data gives your projects credibility and prepares you for job interviews where dataset handling is tested.
14. AI Communities & Forums
Practising AI doesn’t mean going it alone. Free AI communities help you learn faster, share projects, and find hidden opportunities.
Recommended Communities
Reddit (r/MachineLearning, r/Artificial): Active discussions & resources.
LinkedIn groups: Great for networking with UK-based AI professionals.
Discord servers (Kaggle, Fast.ai, Hugging Face): Real-time collaboration.
AI Slack groups: Many offer open invites for learners.
Why Communities Matter
Being active in AI communities helps you stay updated, find collaborators, and even connect with recruiters.
15. YouTube & Free MOOCs
Lastly, don’t underestimate free video content.
Recommended Channels & Platforms
YouTube (3Blue1Brown, Sentdex, Krish Naik): Clear explanations & tutorials.
Coursera free courses (audit mode): Machine learning by Andrew Ng.
edX free courses: Intro to AI from top universities.
Why They Work
Combining video learning with hands-on practice creates the best learning loop: watch, code, test, repeat.
How to Use These Tools Effectively
Having free tools is only the first step. To truly benefit, you need a plan:
Pick one focus area – NLP, computer vision, reinforcement learning, or generative AI.
Choose two main platforms – e.g., Colab + Kaggle, or Hugging Face + GitHub.
Work on mini-projects – Build models on real datasets.
Document your journey – Share projects on GitHub & LinkedIn.
Join competitions or communities – Get feedback and improve.
Final Thoughts
The best way to learn AI is by doing, and thanks to these free tools and platforms, you don’t need an expensive setup to start practising. Whether you prefer coding from scratch in Google Colab, exploring pre-trained NLP models on Hugging Face, or building AI apps without code using Teachable Machine, there’s something for everyone.
The key is consistency. Set aside regular practice time, build a portfolio of small but meaningful projects, and engage with AI communities. Over time, these free resources will help you develop the skills employers are looking for in AI engineers, data scientists, and machine learning specialists.
If you’re serious about an AI career in the UK, start today with one or two of these tools. By experimenting, building, and sharing your work, you’ll not only boost your skills but also increase your visibility in the competitive AI job market.
