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  "job_description": "Trainee AI Engineer - No Experience Needed Future-proof your career in Artificial Intelligence - starting today.\nOr maybe you're between jobs and ready for a fresh start? ITOL Recruit's AI Traineeship is designed to get you into one of the fastest-growing industries with zero experience required.\nTrain online at your own pace and land your first AI Engineer role in 1-3 months.\nPlease note this is a training course and fees apply\nWhy AI?\nAI is reshaping every industry you can think of. The demand far outstrips supply, which means excellent salaries, flexible working arrangements, and genuine job security.\n\nHow It Works\nStep 1 - AI Engineering Fundamentals\nStart with the basics of AI, including neural networks and large language models, to build a solid foundation in AI engineering.\nStep 2 - Data Fundamentals\nUnderstand the data workflow, from collection to cleaning, and learn how to prepare data for AI applications.\nGet hands-on with industry-standard tools like Jupyter Notebooks and VS Code to develop AI systems.\nStep 4 - Python Programming\nMaster Python, covering everything from the basics to object-oriented programming (OOP).\nStep 5 - Python Streamlit Project\nApply your Python skills by building a car price prediction app using Python and Streamlit.\nStep 6 - Python for Data\nLearn essential Python libraries like NumPy, Pandas, and Matplotlib for data manipulation and visualisation.\nStep 7 - AI Sentiment Analysis Project\nWork with Hugging Face to build a sentiment analysis classifier using real-world AI techniques.\nStep 8 - AI Prompt Engineering\nMaster prompt engineering, learning how to craft effective prompts for controlling AI outputs.\nLearn how to integrate external knowledge into AI systems using RAG techniques and vector databases.\nStep 10 - AI Specialised Customer Service Chatbot Project\nCombine prompt engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases.\nStep 11 - Machine Learning Fundamentals\nUnderstand machine learning principles and algorithms, and how to train and test models using scikit-learn.\nStep 12 - Machine Learning Project\nPut your machine learning knowledge into practice with a hands-on project.\nStep 13 - AI & Data Ethics\nStudy the ethical considerations in AI, including issues of bias, fairness, and data privacy.\nComplete a virtual oral exam to assess your understanding and ability to apply your learning.\nStep 15 - AWS Certified Cloud Practitioner\nFinish with the AWS Certified Cloud Practitioner course and exam to gain essential cloud computing knowledge.\n\n100% online, self-paced training\n• Microsoft AI-900 certification included\n• Real-world project experience\n• Five months from complete beginner to AI engineer. now working as a Junior AI Engineer in London\n\nIf you're motivated, curious, and excited about technology, we'll help you turn that into a career you can be proud of.",
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