jesseallardice/handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

Hi there, I'm Jesse

I'm a Machine Learning Engineer with a background in Maths and Physics

• I'm currently focused on NLP but am interested in multi-modal learning.

Bio

I am a machine learning engineer with a strong academic background in Physics and a PhD from the University of Cambridge; my research involved building analytical models of quantum phenomena for efficient solar-energy generation. As a result of the novel research, I developed patents for next-generation electronics which I helped put into production with a commercial partner, resulting in a multi-million dollar company valuation.

Whilst at Cambridge, I participated in the highly selective Schmidt Data Science Residency; the programme featured hands-on training, led by industry experts, with a focus on applying the latest in cutting-edge AI tools and libraries to scientific endeavours. Subsequently, I provided data science training to upskill analysts at FTSE 100 companies with residency coordinators at Cambridge Spark and founded a start-up that leveraged deep learning techniques to infer exercise-actions purely from a webcam.

To further my experience in applying machine learning methods to real-world data, I completed a Faculty.ai fellowship project with Upside Saving, a fintech start-up on a mission to help people save money. I implemented natural language processing and deep learning techniques to translate the language of “open banking” into more structured, usable data. In addition to setting the groundwork for future data science projects at Upside, my work unlocked access to a new data insights market, valued at £1Bn globally.

Most recently, I was accepted into the very first AI residency program hosted at Apple, where I was embedded in the Siri natural language understanding team. During this time, I gained extensive experience developing and deploying machine learning software in a production environment. My research focused on utilising fine-tuned large language models for natural language generation and data augmentation.

“Artificial intelligence is revolutionising how humans and machines interact. Through my research I have been able to build technologies with the aim of benefitting us all.”

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