Machine Learning Tutors : 1-1 Machine Learning Preparation

I am an AI and Data Science Engineer with a passion for harnessing the power of data and artificial intelligence to solve complex real-world problems. With a strong foundation in Python programming, statistics, and analysis, I specialize in developing innovative machine learning models, deploying AI systems, and extracting valuable insights from vast datasets. My expertise spans across areas such as supervised and unsupervised learning, deep learning, natural language processing, and computer vision, using tools like Python, TensorFlow, PyTorch, and scikit-learn. I am adept at building end-to-end pipelines, from data cleaning and preprocessing to model development, evaluation, and deployment in production environments. I stay abreast of cutting-edge trends such as generative AI, large language models, and reinforcement learning to design intelligent solutions tailored to diverse business needs. I prioritize collaboration and communication, working closely with cross-functional teams to translate technical solutions into actionable outcomes that drive growth and efficiency. My problem-solving mindset, combined with a passion for continuous learning, fuels my dedication to developing AI-powered applications that empower organizations, enhance user experiences, and contribute to technological innovation.

Dr Clement Twumasi is a Senior Medical Statistician at Oxford University Nuffield Department of Medicine. He previously worked at Clinical Trials Statistician at Imperial College London and held a postdoctorate in Biostatistics at the University of Oxford Statistics Department. He earned a PhD in Mathematics from Cardiff University in 2021 as a Vice Chancellor’s PhD Scholar, focusing on mathematical and stochastic models for biological systems. He completed his MPhil in Statistics from the University of Ghana & obtained a 4.0/4.0 GPA. His MPhil/master thesis was awarded the best research in mathematics in 2018 among all universities in Ghana. Dr Twumasi has outstanding academic track record within & outside the UK. He is also an academic reviewer for top Q1 high-impact Scopus journals. This includes the International Journal of Forecasting, PLOS One Journal, and PLOS Computational Biology Journal, where he reviews papers on time-series predictions using state-of-the-art time series models and machine learning techniques (with specific applications to blood supply chain) as well as works on mathematical biology/infectious disease modelling. He has a robust background in mathematics and statistics, with a wide range of mathematical modelling and computational expertise across diverse application areas.

I am Sameer Siddiqui, a quantitative researcher with five years of professional experience in Python engineering, data pipelines, and applied machine learning. Currently, I am pursuing my MSc in Quantitative Finance at the University of Glasgow, where I focus on stochastic calculus, derivatives pricing, portfolio theory, and quantitative optimisation. My background combines technical depth with financial expertise, having built and deployed 100+ real-time data pipelines, automated reporting systems, and machine learning models in Azure Kubernetes Service to improve efficiency and decision-making. I have also developed trading algorithms, backtested strategies, and conducted risk analysis, strengthening my foundation in financial engineering. With hands-on expertise across Python, SQL, and deep learning frameworks, I thrive at the intersection of technology and finance, driven by curiosity, problem-solving, and a passion for markets.

I’m a full-time private tutor with an extensive background in maths and data tech. I’ve been tutoring/taught for over twelve years, and worked in the data tech industry for six years. My academic background is from Sussex University. I completed my MMath degree with a 1st class honours in 2014 writing my dissertation on Bochner-Riesz summability and Strichartz estimates. I then completed my PhD in maths in 2019 under the Hirschfeld scholarship passing with minor corrections (2019); my thesis was titled “Topics in Geometric and Harmonic Analysis on Symmetric Spaces and Lie Groups”. I started tutoring as an A-level assistant at school helping GCSE students with maths work in the classroom. I picked it up again as a university undergraduate as a convenient way to fund my studies. I quickly realised that I had a knack for tutoring being able to get the best out of my students and consequently help them achieve their goals. Tutoring has since become my passion. From that point, I’ve had a range of tutoring positions including acting as a classroom assistant in a local school, one-on-one tutoring online and in person, leading university workshops, marking homework, being a university examiner, delivering lectures at universities, and leading technical training in industry. My PhD further enhanced my tutoring. I had regular contact with my research supervisor who had a remarkable ability to explain complex maths concepts in a simplified way unfolding the concepts as if he was telling a story. I integrated this into my tutoring technique. I constantly strive to develop this skill further and pass it onto my students to engender their enthusiasm. I entered the workforce following the completion of my PhD as a data specialist working in tech and applying maths to solve machine learning problems. An example was acting as an intermediary translating academic mathematical models into Python and PySpark code to improve the computation of aggregate functions. I continued teaching responsibilities being responsible for training work colleagues in data engineering skills and advanced machine learning. I’m now a full time tutor after spending six years in data tech. I have expanded my tutoring portfolio into further areas including: computer science, data engineering/science best practices, dissertation advice, maths entrance exams, mechanics, university application assistance, Python/SQL pair programming, statistics, and test preparation.