Wahidul Alam Riyad

Machine Learning Engineer and Data Scientist with experience developing and deploying sophisticated AI models.

Summary

Proficient in Python, Scikit-Learn, TensorFlow, and a range of essential ML libraries, with hands-on expertise in data preprocessing, feature engineering, and model evaluation. Proven track record of delivering high-impact projects, such as achieving 89% accuracy in predicting coronary heart disease using advanced classification models and surpassing state-of-the-art performance in food image recognition with TensorFlow. Adept at collaborative work and problem-solving.

Key Achievements:

  • Accomplished 21 professional certifications from the University of Oxford, IBM, Microsoft, DataCamp, and ZeroToMastery, with a 100% score.

  • Developed over 21 high-level projects regarding artificial intelligence, machine learning, deep learning, computer vision, data science, optimisation, and intelligent systems development.

  • Published a Q3 tier Scopus index journal by the Journal of Advanced Research in Dynamical and Control Systems. Presented research works at the 3rd Global Conference of Computing and Media Technology.

Programming Languages:

Machine Learning Stack:

Software Engineering Stack:

Python, SQL, LaTex

TensorFlow, Keras, Scikit-Learn, Pandas, NumPy, Matplotlib, SpaCy

AWS, Docker, Git, GitHub, Bash

Skills

Education

Experience

Publications

Certifications

MSc in Data Science
University of Greenwich

Thesis: Comparison of numerous classification models to predict coronary heart disease.

BSc (Hons) in Intelligent Systems
Staffordshire University

Sep 2021 - Nov 2022
London, UK

Nov 2018 - Mar 2021
Kuala Lumpur, MY

Thesis: Smart analysis system for juvenile safety against gun violence using facial recognition technology.

Data Scientist
University of Greenwich

Mar 2023 - Present
London, UK

May 2022 - Sep 2022
London, UK

  • Conducted research and developed a system under Dr Jixin Ma on the comparison of numerous classification models to predict coronary heart disease using past medical data from the UCI Machine Learning Repository.

  • Executed the project using tools such as Pandas, NumPy, Matplotlib, Seaborn, and Scikit-Learn, and evaluated the classification models beyond accuracy using ROC Curve and AUC Score, Confusion Matrix, and Classification Report.

  • Achievement - Implemented supervised machine learning algorithms such as Logistic Regression, K-Nearest Neighbour and Random Forest to conduct the research and achieved an accuracy of 89% by hyperparameter tuning and cross-validation.

Jan 2022 - Apr 2022
London, UK

  • Conducted research and developed a system under Dr Chris Walshaw by implementing numerous data visualisation techniques to showcase the customers’ visits to the store using the ChrisCo Company dataset.

  • Executed the project using tools such as HoloViews, Pandas, NumPy, Matplotlib, and Seaborn, and applied Exploratory Data Analysis to find crucial patterns of customers’ visits to the ChrisCo company by categorising them into high, medium and low.

  • Achievement - Implemented Line Plot, Bar Chart, Scatter Plot, Heatmap Plot, Pearson Coefficient Scatter Plot, Seasonality Graph, Line Plot Interactive and Heatmap Interactive Plot to visualise all the insightful data of the customers’ visits.

Jan 2022 - Apr 2022
London, UK

  • Conducted research and developed a system under Dr Stef Garasto on the comparison of numerous machine learning models to solve a multi-task classification problem using an existing dataset for the Amazon Product Reviews.

  • Executed the project using modules such as NLTK, TensorFlow, Pandas, NumPy, Matplotlib, Seaborn, and Scikit-Learn, and evaluated the classification models to predict the ratings of the Amazon Reviews and Product Categories.

  • Achievement - Implemented supervised machine learning techniques such as Logistic Regression, Support Vector Machine, Naive Bayes and Random Forest to conduct this research and achieved an accuracy of 63% using TFIDF Vectorisation.

Jun 2020 - Oct 2020
Kuala Lumpur, MY

  • Conducted research and developed a system under Dr Booma Poolan Marikannan on provisional analysis for obesity issues using numerous data mining techniques by using a past medical dataset from the Kaggle.

  • Executed the project using tools such as PyCaret, Pandas, NumPy, Matplotlib, Seaborn, Scikit-Learn, and Pickle, and evaluated the classification models to classify obesity based on the value of BMI by using Classification Report and Confusion Matrix.

  • Achievement - Implemented supervised machine learning techniques, such as Quadratic Discriminant Analysis, K-Nearest Neighbour, and Random Forest, with an accuracy of 91% to forecast customers' profitability based on consumer products.

Feb 2020 - Oct 2020
Kuala Lumpur, MY

  • Conducted research and developed a system under Dr Hamam Mokayed on an intelligent analysis system for juvenile safety against gun violence using facial recognition technology by utilising real-time data from the users on the premises.

  • Executed the project using modules such as OpenCV, TensorFlow, Keras, Pandas, NumPy, Matplotlib, Seaborn, MySQL, Django, and Scikit-Learn, and evaluated the classification models to identify the face analysis of several subjects at a particular time.

  • Achievement - Implemented supervised machine learning algorithms to identify the potential high-risk individuals with high accuracy of 99%, track their location in real-time, and add citizens, authorities and most wanted criminals to the database.

Technical Operations
Setel

Sep 2019 - Nov 2019
Kuala Lumpur, MY

  • Collaborated with teams from Software, Hardware, and System Engineering to troubleshoot critical software production issues. Assisted the Customer Operations team in providing technical guidance and troubleshooting operational issues.

  • Created training documentation and served as a mentor to other team members. Improved overall application performance and availability by driving the development of automation tools to reduce failures and manual task execution.

  • Resolved critical incidents and documented specific incident resolution techniques to enable proactive system management. Improved the GPS Accuracy of the Setel application to distinguish between Petrol Stations.

Artificial Intelligence Methods
Asia Pacific University

Nov 2018 - Mar 2019
Kuala Lumpur, MY

  • Conducted research and developed five systems under Dr Rajermani Thinakaran on the comparative evaluation of numerous optimisation algorithms for compiling travel salesman problems using real-time geographical city positions.

  • Executed the project using tools such as HashMap, ArrayList, IOException, Executors, and ExecutorService, and evaluated the models to identify the features of the algorithms and generate the best result for solving the Travel Salesman Problem.

  • Achievement - Implemented optimisation algorithms such as Ant Colony Optimisation, Hill Climbing, Nearest Neighbour, Recursive Brute Force, and Simulated Annealing to generate the shortest route of 6047 miles in 1.2 seconds.

  • Developed and deployed machine learning models using TensorFlow, Keras, and Scikit-Learn to enhance predictive analytics for recommendation systems, classification tasks, and clustering, boosting user engagement and efficiency.

  • Built and optimised data pipelines with SQL, Pandas, and NumPy to manage large datasets, ensuring efficient data preprocessing, transformation, and integration, leading to faster model training and deployment.

  • Implemented scalable machine learning solutions on AWS, using Docker for containerisation and automated CI/CD pipelines with Git and GitHub to streamline deployment, optimise resource allocation, and reduce costs.

  • Developed NLP models with SpaCy and other tools to improve text classification and sentiment analysis, providing deeper insights from unstructured data and enhancing data-driven decision-making across applications.

  • Created interactive data visualisations and dashboards with Matplotlib to clearly present complex insights and model performance, aiding stakeholders' understanding and driving more informed strategic decisions.

Data Science
University of Greenwich

Data Visualisation
University of Greenwich

Applied Machine Learning
University of Greenwich

Knowledge Discovery & Big Data Analytics
Asia Pacific University

Intelligent Systems
Asia Pacific University

Comparative Evaluation of Numerous Optimisation Algorithms for Compiling Travel Salesman Problem
Institute of Advanced Scientific Research, Inc.

TensorFlow Developer, Oxford Machine Learning Summer School, IBM Data Science Professional

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