IBM – Machine Learning with Python

A hands-on certification focused on applicative and foundational machine learning using Python. Gained practical experience with Scikit-learn, supervised and unsupervised learning, model evaluation, and hyperparameter tuning.

• Implemented core ML algorithms including regression, SVM, decision trees, and clustering.
• Applied techniques like feature engineering, cross-validation, and bias-variance management to optimize models.
• Completed a capstone project by developing and evaluating a classification model using historical rainfall data.

Applied Text Mining in Python

A hands-on certification focused on text processing, natural language techniques, and unsupervised topic modeling using Python. Explored the use of nltk, regular expressions, and ML tools to prepare and analyze textual data.

• Applied core NLP methods including tokenization, stemming, and text classification.
• Used regular expressions and Python functions to clean, structure, and transform raw text data for analysis.
• Implemented document clustering and topic modeling techniques to group and extract insights from unstructured text datasets.

Machine Learning for All

A Certification course designed to understand the core principles, social implications, and real-world applications of machine learning. Gained hands-on experience building a computer vision model using visual tools.

• Explored the fundamentals of ML including features, data impact, and supervised learning concepts.
• Completed a visual image recognition project by training a model to classify images using Goldsmiths' custom ML platform.
• Developed awareness of the societal impact, ethical concerns, and potential of AI technologies in everyday life.

Bachelor of Science

Grade: Distinction.

Higher Secondary School

Grade: Distinction.