Data Visualisation
Skills for certificate:
Deep Learning
Artificial Intelligence
Data Science
Data Visualisation
Neural Networks
Pandas
NumPy
Matplotlib
Seaborn
Data Visualisation
This is the page displaying all the material related to Data Visualisation. This can include projects, blogs, certificates, university modules and work experience along with sub-skills.
Material
Handwritten Digit Classifier
A handwritten digit classifier using built using a Convolutional Neural Network (CNN). Used various techniques such as data augmentation, batch normalisation, and dropout to improve the model's performance.
Adult Income Prediction
Comparing various classification algorithms to predict whether an adult earns more than $50,000 a year. Emphasis is on feature engineering, data preprocessing with One-Hot Encoding, and model optimization through hyperparameter tuning.
House Price Prediction
Comparing various regression algorithms to predict house prices in relation to the distance from the coast. Emphasis is on feature engineering, data preprocessing with One-Hot Encoding, and model optimization through hyperparameter tuning.
Machine Learning Algorithms
Practicing various algorithms and techniques. This includes supervised, unsupervised, and reinforcement learning algorithms, as well as feature engineering, data preprocessing, and hyperparameter tuning.
Reinforcement Learning Lab
Practicing various Reinforcement Learning algorithms and techniques. This includes Q-Learning, Deep Q-Learning, and Asynchronous Advantage Actor-Critic (A3C) algorithms.
Machine Learning Assignment 1
Implementing algorithms from scratch such as the Nearest Neighbours algorithm. Requires an understanding of the Mathematics behind the algorithms and the ability to implement them.
Machine Learning Assignment 2
Be able to use and implement algorithms, with the Lasso and inductive conformal prediction algorithms as examples.
Machine Learning Assignment 3
Be able to use and implement algorithms, with the SVM, neural networks, and cross-conformal prediction algorithms as examples.
Machine Learning Labs
Implemented various algorithms and techniques learnt during the course, such as Nearest Neighbours, Conformal Prediction, Regression algorithms, data preprocessing, kernels, Neural Networks, SVMs, etc.
Computational Finance Assignment
Exploring valuation of options using methods like Black-Scholes, binomial trees, and Monte Carlo. Also includes theoretical aspects of put-call parity and financial arbitrage opportunities.
Machine Learning & Data Science Lab
Focusing on learning generative models, using third-party models and using advanced techniques. This includes techniques such as transfer learning, LLM Agents, and Generative Models.