Skill Qualification
Artificial Intelligence
Highlights:
Image Captioning (Computer Vision)
Instance Segmentation (Computer Vision)
Automatic Trading Bot (Reinforcement Learning)
Explainable AI
Dec 2019
Computer Cognitive System
City University of London
London Campus
Analyse cognitive processes by gaining specific knowledge and skills in modelling perception, attention and memory, learning, and decision-making
Dec 2019
Agents and Multi-agent Systems
City University of London
London Campus
Agent architectures that perform differently according to the characteristics of the task environment, e.g., whether the “world” in which the agent is situated is dynamic or static, deterministic or stochastic, discrete or continuous.
May 2020
Deep Learning: Classification
City University of London
Study Deep Learning techniques for solving classification, detection, segmentation and image generation problems.
London Campus
May 2020
Deep Learning: Prediction
City University of London
London Campus
Deep Learning techniques for solving sequence prediction problems. Particularly, the main focus in this module is directed to the study of Language Models and Recurrent Neural Networks.
May 2020
Deep Learning: Optimization
City University of London
London Campus
Solve optimization problems using Deep Learning techniques. An important part of this module is to learn Reinforcement Learning techniques starting from the mathematical foundations, including Markov Decision Processes and Dynamic Programming.
May 2020
Explainable AI
City University of London
London Campus
Learn about the problems inherent to opaque AI systems, as well as become aware of the importance of interpretability and/or explainability as AI/ML system properties when deploying AI/ML systems in the real world.
Deep Learning
Highlights:
Convolutional Neural Network (CNN)
Recurrent Neural Network (RNN)
Attention Network
LSTM/GRU
Transformer
Mar 2018
Neural Networks and Deep Learning
Deeplearning.ai
Online Course
​Familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications
Apr 2018
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Deeplearning.ai
Online Course
Learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence; and implement a neural network in TensorFlow.
Jun 2018
Convolutional Neutal Network
Deeplearning.ai
Online Course
Build a convolutional neural network, including recent variations such as residual networks; apply convolutional networks to visual detection and recognition tasks; and use neural style transfer to generate art and apply these algorithms to a variety of image, video, and other 2D or 3D data.
Jul 2018
Sequence Models
Deeplearning.ai
Online Course
Build and train Recurrent Neural Networks (RNNs) and commonly-used variants such as GRUs and LSTMs; apply RNNs to Character-level Language Modeling; gain experience with natural language processing and Word Embeddings; and use HuggingFace tokenizers and transformer models to solve different NLP tasks such as NER and Question Answering.
Jul 2018
Deep Learning
Deeplearning.ai
Online Course
Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications ; Train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow; Build a CNN and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data; Build and train RNNs, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformer models to perform NER and Question Answering
Machine Learning
Highlights:
Multivariate Regression
Random Forest
XGBoost
Naive Bayes / Gaussian Bayes Classifier
Principle Component Analysis (PCA)
Feb 2018
Machine Learning
Stanford University
Online Course
Covered to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course draw from numerous case studies and applications. Apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
Mar 2018
Structuring Machine Learning Projects
Deeplearning.ai
Online Course
Diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning.
Mar 2022
Machine Learning for Trading
Google Cloud
Online Course
Understand the structure and techniques used in machine learning, deep learning, and reinforcement learning (RL) strategies; Describe the steps required to develop and test an ML-driven trading strategy; Describe the methods used to optimize an ML-driven trading strategy; Use Keras and Tensorflow to build machine learning models.​​
Data Science
Highlights:
Time Series Analysis
Exploratory Data Analysis (EDA)
Tail Distribution Analysis
Data Visualization
Data Science Pipeline
Jan 2019
IBM Data Science Professional Certificate
IBM
Online Course
Course Certificates Completed: 1. Machine Learning with Python 2. Data Analysis with Python 3. Python for Data Science, AI & Development 4. Databases and SQL for Data Science with Python 5. Applied Data Science Capstone 6. Data Science Methodology 7. What is Data Science? 8. Tools for Data Science 9. Data Visualisation with Python
Feb 2022
Data Visualization with Tableau
University of California, Davis
Online Course
Examine, navigate, and learn to use the various features of Tableau; Assess the quality of the data and perform exploratory analysis; Create and design visualizations and dashboards for your intended audience; Combine the data to and follow the best practices to present your story.
Python / SQL Programming
Highlights:
Pandas, Numpy
Scikit-learn
Pytorch
Tensorflow/Keras
Dec 2019
Programming and Mathematics for AI
City University of London
London Campus
Fundamental and advanced programming and mathematical skills for studying specialist Artificial Intelligence topics. The paradigm used is object-oriented programming, and Python the guiding programming language.
Dec 2020
SQL for Data Science
University of California, Davis
Online Course
Identify a subset of data needed from a column or set of columns and write a SQL query to limit to those results; U​se SQL commands to filter, sort, and summarize data; Create an analysis table from multiple queries using the UNION operator; Manipulate strings, dates, & numeric data using functions to integrate data from different sources into fields with the correct format for analysis.
Cloud Computing
Highlights:
Microsoft Azure
Microsoft Power Platform / Power App/ Power BI
IBM Watson Studio
May 2021
Microsoft Certificate: Power Platform Fundamentals
Microsoft Certificate
Identify a subset of data needed from a column or set of columns and write a SQL query to limit to those results; U​se SQL commands to filter, sort, and summarize data; Create an analysis table from multiple queries using the UNION operator; Manipulate strings, dates, & numeric data using functions to integrate data from different sources into fields with the correct format for analysis.
May 2021
Microsoft Azure Fundamentals
Microsoft Certificate
Describe Cloud Concepts; Describe COre Azure Services; Describe core solutions and management tools on Azure; Describe general security and network security features; identity, governance, privacy, and compliance features; cost management and service level agreements
May 2021
Microsoft Azure AI Fundamentals
Microsoft Certificate
Describe AI workloads and considerations; Describe fundamental principles of machine learning on Azure; Describe features of computer vision workloads on Azure; Describe features of Natural Language Processing (NLP) workloads on Azure; Describe features of conversational AI workloads on Azure
May 2021
Microsoft Azure Data Fundamentals
Microsoft Certificate
Describe core data concepts; Describe how to work with relational data on Azure; Describe how to work with non-relational data on Azure; Describe an analytics workload on Azure