Certificates

These are extra courses that I have done in my spare time outside of education or work. This list can also be found in the "Licenses and Certifications" section of my LinkedIn profile.

Databases and SQL for Data Science with Python (Coursera)

Key Skills:
MySQL
Creating tables and relational databases with DDL commands. Constructing complex SQL queries using DML commands.
Use views, joins, transactions and stored procedures to efficiently manage databases.

LLMs in production (Activeloop)

Key Skills:
Required a solid foundation in how tokens and LLMs work.
Setup of the dockerfile and the notebooks I used with VScode for the course can be found here.
Extensive use of LangChain and the OpenAI API.
Use of other APIs such as Selenium, Google Search, SerpAPI. I modified a dockerfile
Use of input parsers for efficient prompt formatting.
Use of output parsers for code generation and output testing.
Use of multiple agents associated to a variety of APIs.
Use of a data lake dataset (DeepLake) for similarity comparisons using word embeddings.

Deep Learning Specialization (Deeplearning.ai)

This specialisation is split into 5 different sections:

Neural Networks and Deep Learning.

Key Skills:
Creating tables and searching tables
Using a relational database with multiple tables

Hyperparameter tuning, regularization and optimization.

Key Skills:
Concepts such as batching, batch normalization, dropout and L1, L2, regularization.
Optimization theory of algorithms such as GD with momentum, SGD, Adam.

Structuring Machine Learning Projects.

Key Skills:
Project management for machine learning.

Convolutional Neural Networks.

Key Skills:
CNN theory. Concepts such as padding, stride, filter size.
Using anchor boxes in code for object detection, and practical exercises using MNIST.
Autoencoders and Variational Autoencoders for image datasets.

Sequence models.

Key Skills:
Coding RNNs, LSTMs, Bi-LSTMs in pyTorch.
Understanding the theory behind sequence models.

Machine Learning (Coursera)

Key Skills:
Machine learning course in MATLAB
Each chapter had a coding assignment to complete in order to continue with the course.
Covered a variety of topics, including MLPs, KNN, K-means, SVMs, RNNs, and machine learning project planning techniques.