IBM Machine Learning Professional Certificate

This program consists of 6 courses to develop working skills in the main areas of Machine Learning: Supervised Learning, Unsupervised Learning, Deep Learning, Reinforcement Learning and specialized topics such as Time Series Analysis and Survival Analysis.

Exploratory Data Analysis for Machine Learning

This project attempts to investigate data consisting of records on healthy life expectancy and number of years lived with disability by countries and years.

Supervised Learning: Regression

I used linear regression algorithms to construct a model that accurately predicts how covid-19 affected the global economy of different countries by finding relationship between GDP and human development index and total number of death.

Supervised Learning: Classification

ClinVar is a public resource containing annotations about human genetic variants. In this project, I employed classifier models to predicts whether a ClinVar variant had conflicting classifications.

Unsupervised Learning

The dataset for this project contains New York Stock Exchange historical metrics. I focused on clustering and apply unsupervised learning techniques to find the best candidate algorithm that accurately predicts whether a company has net profit or net loss.

Deep Learning and Reinforcement Learning

In this project I used images of Egyptian hieroglyphs found in the Pyramid of Unas. The goal was to train an image classifier from scratch and utilize transfer learning to build a model that recognizes different hieroglyphs and predict their Gardiner labels.

Specialized Models: Time Series and Survival Analysis

Here I used Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) to predict Google daily stock market prices. We are interested in forecasting the 'Close' series.