Linear regression is an algorithm that is used to visualize the relationship between two variables. The two variables which are used in this algorithm are the independent and dependent variables.
The independent variable is the variable that is not impacted by the other variable. When adjustments are made in this variable, the levels of the dependent variable will fluctuate.
The dependent variable is the variable that is being studied, and is what the regression model attempts to predict.
The relationship between the input variables (X) which is the dependent variable and the target variables (Y) which is the independent variable…
A Gaussian function is a function that cuts across mathematics and statistics. It is also used in signal processing for computer vision.
This function is a type of function that shows continuous probability distribution for a real-valued random variable. This function is characterized by a ‘bell-curve’ graph which signifies normal distribution.
μ = mean
σ² = variance, often written as its square value.
To represent uncertainty in the estimated location of a self-driving vehicle.
After importing the required library, I set the Gaussian function using the above equation and putting the exponential and coefficient into consideration. …
Machine leaning involves analyzing large sets of data to look for trends or correlations, and to use that to help characterize new observations and, in some cases, to perform tasks.
Using machine learning in research is like using other new tools in the practice of science.
In this article, I would be articulating the machine learning laboratory protocols used in medical imaging diagnosis.
This section is where you define what you are working on, the available statistics, previous methods or traditional methods used in working on this kind of project, the kinds of data that is usually associated with this…
According to the FDA, medical imaging refers to several different technologies that are used to view the human body in order to diagnose, monitor, or treat medical conditions. Imaging can come in different formats which includes; CT Scan, MRI, X-rays etc. This technique has been in existence since the 1960s and has evolved over the years into better ways of usage.
Artificial Intelligence has been infused into this sector of healthcare and has increased accuracy and precision medicine. It is also cost effective, faster, efficiency and reduced burn out in sectors that deal with medical imaging.
Applying AI in 2D…
Electronic Health Record (EHR) according to Wikipedia is the systematized collection of patient and population health information stored electronically in a digital format. These records are shared through network-connected or other information networks and exchanges. EHRs may include a range of data, from demographics, medical history, medication and allergies to immunization status, laboratory test results, radiology images, vital signs, personal statistics like age and weight, and billing information.
This project is a hypothetical case of a data scientist working with EHR for patient selection for diabetes and it is one of my projects from AI for Healthcare nanodegree program. This…
Feature scaling in machine learning is a process of calculating distances between data. There are so many methods of scaling data, but in this practice I worked with the standard scaler from scikit-learn.
Standard scaler involves standardizing a feature by subtracting the mean and then scaling to unit variance. This results in a distribution with a standard deviation equal to 1. The variance is equal to 1 also, because variance = standard deviation squared. And 1 squared = 1. It also makes the mean of the distribution 0. About 68% of the values will lie be between -1 and 1.
The novel Coronavirus disease also known as COVID-19, is a new strain of SARS-Cov-2 that has ravaged the world as a global pandemic with its rapid spread and high mortality. This has brought together the different stakeholders in the world from the government to academia to researchers and scientists to curb this virus through vaccine development and testing. Currently, there are varieties of vaccines all around the world in different countries and this analysis seeks to monitor its progression.
Tracking the progress of the Covid-19 vaccine in Nigeria in comparison to Africa and the world.
Time series analysis is a statistical analysis that deals with trend analysis. Time series analysis is done using a time series data that spans across a period of time. In summary, it involves looking for the correlation between your dependent variable and time.
Facebook Prophet algorithm is an algorithm designed by facebook which is an open source time series forecasting algorithm. It builds a model by finding the best smooth line represented by:
y(t) = g(t) + s(t) + h(t) + ϵ
g(t) = overall growth trend
s(t) = yearly seasonality, weekly seasonality
h(t) = holiday effect
One hot encoding is a process where categorical variables are converted into a form that is fed to a machine learning algorithm for more accurate prediction.
I demonstrated the concept on a dummy data curated by me.
In this sample dataset, ‘ascites’, ‘edema’, and ‘stage’ are categorical variables while ‘cholesterol’ is a continuous variable, since it can be any decimal value greater than zero.
In this dataset, I applied one hot encoding to the edema column because it had three categories. One hot encoding onto this column will create feature columns for each of the possible outcome. …
This is the second phase of my image classification model. The article on how was done can be found here.
For the second phase, I deployed the model to streamlit. To make this possible, you will need to apply for an invite and once you have gotten it, you can start your deployment.
First, you will need to install streamlit into your local machine using
pip install streamlit
Then run => streamlit hello
This will open up the web page on your localhost. For more information, you can check out their documentation .
Data Scientist || Machine Learning enthusiast and hobbyist