Governments can start AI regulation by tackling pressing problems of unethical existing AI. Ethical training is of the essence in the Computer Science curriculum.
Ethics in Artificial Intelligence : Safety
While AI can greatly improve our safety. It is there to remind us of what we can be forgetful about or make us aware of inherent risks. However, we should be careful when counting on it to be our angel guardian.
Ethics in Artificial Intelligence : Input Data
While it is exciting to make Artificial Intelligence projects that can interact with human beings. It is of essence for AI designers and developers to be mindful of all possible user input data, as it can be very dangerous.
5 Major Ways Artificial Intelligence Will Impact Our World
Artificial Intelligence & the advancement of machine learning is inevitably going to impact the world we live in. Here are five major ways it’ll impact our everyday lives.
Verifying Assumptions of Multiple Linear Regression
When we run regression, we hope to be able to generalize the sample model to the entire population. To do so we have to meet several assumptions of the multiple linear regression model. If we are violating these assumptions it stops our generalizing conclusions to our target population because the results might be biased or misleading, so what are the assumptions ? how do we check them ?
Handling Categorical Features with SciKitLearn
After dealing with missing data in your dataset. You will most likely face Categorical Features in numerous datasets. In the majority of cases, these features tend to be non-numerical and thus need to be converted to be processed in machine learning algorithms.
Launching Save Snippet SaveSnippet.com
SaveSnippet or Save Snippet is tool that can be very useful for developers. As they always need to recheck and get some snippet that does the job perfectly.
Handling missing data with SciKit SimpleImputer
When working on data science projects, it’s very likely that you’ll be encountering missing data in your columns. It’s not ideal to disregard or take out all the rows containing missing data for any project. Other columns for the same row where the data is missing can be critical for the data preparation state, so it’ll be wiser to infer or find a way to fill in the missing values in our dataset for a better outcome.