Fairness and Bias
You don’t need to do the heavy lifting alone.
If you have ever developed or worked on any type of machine learning algorithm, then you must have — at some point — needed to check if your model is biased and ensure that this bias is removed. Having a biased system will lead to inaccurate results that could jeopardize your entire project.
Machine learning algorithms have proven their value in various application fields, from medical applications to self-driving cars and weather predictions. Although machine learning has many advantages, if your machine learning model contains any type of bias, you’ll not be able to harness its full potential.
Different sources…
Learn all the basics of data science from the comfort of your home.
So, you decided you want to get into data science. You go ahead and Google for hours — if not days — how you can learn the basics of data science, materials you can use to study, and courses and bootcamps you can take that will improve your chances of landing a job once you’re done.
All of that is great; the only problem is that you will end up with millions of results if you try to Google data science courses or materials. How can you choose what is good and what’s not? …
To fix your bias, you need to know why it happened.
Machine learning bias is a term used to describe when an algorithm produces results that are not correct because of some inaccurate assumptions made during one of the machine learning process steps.
To develop any machine learning process, the data scientist needs to go through a set of steps, from collecting the data, cleaning it, training the algorithm, and then deploying it. This process is prone to error; if one occurred in any of the steps, it would mitigate through the entire process, causing its effect to magnify in the final results.
All subfields of data science, whether it be…
Simple steps for a faster code
In the past years, Python has proven itself an easy to learn, versatile, and potent programming language. It became one of the top choices to learn if you’re new to the programming world or if you’re interested in scientific programming, data science, or general computer science.
Due to this increase in popularity, developers and companies utilized Python in their daily work routine by using it to build applications, conduct research, and develop packages for others to use. …
Simple steps for much better results
One of the most frustrating things that happen — more often than data scientists like to admit — after they spend hours upon hours gathering data, cleaning it, labeling it, and using it to train and develop a machine learning model is ending up with a model with low accuracy or large error range.
In machine learning, the term model accuracy refers to the measurements made to decide whether or not a certain model is the best to describe the relationship between the different problem variables. …
There are more options than you may think.
One of the essential skills one needs to master to get into any data science branch is programming. Now, if we overlook how confusing it is to start learning data science, choosing a programming language to use on your learning journey is an entirely independent challenge.
There are many things that you need to consider when choosing a programming language. Which one will perform better in the field you’re targeting? Which has somewhat a better future? Which supports more features? And if you’re a beginner or on a time-limit, which is easier to learn?
All these are valid questions; some…
6 Steps towards natural language processing
Like any other data science branch, there are thousands of learning materials for natural language processing online. Also, like any other branch, the majority of these materials aim towards specific concepts of the field rather than the bigger picture.
These tutorials are essential, given that you know what you want to learn and in what order you should learn them. The problem arises when you’re new to the field and don’t know where to start to reach your desired endpoint.
When you want to learn something new, your first logical step will be to look up a road map or…
Knowing the terminology is essential to understanding any tutorial.
When you decide to learn a new skill, there are often multiple challenges that you need to overcome until you master that skill. You need to have a solid idea of what you need to do/learn to master the idea; you need to know what resources you can use, you need to be able to distinguish between good and bad resources so you won’t waste your time on the wrong ones.
Perhaps the toughest step you’ll need to take is to learn the skill's language. If we talk precisely about natural language processing (NLP) as the new skill you’re aiming…
Master NLP regardless of your data science level
Beginnings are always the most difficult. You feel lost, you don’t know where to start and which way is best. That gets even more true if what you’re trying to get into is a complex and very broad field like data science. Not just broad, data science is one of those fields with an overwhelming amount of information that you could find online.
The most popular data science branch is natural language processing (NLP). NLP is a branch of computer science that is concerned with allowing computers to understand and use natural languages. …
Explaining something gives you a deeper understanding of it.
Data science is one of the widely popular fields today, so many people consider joining the field every day. And those in the field are always seeking to expand their knowledge, strengthen their portfolios and connect with similar minded people.
When you’re a part of a fast-advancing field like data science, where there is always something new to be learned, implemented, and analyzed, then your learning journey is never really over. That’s one of the main advantages of the field; it challenges you intellectually and keeps you in a state of continuous learning and growing.
Because of that, you will…
