Today we will talk about machine learning and why you should be using it. Machine learning is a topic that seems to have been gaining momentum lately, likely because of companies such as Google or Facebook using machine learning systems for their applications. In this article, I will discuss ten reasons why you should be working with machine learning algorithms as a developer, data scientist, statistician, or engineer.
Data Handling
The amount of data being stored has been growing tremendously over the last several years. It’s estimated that 90% of all information in the world, today was created in just the past two years. This means that more information is available than ever before in history – but all data is useful. When your data is useful, you can extract value from that data. The three V’s of big data are volume, velocity, and variety – I think that another V should also be added to this list: veracity. Veracity refers to the correctness and trustworthiness of the data. Check RemoteDBA.com for more.
How do I know whether my data is trustworthy? Some people think that it doesn’t matter how incorrect your data might be as long as you get the gist of what’s going on – I disagree with them! Imagine if Netflix used useless user information for their recommendations or if Facebook only looked at “likes” without paying attention to all other types of engagement (comments, shares, saves). Both companies would fail miserably because they require information. Therefore, regardless of the application, whether it’s Netflix or Facebook or a new startup you’re building – I’d argue that the truth and usefulness of the data are more important than the type of data. Machine learning can make your data truthful and useful.
Technology Advancements
The advancements in technology (processing power and storage capabilities) enable us to build massively scalable systems and algorithms. Most of the challenges we faced 10 years ago don’t exist today because we no longer have these limitations. We live during an amazing time! A few days ago, one Twitter user asked Google why they don’t use reinforcement learning for ranking search results. Their response was simple: “Because it doesn’t work.” They meant by this statement that current supervised machine learning algorithms are sufficient to rank search results without any problems automatically. Although you might not be using Google Search, the advancements in machine learning enable companies to build all sorts of scalable systems; therefore, this development enables us to solve more and more problems every day.
Transformational
Machine learning is transformational. Data scientists always like to say that data science will “transform” or “revolutionize” business and how we work. I think they’re right. I believe that machine learning is transforming our world today because companies such as Microsoft, Facebook, and Google (again) use these algorithms for their applications (in Microsoft’s case: Office). In contrast, Uber uses them for their drivers, and Airbnb uses them for their customers. If your company doesn’t use machine learning, then that means that your competitor probably does. Understanding what your customers are doing or being able to process natural language is extremely valuable nowadays – so much so that companies are willing to pay millions of dollars for it.
If you’ve ever worked with data before, you know how frustrating it can get when you have to deal with dirty data. It’s common for people to enter the wrong email in a sign-up form accidentally, and it’s also common for users in web forms to skip questions (even though they’re required!). This makes sense because we usually enter information quickly; we make mistakes, and we forget things. Sometimes we even need more than one try when entering our information. The only problem with these mistakes is that they prevent us from getting the data we want. 4) Machine learning algorithms require less human intervention. If you use machine learning algorithms, you don’t have to worry about this anymore since they automatically learn from your mistakes! I know that sounds amazing, but it’s true – all of the dirty work is done by the system without any human intervention!
Machine learning allows companies to understand their users better. As mentioned before, having more information typically means making better decisions or performing certain tasks better. One very popular example of how machine learning helps companies understand their users is recommender systems (e.g., Netflix, Spotify). Recommending products is not an easy task, and even though we’ve made a lot of progress in the past 10 years, we’re still pretty far from getting it right. The reason for this is that there are so many different types of users and only one product. If we let a user define what they like and dislike, we can help them find something that matches their interests. In other words: machine learning helps us understand our users better!
As mentioned in point, having more information also means making predictions about your users’ actions or preferences with great accuracy. This is extremely valuable because you don’t have to label millions of items by hand anymore – these systems can do that automatically! For example, if you want to build a system that recommends products based on a user’s purchase history, then you don’t have to spend days or even weeks labeling users’ profiles – these systems can do that on their own! 6) Machine learning algorithms work without supervision.
Machine Learning is Artificial Intelligence
Machine learning is a form of artificial intelligence. Many people believe that machine learning and artificial intelligence are the same things. Although this might be true, I think it’s important to understand them as different fields. Artificial Intelligence is an umbrella term for all sorts of algorithms that help us solve problems by imitating human behavior. Machine learning is just one branch of AI, which focuses on building models from data automatically. In other words: machine learning helps us build better AIs!
There are many applications that use machine learning. The more you work with data, the more likely you are to discover that data is everywhere. This means that it’s also very easy to find applications that use machine learning. Not only do the big companies like Google, Facebook, and Netflix make extensive use of machine learning (e.g., for ad placement), all sorts of other companies employ them too! For example, if you work in health care or finance, you can use machine learning algorithms to help improve diagnosis or optimize portfolios. Even if you don’t work in certain industries, there are still many opportunities where these systems could be applied – think about recommendation engines!
Machine learning is used for many different tasks. The most common form of machine learning consists in training a model from labeled examples which
