Behind The Scenes of a Smart Artificial Intelligence

By Wahyu Kwan | December 25, 2020

Data Science is a combination of multi-disciplinary fields that uses theories from Computer Science, Business, and Mathematics by applying various algorithms with the goal of extracting data and gaining insights from datasets. Although there are a lot of libraries that ease this process, it’s better for you to truly understand the mathematics behind machine learning algorithms to create an Artificial Intelligence that’s powerful. In this post, we’re going to talk about how Math is applied and why Math is crucial to make a smart Artificial Intelligence. Since I don’t want it to get too technical, let’s keep this short and simple.

illustration of an artificial intelligence robot building a connection with human

STATISTICS

Statistics and probability are just like roots to a tree. It’s the core of Data Science. To thrive as a Data Scientist, you need to truly understand how statistics work. One example of a common algorithm that uses probability is Naive Bayes’ Classifiers. Ever wonder how a machine can identify your text or email spam? Magic~ Ok but on a serious note, no. There are mathematical calculations behind Smart Artificial Intelligence that take different features using conditional probability to accurately identify spam. Scary isn’t it?

LINEAR ALGEBRA

Your video recommendations on YouTube, friend suggestions on Facebook, song recommendations on Spotify, and many other apps you may use, are there thanks to linear algebra. It’s used to abstract data and models with the concepts of scalars, functions, matrices, topology, etc. Another example of Linear Algebra in Data Science is Image Recognition. Artificial Intelligence utilizes matrix computations of brightness or color value on each pixel of an image.

CALCULUS

When we talk about calculus, it brings back memories of the ol’ college days. However, to optimize models to produce faster results, calculus plays a pretty significant role here. Some topics to add to your skill sets include differential, integral, multivariate, limits, error minimization, and logistic regressions. One of the wonders of calculus in Data Science is the Neural Network Model. The methodology applies fundamental differential calculus such as Gradient Descent to find the smallest error value so that the model has a high performance. Once you master calculus, you can choose different optimization methods in different cases. Another good use of Calculus in Data Science is to calculate trends like rates of birth or rates of death. Not to mention that economic forecasts and predictions rely on calculus as well.

CASE STUDY

In today’s world, we can get almost anything instantly anywhere within a few clicks. We can request transport from an app like Uber or Gojek, or we can request a food delivery service. But with millions of places, names, and clusters, how does the app know where the hottest pickup location is? Better yet, how do they know how to conveniently name it so we don’t have to make an effort to describe where we are? Surely there’s no way for people like us to do all of this task. This is where Mathematics is needed. K-Means Clustering is a Data Mining process to group data using a partition system. What’s crazy is that all of this process is unsupervised.

CONCLUSION

By just the looks of it, it can be a daunting experience to learn all the math, algorithms, and data science in general. But rest assured if you take the time to get a good grasp of the basics of mathematics, you’ll be the Data Scientist who won’t need to search for a job because employers will be looking for you instead.

In Algoritma, we prepare future Data Scientists like you from zero to hero. We’ll lead you step by step in a carefully designed curriculum so you won’t feel overwhelmed. We also believe in learning by building, hence if you complete our academy, you’ll then have projects using real-world datasets to showcase your portfolio to future employers upon graduation. Click here if you’d like to learn more about our academy. We’ll have someone standby to answer any questions you might have.

If you enjoy today’s blog post about smart artificial intelligence, don’t forget to share it with your friends. Thank you for reading and for your support. Stay safe, productive, and keep learning!

Yuk belajar data science di Algoritma Data Science Education Center! Kamu bisa ikut berbagai kelas data science untuk pemula, salah satunya di program Academy kami.

PELAJARI LEBIH LANJUT

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Data Science is a combination of multi-disciplinary fields that uses theories from Computer Science, Business, and Mathematics by applying various algorithms with the goal of extracting data and gaining insights from datasets. Although there are a lot of libraries that ease this process, it’s better for you to truly understand the mathematics behind machine learning algorithms to create an Artificial Intelligence that’s powerful. In this post, we’re going to talk about how Math is applied and why Math is crucial to make a smart Artificial Intelligence. Since I don’t want it to get too technical, let’s keep this short and simple.

STATISTICS

Statistics and probability are just like roots to a tree. It’s the core of Data Science. To thrive as a Data Scientist, you need to understand how statistics work. One example of a standard algorithm that uses probability is Naive Bayes’ Classifiers. Ever wonder how a machine can identify your text or email spam? Magic~ Ok, but on a serious note, no. Mathematical calculations behind smart Artificial Intelligence that take different features using conditional probability to identify spam accurately. Scary, isn’t it?

LINEAR ALGEBRA

Your video recommendations on YouTube, friend suggestions on Facebook, song recommendations on Spotify, and many other apps you may use, are their thanks to linear algebra. It’s used to abstract data and models with the concepts of scalars, functions, matrices, topology, etc. Another example of Linear Algebra in Data Science is Image Recognition. Artificial Intelligence utilizes matrix computations of brightness or color value on each pixel of an image.

CALCULUS

When we talk about calculus, it brings back memories of the ol’ college days. However, to optimize models to produce faster results, calculus plays a pretty significant role here. Some topics to add to your skill sets include differential, integral, multivariate, limits, error minimization, and logistic regressions. One of the wonders of calculus in Data Science is the Neural Network Model. The methodology applies fundamental differential calculus such as Gradient Descent to find the smallest error value so that the model has a high performance. Once you master calculus, you can choose different optimization methods in different cases. Another good use of Calculus in Data Science is to calculate trends like rates of birth or rates of death. Not to mention that economic forecasts and predictions rely on calculus as well.

CASE STUDY

In today’s world, we can get almost anything instantly anywhere within a few clicks. We can request transport from an app like Uber or Gojek, or we can request a food delivery service. But with millions of places, names, and clusters, how does the app know where the hottest pickup location is? Better yet, how do they know how to conveniently name it so we don’t have to make an effort to describe where we are? Surely there’s no way for people like us to do all of this task. This is where Mathematics is needed. K-Means Clustering is a Data Mining process to group data using a partition system. What’s crazy is that all of this process is unsupervised.

CONCLUSION

By just the looks of it, it can be a daunting experience to learn all the math, algorithms, and data science in general. But rest assured if you take the time to get a good grasp of the basics of mathematics, you’ll be the Data Scientist who won’t need to search for a job because employers will be looking for you instead.

In Algoritma, we prepare future Data Scientists like you from zero to hero. We’ll lead you step by step in a carefully designed curriculum so you won’t feel overwhelmed. We also believe in learning by building, hence if you complete our academy, you’ll then have projects using real-world datasets to showcase your portfolio to future employers upon graduation. Click here if you’d like to learn more about our academy. We’ll have someone standby to answer any questions you might have.

If you enjoy today’s blog post about artificial intelligence, don’t forget to share it with your friends. Thank you for reading and for your support. Stay safe, productive, and keep learning!

Yuk belajar data science di Algoritma Data Science Education Center! Kamu bisa ikut berbagai kelas data science untuk pemula, salah satunya di program Academy kami.

PELAJARI LEBIH LANJUT