Machine learning is a hot topic, but becoming a machine learning engineer or data scientist may not be very easy. Some people will tell you that you must learn a certain number of classic algorithms, while others will assure you that you do not have to get into all that stuff. It is easy to get confused and wonder if you need to study algorithms for machine learning or not.
Machine learning engineers need algorithms, but it is not the case for data scientists. You do not need to learn algorithms if you only want to apply the existing ones to data sets. But if your goal is to develop new algorithms, knowing the classic algorithms is crucial.
Regardless of whether you learn algorithms or not, understanding the fundamentals will help you implement them better. Read on to learn more about the need for algorithms in machine learning and the different types of machine learning algorithms.
Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!
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Are Algorithms Necessary for Machine Learning?
As we have said, whether you should learn algorithms depends on what you plan to do. If you only want to use the existing algorithms, you can do it without learning classic algorithms. However, knowing these algorithms will help you understand the behind-the-scenes of machine learning.
People usually ask this question with a job position in mind. Here, it is important to note the vast difference between a machine learning engineer and a data scientist.
A machine learning engineer designs and develops machine learning systems and schemes. They have to analyze and implement suitable machine learning algorithms and tools. So, extensive knowledge of algorithms is necessary as they work with algorithms much of the time.
On the other hand, a data scientist does not work with algorithms a lot. It is about generating ideas and developing modules in the algorithms. They investigate data and perform exploratory data analysis. So, you may not have to learn all the ins and outs of various algorithms.
With that said, it does not hurt to know the most common machine learning algorithms like linear regression, logistic regression, k-means clustering, etc. If you understand these algorithms well and can work with them, you will be much more skillful.
Understanding the Types of Machine Learning Algorithms
It is essential to understand the types of algorithms in machine learning and how they work. Even if you won’t be writing algorithms, knowing their basics will help you implement them better. Not only that, but the understanding the algorithm types will also help you see the goal of machine learning and AI, putting you in a better position to tackle real-world problems and design machine learning systems.
You can define the types of machine learning algorithms in more than one way. But they are commonly divided into four categories based on their purpose. But before we learn them, it’s essential to clear up some fundamentals.
Basics of Machine Learning Algorithms
Let’s take it from the very beginning. Data is information, and machines can process this data in various ways. They can even teach themselves to recognize and work with data so that they’re able to make predictions about new data.
For example, let’s say there is a dataset of pets. Each row represents a pet, and several columns represent their features like size, name, weight, age, type, etc. This is the kind of dataset that’s fed to the machine.
Now, some columns or features are special, and they’re called labels. Labels are a little less obvious as they’re dependent on the context of the problem we’re working to solve. Usually, if we want the machine to predict a particular feature based on others, then that feature is the label.
Let’s continue our example. If we want to predict whether a pet is healthy or sick based on the symptoms and other details, then that is the label. Or if we’re going to predict the type of pet (cat, dog, mouse, etc.) based on their qualities (size, weight, and other features), then the type of pet is the label.
Based on what we know, there are two types of data:
- Labeled data: Data that has a label
- Unlabeled data: Data that does not have any labels
Now, let’s understand the types of algorithms.
Supervised Machine Learning Algorithms
Supervised machine learning is what you find in common applications like image recognition, recommendation systems, text processing, etc. In supervised learning, a human expert teaches the computer to learn patterns by feeding it labeled data that is already tagged with the correct answer. The machine learns relationships between data sets, and it can then predict the output values for new data sets.
In our example, the machine would remember the dataset of pets and formulate a model for cats and dogs. When an image is presented, the model can predict the picture and tell us whether it’s a dog or a cat.
Unsupervised Machine Learning Algorithms
Unsupervised machine learning is also very common. In this type of learning, the data fed to the machine has no labels. As you may have guessed, we can not do as much with unlabeled data as with a labeled one. But still, unsupervised learning algorithms are just as important.
In our example, the machine may not be able to tell us the type of pet. However, it can still figure out that two dogs’ images look similar and different from the picture of a cat. It may be able to group them in some way, without knowing what the groups represent.
Unsupervised learning algorithms can be used to preprocess data even if it has labels. This allows us to apply supervised learning algorithms more effectively, which brings us to the next machine learning method.
Semi-Supervised Machine Learning Algorithms
Supervised learning is great, but hand-labeling data is a costly process. And unsupervised learning doesn’t require labels, but it has limited application. Semi-supervised learning algorithms were designed to get the best of both worlds.
Here, we use a very small amount of labeled data and a large amount of unlabeled data. An unsupervised learning algorithm clusters this unlabeled data. Then the machine uses labeled data to label the groups of unlabeled data. This way, we get a large amount of labeled data without incurring the costs of hand-labeling them.
Reinforcement Machine Learning Algorithms
In this type of learning, no data is provided to the machine; only an environment and a problem are given. There’s also an agent who navigates in the given environment and has a goal. The environment is filled with rewards and punishments, which teach the agent to make the right decisions to fulfill its goals.
For example, you can teach a machine to play and master Super Mario Bros. The game world is an environment with various rewards and punishments. The machine can learn to maneuver the right way to reach the next level.
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The field of machine learning or artificial intelligence has gained much popularity in the last couple of years as technology grows. More and more people want to work in this industry.
Machine learning algorithms are essential to master if you want a job that involves creating new algorithms, such as a machine learning engineer’s position. However, you may get away without knowing everything about algorithms as a data scientist. So it depends on what you want to do.
In any case, knowing the basics of machine learning algorithms is always better. We have discussed four types of algorithms or learning methods, in this article, to help you get started.
BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.
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