Machine Learning Simplified

The problem with most machine learning explanations is that they are quite complicated in nature. Few people can explain machine learning algorithms and data machine learning in a way that will be simple for everyone to understand. Even worse, the best practices machine learning skills you need to know and understand are often lost in the most complicated explanations.

Machine learning is a field of computer science and artificial intelligence that deals with teaching machines to learn from data. Without data, you cannot have machine learning. That is the first thing you need to understand. However, data machine learning is something that you also need to think about in a holistic way as well. The entire machine learning landscape can get complicated if you aren’t smart about how you look at things. It will be simple for you to understand when things go a certain way.

What Makes Up Machine Learning

The biggest thing to understand is that machine learning is a subfield of artificial intelligence, which is a subfield itself of computer science. However, many mistake machine learning as a separate branch from artificial intelligence. There are multiple ways to do artificial intelligence, and machine learning is only one of those ways. Let’s look at what makes up machine learning as well. It is crucial to understand that because you now see what we need to do to make machine learning work well.

Features – You can think of features are the specific things you adjust inside your data. These are the variables that you use to anchor your machine learning model. For example, if you have thousands of pictures of cats, how would you figure out where to start teaching the machine learning model what a cat is? That is where features come in.

Data – Data is the first component of machine learning. In fact, you cannot have a machine learning model without data. Data is what you run algorithms against to make predictions in the future.

Algorithms – Algorithms are what they sound like. These are the different machine learning algorithms that you run on data to make predictions in the future. They are a fundamental part of the process and crucial for success.

Machine Learning vs. True Intelligence

Before we go further, it is crucial to understand the major difference between machine learning and true intelligence. There are many articles online that talk about machine learning being a replacement for human beings. While it is possible in certain fields, the reality is that machine learning is nothing compared to intelligence. Machine learning is simply feeding data into an algorithm that can then make predictions based on what it learns.

If fundamental things change during that chain, you start getting big errors in your machine learning models. True intelligence can create new things, which is not true for machine learning models. Ultimately, true intelligence offers many more advantages compared to machine learning models. The good news is that we are far away from computers that are truly intelligent.

The World of Machine Learning

The machine learning world is a huge one that you need to understand. There are plenty of fields and subfields to go along with the hundreds of algorithms you can use for your problem. However, there’s also the field of deep learning to think about. In terms of variables, feature types in machine learning are also crucial because they determine whether your machine learning model will be accurate. The main parts of the machine learning world are:

Classic Learning

Deep Learning

Reinforcement Learning

These are the major components that you need to know about the modern machine learning paradigm. They ensure many benefits in terms of successfully implementing machine learning initiatives.