Have you ever wanted to get into machine learning and just never knew where to begin? In this article, I share my ideas on getting started with machine learning and Artificial Intelligence. I m going to share what language you should learn or use, what kind of math background you need, and the process you should be following in terms of what should you learn first, and when you should move from there to get better and better with machine learning at a beginner level.
The most important thing to start with is the language you should be using for machine learning as a beginner. There are a lot of languages that are designed specifically for machine learning, like R and Python. Do not get me wrong, you can even do it with languages like JavaScript and Java, but I recommend Python. This is because Python is known worldwide as the industry standard for machine learning and artificial intelligence. If you get a job in this field, there is a high chance that you will be using Python to do most of your work. Python is great because you not only use it for machine learning, it is capable of doing many other things, and it is probably one of the easier languages to pick up and start using as a beginner. Another huge reason is that Python has the most modules and support for machine learning and artificial intelligence.
The next thing to talk about is mathematics. Now, I know this scares a lot of people because not everyone is good at math. Let me burst that bubble now. You do not need to be good at math to do Machine Learning. However, having a good understanding of math will be a plus. Let me explain this. You can go through and do any machine learning tutorial and even get a job without understanding how the math for machine learning works, and you can use it well. Here is the thing though, if you understand how the math works, it makes everything so much easier and clearer because, at the end of the day, machine learning is not just inputs and outputs. It is a combination of very complicated mathematical algorithms developed by experts in the field. If you can understand why this works and why things are happening the way they are, you will be better off.
Now that we have picked a language and we understand how much math we need to know for machine learning, or maybe how much math we do not need to know for machine learning, I will go into the steps involved in machine learning so that you can get a vivid picture of the kind of work you will be doing. The first step in any machine learning project is analyzing the problem and figuring out what your machine learning model will do for you. Is it going to predict something? Is it going to create something? Is it going to recommend information? Understanding the problem is important because that is how you are going to pick what algorithms to use.
The second step is data collection. In this step, you get to look for the data you will use for your machine learning algorithm. You must understand that the core of machine learning and artificial intelligence is data. Sometimes you may end up using terabytes of data just to train a single model. So, what data do you need, and where are you going to get it from? The next step is data cleaning. Not all data comes in clean, nice, and crisp. Data cleaning is the process of detecting and correcting corrupt or inaccurate information from your source.
Now that your data is clean, you will need to select the set of data to use for your project. It is not abnormal to find irrelevant data within your cleaned data. This step ensures that only the relevant data goes into your model for training. This brings me to the next step, which is choosing a model. There are so many machine learning models to choose from.
After that, we need to train, validate, and test the accuracy of the model. You will notice that you will have to repeat this step several times while tweaking the model and making it better and better and better until it cannot get any better anymore. Are you confused? This got me all confused the first time I was building my first virtual digital assistant. In case you did not notice, the machine learning part of this is not a major part of this. A major part of this is picking the correct data and what algorithm to use.
So how exactly do you get started with machine learning? The first step is to make sure you understand the basics of your language of choice, and maybe even more than the basics, kind of like the intermediate concepts involved with that language, like objects and classes and all of that. In this article, for example, I have recommended Python. If you are going to use that, make sure you know how Python works, the syntax e.t.c because these beginning steps involve understanding Python or your preferred language to pick out your data, clean it, select it, and work on the model.
After going through the first step, it is now okay to jump into machine learning. It is easy for beginners to lose track and try to go into the deep concepts right off the bat, but I always preach the basics. Learn the basic algorithms first. Now, this is not going to be fun at first. Most people do not brag about using these algorithms, but they are important to understand. They are fundamental, especially if you want to understand how things work and progress in the correct order.
The first algorithm I recommend you learn as a beginner is called linear regression. It is a simple algorithm, and you will understand how this works based on some simple math principles. The next thing that I suggest you should learn after linear regression is K-Nearest-Neighbors ( KNN ). This is a more advanced and useful algorithm. KNN is used for a lot of different applications. I mean, even some of the most advanced AIs use it to do specific things like clustering information. When you get into this, you will notice that it is a different flavor from linear regression. Who doesn’t like different flavors? Anyway, the next thing to learn is Support Vector Machines ( SVM ). These are more advanced because they deal with high dimensional data, and this is where your math will be tested a little bit.
Once you have gone through these three algorithms and understanding exactly how they work, you can move into any area in machine learning. I usually recommend starting with something simpler, like cue learning. It is a powerful algorithm used for training. It is known as reinforcement learning, and that is its whole own strain of artificial intelligence and machine learning. Once you have done cue learning, you can do things like neural networks or computer vision. You can do all of these different things, but start with those three things, that is, linear regression, K-Nearest-Neighbors, and support vector machines, and then move to where you want to go. I hope this article has given you an idea of what you will need in your journey as you go, a road map if you will.
