Top 13 machine learning algorithms and techniques

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There are a lot of different options that you are able to choose when working with the idea of machine learning. There is a combination of supervised machine learning, unsupervised machine learning, and reinforcement learning that you can use in order to see the results of your project working the way that you want. 

The method that you choose is going to depend on the data points that you have and what your overall goals are about. Some of the different machine learning algorithms and techniques that you can choose includes:

Machine Learning Algorithms and Techniques

Machine Learning Algorithms and Techniques

K-Nearest Neighbors

The first algorithm that we are going to look at is known as the K-Nearest Neighbors algorithm or KNN. This is an example of what we are able to do when working on supervised machine learning so we can see how providing the examples to the system is going to work. When you decide to use the KNN algorithm, you can use it in order to search through all of the data that you are presenting to this algorithm for most similar examples of any kind of instance that you would like to be able to work with.

Once you can do this and you start to see that it is successful for the work you are doing, then this particular algorithm is going to be able to move on and then look through the data points and other information you are using and can provide you with a summary on this. Then, to end it, you will take the results that come out of it in order to make some predictions that you are able to work with.

When we choose to work with this algorithm model, we will find that the learning we can do with our data points is going to end up being more competitive than other models. The reason that this works is that the different parts are able to compete against one another, or they compete against some of the different elements in all of the models so that with the competition, you end up with the best predictions possible.

As you work through some of these different algorithms in this post, you will see that the KNN algorithm is going to work in a different manner and way. In some cases, this algorithm is seen as kind of a lazy approach, but that is due to the fact that it is set up to create all the models you need, but struggles when you go back into it and ask for a brand-new prediction. Depending on the situation you are working with, this can be something good because waiting to make predictions based on where you would want them can ensure that the data used in the predictions is the best that you have, or at least the most relevant or the newest.

There are several benefits that you will see when it comes to using this particular algorithm. When you bring out the KNN algorithm, you can cut through a lot of the noise that is found in the set of data that you are using. This noise is sometimes crazy and loud, especially when the set of data that you are trying to sort through is pretty large. And the more noise that there is, the harder it is to make accurate predictions. Any time that you are worried about all of the noise in your data set and you are working with a lot of data in the process; then it is time to use this algorithm.

With this in mind, there are going to be a few different steps that you can take to work with this kind of algorithm for your needs. This one is pretty easy to bring out, but it can take some time since there are a lot of different points in your set of data that it is working with. Some of the steps that you can focus on to make the KNN algorithm work for your set of data includes:

Machine learning algorithms and techniques

• Load the data into the algorithm for it to read through.
• Initialize the value that you are going to use and rely on.
• When you are ready to get the predicted class, iteration from one to a total number of the data points that you use for training, you can use the following steps to help.
• Start by calculating the distance that is in between each of your test data, and each row of your training data. We are going to work with the Euclidean distance as our metric for distance since it’s the most popular method. Some of the other metrics that you may choose to work with here include the cosine and Chebyshev.
• Sort the calculated distances going in ascending order, based on their distance values.
• Get the k rows from the sorted array.
• Get the most frequent class for these rows.
• Return back the class prediction.

The support vector machines

The next thing that we need to look at is known as a support vector machine or SVM. This is going to be something that can help out with many of the different challenges that the programmer is going to encounter with both classification and regression problems that come up. With this one, a lot of the work that has to be done on any problem that is known as classification can end up doing the work that you do tricky, but this is why the SVM algorithm can come in and help you deal with the issues.

For the SVM algorithm in machine learning to do the work that you want, you can start by taking each set of the data item that you want to work with, and then plot them out in a manner that allows them to become one point on what is known as an n-dimensional space. N is going to be the number of different features that are going to be used in this project. Then you can take the values of those features and work to translate them over to the value that will show up on the coordinates. Once you have been able to reach this point, it is time to determine your hyperplane because it is the part that can show us what differences, if any, are going to show up in our classes.

It is at this point that you are going to notice how the SVM algorithms can sometimes come with more than one support-vector. Often most of these are not going to be that important, and you can ignore most of them and move on. They may just be the coordinates of some of the individual observations that you are going to see. You can then use your main SVM as the frontier that is going to separate them into classes, and this leaves you with two main parts to focus on including the hyperplane and the line.

This may sound a bit confusing, and you may not be sure what steps you are supposed to take or even what you are supposed to be able to do to make all of this happen. The good news is that there are a few different steps that you are able to take to make sure that the SVM is able to sort through any data that you choose. [what is big data?]

The first step that we want to use is to look through and analyze the hyperplane that we want to use. As we go through, it is possible that many different hyperplanes are going to present themselves for you to search through and pick from. And there is also the added challenge that even with all of these options, there is obviously going to be one that works the best. How do you make sure that you are picking the right hyperplane to help you get accurate results with your model? The steps that you can take to sort through the different hyperplanes that show up include:

• We are going to start with three hyperplanes that we will call 1, 2, and 3. Then we are going to spend time figuring out which hyperplane is right so that we can classify the star and the circle.
• The good news is there is a pretty simple rule that you can follow so that it becomes easier to identify which hyperplane is the right one. The hyperplane that you want to go with will be the one that segregates your classes the best.
• That one was easy to work with, but in the next one, our hyperplanes of 1, 2, and 3 are all going through the classes, and they segregate them in a similar manner. For example, all of the lines or these hyperplanes are going to run parallel with each other. From here, you may find that it is hard to pick which hyperplane is the right one.
• For the above issue, we will need to use what is known as the margin. This is basically the distance that occurs between the hyperplane and the nearest data point from either of the two classes. Then you will be able to get some numbers that can help you out. These numbers may be closer together, but they will point out which hyperplane is going to be the best.

When you are able to pick out the right hyperplane, you will find that your points are going just to organize themselves out well, and you will be able to use this information to make good predictions that can move your business forward on the right track. You do need to take some time to explore which hyperplane is going to be the best for your needs in order to get the SVM algorithm to work the best for you, so take your time here and get the right one.

Decision Trees and random forests

The next topic that we are going to explore a little bit is going to be a combination of two topics, but they will work together to help us really get some of the results that we are looking for with predictions and coming up with which decision is right. The decision tree, as well as the random forest, is going to work together in many ways to ensure that you get the right predictions. We will start out this part with a look at decisions trees to learn more about how these work, and then we can compare that with the random forest to better see how the two topics and algorithms work together.

If you would like to take a look at more one choice at a time, you will find that the decision tree is a very efficient tool for data, especially if these choices are really different. Then you can use the information that you get from the decision tree to choose a decision that makes more sense of your business. When the various options are presented to you when the decision trees are done, you can then use this algorithm to see all of the outcomes that each will provide to you. The point of using this kind of algorithm is that it helps you to plan out all of the decisions and see the predictions for each one, helping you to choose the right path.

Now, you are able to work with a few different methods to help with the decision trees. Many times, when we use it in machine learning, it can be used for a variable that would be seen as categorical, or it could be for random variables. However, you could also bring out these decision trees, and the algorithm that goes with it, when you want to work on a problem of classification as well.

To make sure that you are using the algorithm in the proper way and that your decision tree is good, you will need to take up all of the sets of data that you would like to use, and then split them up so you end up with two or more sets, and then each of these sets should have similar data in the process. You can then sort all of these out using independent variables to ensure that they are distinguished out in different sets.

At this point, we are going to answer a question that seems hard to make work when doing the decision trees. To ensure that we make the good decision tree and that all of the different parts are going to work well, it helps to have a good exercise and example to make sure we know what we are doing.

We are going to look at this exercise with the idea that we have a class with 60 people inside of it. All of these students are going to have their independent variables, and we are going to focus on three of these. The independent variables that we need to focus on include the students’ gender, their heights, and their class. As you look through all of the students in our sample class, you will be given the information that 30 of them like to play soccer, before you even get started.

With this information about half the students wanting to play soccer, you decide that it is time to create a model to figure out which 30 students like to spend their time with soccer playing, and then which half of the class would like to do something other than playing soccer in their free time. And we want to make sure that the model we create is as accurate as possible.

To figure out the right steps to take in order to create a model for this exercise, the decision tree has to be able to look at all of the students that you have in the group, and then it has to be able to split them into two groups and get it right. The variables that we have, if you remember from earlier, include the gender, class, and height. The hope with this kind of model is that when it is all done, you can then present a homogenous set of students, one that has students who like to play soccer, and one with students who do not like to play soccer.

There are a few other algorithms that can work well when you are using a decision tree, and they are going to help you to split up the data that you have. This is going to give you a few subsets that you can work with, and they will produce good outcomes that are the most homogenous and help you to make the best decisions for your needs. Remember that you can have more if the situation calls for it. But in this example, we just need to work with two groups; one which consists of the students who play soccer, and one that consists of those who do not.

You will find that there are many times that you need to go through some complex data, and the decision tree can help you to sort things out based on similarities and differences. These decision trees will provide you with a lot of the data that you need, and you can then take that data and make smart and informed decisions for the business.

It is possible to avoid doing this kind of model and working with intuition and older forms of decision making if you would like. But these can be slow and take a lot of time and energy, especially if you have a set of data that is more than 60 people. The decision tree helps to gather in all of the information that you have and can make the process easier. The decision tree can take that information, sort it through quickly, and can help you to look through the information more thoroughly than you would ever be able to do with some of the older methods that you may have used in the past.

Now that we know a bit more about the decision trees and how they work, it is time to work with what is known as the random forests. There are a few times when you do things and meet challenges in machine learning where you are going to want to utilize these random forests. The reason for this is because the random forest is going to help you to look at a lot of different scenarios and options to determine which one is the best for you.

Often you will find that the random forests are going to be the best for providing you with all of the results that you need, and their work is often better than what we see with some of the other available algorithms. There are a lot of benefits to using these, but some of the basic steps that you can use to get the random forest to work for your needs, and to ensure that you can actually make it work when you present it with your set of data will include the following:

• When you are working on your own training sets, you will find that all of the objects that are inside a set will be generated randomly, and it can be replaced if your random tree things that this is necessary and better for your needs.
• If there are M input variable amounts, then m<M is going to be specified from the beginning, and it will be held as a constant. The reason that this is so important because it means that each tree that you have is randomly picked from their own variable using M.
• The goal of each of your random trees will be to find the split that is the best for the variable m.
• As the tree grows, all of these trees are going to keep getting as big as they possibly can. Remember that these random trees are not going to prune themselves.
• The forest created from a random tree can be great because it is much better at predicting certain outcomes. It can do this for you because it will take all prediction from each of the trees that you create and then will be able to select the average for regression or the consensus that you get during classification.

Random forests are going to be the perfect tool to work with when you want to use data science along with some of the data science workflow algorithms of machine learning, and it brings in some advantages that you won’t be able to get with the other algorithms. When comparing it against some of the other algorithms that we are going to talk about in this post, you can see that it is a great option to help you make decisions that are going to improve your business.

Neural networks

No guide on machine learning is going to be complete without a good look at neural networks. These are going to be an example of how we can work with unsupervised machine learning, and they are used in many challenges because they will be set up to look through your data, and then notice if there are any patterns, even if there is a lot of data. This is going to be done in a variety of levels, and it is actually very useful because it can do what the human brain can, but in a faster and more effective way than any human is able to do.

When we look through how these neural networks work, each of the layers that we focus on will have the algorithm stop and then check out if there is some kind of pattern that we can find in that image. If the network gets to a new layer and doesn’t find a brand-new pattern, then it is going to take the steps that are necessary to move on to the next layer. This process is going to go on from one layer to the next until we get all of the layers done and created. The program, if you have set up this machine learning algorithm the proper way, is going to be able to provide us with a fairly good prediction about the image that was scanned, and it gets better the more that we use it.

Now, when we get to this point, there are a few possibilities of what can happen, and the result is going to be based on how well the program will work. If the algorithm was able to go through all of the layers and the different process that is used above, and it was successful at sorting through all of the different layers, then the algorithm can come out with a prediction. If the program was successful here and the prediction is right, then the neurons that come with the system, just like how we see the neurons in the human brain work, will become stronger and will remember this kind of image next time.

The reason that this one is going to work so well is that the program is working with something that is known as artificial intelligence. This is going to allow it to make some strong associations between the patterns and the objects that it was able to find. The more times that our system is able to go through and look at the picture, and then provide us with the right answer, the more effective and efficient it can be if you decide to use it again.

To find out how this works will require us to look a bit closer at how the neural networks work together. For example, let us say that you have set out to create a new program that is able to hold onto a picture, which will be your input. And then it can look through the various layers of that picture until it figures out that the image inside is a car.

If you have gone through and coded this in the right way, the program is able to predict that the picture is that of a car. The program will be able to come up and present you with this prediction based on some of the features that it knows, from past experience, belongs to a car. It could look at the headlights, the placement of the doors, the license plate, and more to come up with this prediction.

When you are looking at your skills in conventional coding and what all is available to you through this, then this process is going to be a difficult one to do. You will find that with machine learning, and the neural networks algorithm, you will be able to get this to work without too many problems.

But how do we get this algorithm to work? To see this happen, you need first to make sure the program has an image of a car to compare the newer image to. The neural network will take this learning picture and look it over. It would be able to start with the very first layer. In this case, this is going to be the edges on the outside of the car. From there, the program would go through other layers that are going to help the neural network learn whether there are any unique characteristics to the picture and will tell it that a car is in the picture.

If the program has worked with trial and error, and it is good at doing this job, it is going to make the right prediction. And the more pictures of cars that you provide to this program, the better it is going to get at finding the car and predicting it, and the smaller details it will notice.

Depending on the type of picture that you are working with, there could be a lot of potential layers that come with this algorithm. The good news here is that the more details and the more layers that your neural network is able to find, the better accuracy it comes with when it predicts the car and what kind of car even.

If the neural network is accurate and does a good job with the predictions, it is able actually to learn from these lessons. It is going to remember what it learned as it went through the layers and can store that information to use later on. If it does need to look at another similar picture in the future, it will be able to make a very quick prediction in the process.

Any time that there is a programmer who is looking to work with the algorithm of neural networks, you may find that there are a lot of projects to choose with, including software for facial recognition. When this happens, all of the information that you or the software is going to need to use won’t be presented ahead of time. You can use the neural networks in order to teach your system, through machine learning, how it is supposed to behave and recognize the faces that show up. This may take some time, but with some dedication and some patience with teaching, it is going to work.

Linear Classifiers

Another type of algorithm that we are going to take a look at is the supervised machine learning algorithm known as linear classifiers. This one is similar to what you find with linear regression, but it is going to help us focus more on the class, rather than on the value of our data. While both of these linear algorithms are useful when it comes to machine learning, we will look at linear classifiers and how we can use them in machine learning.

The linear classifier is going to be used often because it can help you handle a lot of the classification problems that come your way. These classification problems can take up to 80 percent of the tasks that you attempt when working with machine learning, so having an algorithm that happens with this one can help. The main point that we see with classification is that it is supposed to help us predict how probably it is that each class is going to happen given the inputs that you put in, the label and the class. The label for this one is going to be known as the dependent variables.

If you label or your dependent variable is only coming in with two classes to work with when you first get started, then this is a good sign that the algorithm that you are working with should be known as a binary classifier. If you would like to take a look at the classifier and you want it to come with more than one class, this means that it can tackle any kind of label that can handle three or more classes.

Linear Regression

We just took a look at the linear classifiers, but now it is time to take a look at what the linear regression. When we are in machine learning, we are going to have a set of variables for the input (known as x), that is going to help us to determine the variable of the output (y). The relationship is going to be present between our input variables and the output variables as well. The goal of using machine learning here is to quantify this kind of relationship and see what it is and how it works together.

With the algorithm for Linear Regression, we are going to take a look at this kind of relationship between the variables of the input and the variables of the output. This is going to be expressed using the equation that is in the form of y = a + bx.

This means that the goal of using this Linear Regression is to find out what the values of the a and b coefficients. Here, a is going to be the intercept, and then b is going to be the slope that we are able to use with this line.

Logistic Regressions

Linear regression predictions that you get when using that algorithm are going to be in a continuous value. But when we look at logistic regression, the predictions are going to be more of discrete values, but this happens after we have been able to apply some of the transformation functions.

The logistic regression is going to be best suited when we want to do binary classifications. This is when we are going to use data sets that are based on 0 and 1. 1 is going to be the default class, and then 0 is the other part. Let us look at an example. When we want to predict whether an event will occur or not, there are really only two possibilities. It is either going to occur (which will be denoted with a 1) or that the event is not going to happen (which is going to be denoted with a 0). So if we are predicting that our patient is sick, we would label the sick patients using a value of 1 inside the data set.

The logistic regression is going to be named thanks to the transformation function that it relies on, which is going to be called the logistic function. The formula that is used for this one is going to include h(x) = 1// (1 + ex). This is going to end up as an S-shaped curve when you plot it all out on your chosen graph.

In logistic regression, we will find that the output we get is going to take the form of probabilities of the default class. This is different from the linear regression, where we saw that the output was going to be produced directly. As it is a probability, the range of our output is going to be in the range of 0 to 1. So, going back to our example from before, if we are trying to get a good prediction on whether the patients are sick or not, we already know that in our set of data the patients who are sick are denoted with a 1. If our algorithm is able to assign the score of 0.98 to a patient, then this is going to show that the algorithm is pretty sure that the patient is sick.

CART

CART, or Classification and Regression Trees, are one of the ways that you can implement the decision trees. This one will find that the non-terminal nodes that we are able to find with the CART will be the root node and the internal node as well. Then the terminal nodes of that will be considered the leaf nodes. Each of the non-terminal nodes that we find is going to represent just one variable of the input (x), and it can also be the variable for the splitting point that is there.

Then we move on to some of the leaf nodes as well. These are going to help us look at the output variable. The model is going to be used to help us make some predictions, walk the splits of the tree in order to arrive at a leaf node and the output the value present on the leaf node as well.

Naïve Bayes

The Naïve Bayes algorithm will be a technique that is based on the popular Bayes' Theorem for classification. With this technique, you will be utilizing the assumption that there is independence with your predictors, and if this is not the case, the Naïve Bayes algorithm will not work. This can sometimes sound a bit complex in the beginning, but to help keep things easy, the classifier we are using will assume that when we add in any classes, later on, it won't have a relation to the other features that are already there.

Let us take a look at an example of how this works by using apples. When we take a look at an average apple, we can easily say that it has some distinguishing features, such as being red, round and about three inches round. While these features are found in some other fruits at times, when they come together, we can say that we are holding onto a fruit that is an apple. This is pretty beginning thinking, but it is why the algorithm is called a Naïve Bayes’.

The Naïve Bayes’ model is pretty easy to put together, and while the apple example is pretty simple, it can be used to help you out with some of the larger sets of data you will encounter. One of the advantages of using the Naïve Bayes’ model, though, is that it is simple, and sometimes it can perform better than the other sophisticated classification models that you can pick.

As you work with this algorithm a bit more, you may find that there are many times when it is going to work well with your machine learning problems. The Naïve Bayes model is going to be one of the easiest ones to use, and it is actually really efficient at predicting the class that your test data is set to, making it the perfect choice for a business who doesn’t know how to do much with machine learning yet, or who would like to keep things simple.

PCA

PCA, which are also known as Principal Component Analysis, is going to be used to help make the data easy to explore and to visualize by reducing the number of variables. This is going to be done by capturing the maximum variance in the data into a new coordinate system with axes called the principal components.

Each component is going to be a linear combination of the original variables, and it is going to be orthogonal to one another. The orthogonality between the components indicates that the correlation between the components is going to be seen as zero.

The first component that is principal here is to capture the direction of the maximum variability that is found in the data. The second principal component is going to help capture the remaining variance in the data, but instead, it is going to be variables that are uncorrelated with the first component. Similarly, all of the successive principal components that come next are going to try and capture the rest of the variance while still being seen as uncorrelated with the component that comes before it.

Clustering

The next algorithm that we are going to take a look at is known as the clustering algorithm. This is another example of how to work with unsupervised machine learning, and there are a few options here that we can choose to help us with clustering. This method is basically going to help us to take all of the points of data in our set, and then put them in the group that they match up with the most. You get to be in charge with this one and decide how many of these clusters you would like to put the information into. You could have two clusters, five clusters, or twenty clusters, based on what you are trying to find and how much information you are sorting through.

The algorithm for clustering is going to be helpful as you work with machine learning because it is responsible for doing most of the work for you. This happens because the algorithm is going to pick out how many points, and which points, are going to fall into the main clusters that you choose. To make sure that you can keep all of this information organized, the main clusters are going to be known as the cluster centroids.

So you will be able to look at this one and see that all of your data point clusters will have the same centroid if they are similar or have something in common. There will be some kind of attribute that all of these data points are going to have in common. Once the original clusters are formed, you can take these individual centroids and divide them to end up with more cluster sets. This can happen a few times with more of this division going on. This continues until you have a situation where the centroids stop changing, and you know you are done.

There are a few reasons why you would want to pick out a clustering algorithm to help you with your program in machine learning. First, doing computations with clustering has a low and efficient cost, especially when you compare it with the other algorithms that we have talked about so far. If you want to work on problems for classification, you will find that the clustering algorithms can be very efficient. You do need to be careful though because it will not be able to solve any predictions for you and if you end up categorizing your centroids in the wrong way, the whole project can turn out incorrectly.

The Markov algorithm

The next algorithm that we are able to take a look at is the Markov algorithm. This is going to be an algorithm that is then able to take some of the data that you input and then helps to translate it a bit so that it works when you want to use it in another language. You have to take some time with this one to set up the rules of how you want it to work, or there is going to be some confusion. This is a nice thing to have, though because you can pick out and experiment a bit with the parameters that are going to tell you how the set of data is going to behave for you.

There are a lot of different ways that you can use the Markov algorithm in machine learning. One method is when you are working with DNA. You would take the sequences of DNA and then use the Markov algorithm to translate them into numerical values. Numerical values are a lot easier to understand compared to trying to read through the strands of DNA.

One of the main reasons that you would use this algorithm is that it is really good at learning problems when you know the input, but the parameters are not always specified. This algorithm can find insights inside of the information; sometimes, these insights are going to be hidden, and the other models we have discussed will not bring them out.

But there are a few downfalls to using it. The Markov algorithm can be difficult because, with this model, you have to go through and create new rule each time you want to use a new programming language. If you plan to stick with just one programming language to do everything, this won't be a big deal. But since a lot of programmers like to combine languages or work on many different options, this can get pretty tedious over time.

SARSA

Most of the algorithms that we have taken a look with so far in this post are going to focus mainly on the supervised and the unsupervised machine learning options that you can work with. While those can be useful, we are going to end this post will a look at one of the reinforcement learning algorithms that you can work with.

This algorithm is going to be known as the “state action reward state action” algorithm, or SARSA. For this one, we are going to spend some time describing the decision process policy that works with the Markov algorithms that we just talked about above. This is going to be the main function for your updated q-value, which is going to rely solely on the learner’s current state, the reward that the learner can get when they make their selections, the action that they choose, and the place that the learner is going to be at once they are done with that action. There are many parts to bring together the right way to make this algorithm work well for you, but this is part of the beauty of working with the SARSA algorithm for your needs.

In many cases, this is going to be one of the safest algorithms to use to find the solution. But there will be times when the learner will earn a reward that is higher than average for their trials, especially compared to some of the other algorithms that you could use. In some cases, this one won’t end up choosing the optimal path either, so this could run into some issues as well.

As you can see, there are a lot of different types of machine learning algorithms that you can work with. Each of these is going to be slightly different, and this makes them perfect based on the kind of data and the information that you want to work with when doing these. Take a look at all of these different algorithms that work with machine learning, and familiarize yourself with some of those that you think you are most likely to use for your own data and your own needs.