For newbies, machine learning algorithms may seem too boring and complicated. Well, to some extent, this is true. In most cases, you stumble upon a few-page description for each algorithm and yes, it’s hard to find time and energy to deal with each and every detail. However, if you truly, madly, deeply want to be an ML-expert, you have to brush up your knowledge regarding it and there is no other way to be. But relax, today I will try to simplify this task and explain core principles of 10 most common algorithms in simple words (each includes a brief description, guides, and useful links). So, breath in, breath out, and let’s get started!
Linear Regression: Initially developed in statistics to study the relationship between input and output numerical variables, it was adopted by the machine learning community to make predictions based on the linear regression equation.
The mathematical representation of linear regression is a linear equation that combines a specific set of input data (x) to predict the output value (y) for that set of input values. The linear equation assigns a factor to each set of input values, which are called the coefficients represented by the Greek letter Beta (β).
Logistic Regression: In logistic regression, our aim should be to produce a discrete value, either 1 or 0. This helps us in finding a definite answer to our scenario. The logistic regression model computes a weighted sum of the input variables similar to the linear regression, but it runs the result through a special non-linear function, the logistic function or sigmoid function to produce the output y.
KNN Classification: The purpose of the K nearest neighbours (KNN) classification is to separate the data points into different classes so that we can classify them based on similarity measures (e.g. distance function).
KNN learns as it goes, in the sense, it does not need an explicit training phase and starts classifying the data points decided by a majority vote of its neighbours.
The object is assigned to the class which is most common among its k nearest neighbours.
Support Vector Machine (SVM): Support Vector Machine was initially used for data analysis. Initially, a set of training examples is fed into the SVM algorithm, belonging to one or the other category. The algorithm then builds a model that starts assigning new data to one of the categories that it has learned in the training phase.
In the SVM algorithm, a hyperplane is created which serves as a demarcation between the categories. When the SVM algorithm processes a new data point and depending on the side on which it appears it will be classified into one of the classes.
Decision Trees: Decision trees are basically a tree-like support tool which can be used to represent a cause and its effect. Since one cause can have multiple effects, we list them down (quite like a tree with its branches).
Random Forest: A random forest algorithm was designed to address some of the limitations of decision trees.
Random Forest comprises of decision trees which are graphs of decisions representing their course of action or statistical probability. These multiple trees are mapped to a single tree which is called Classification and Regression (CART) Model.
Artificial Neural Network: In our quest to play God, an artificial neural network is one of our crowning achievements. We have created multiple nodes which are interconnected to each other, as shown in the image, which mimics the neurons in our brain. In simple terms, each neuron takes in information through another neuron, performs work on it, and transfers it to another neuron as output.
K-means Clustering: In this machine learning algorithm, the goal is to label the data points according to their similarity. Thus, we do not define the clusters prior to the algorithm but instead, the algorithm finds these clusters as it goes forward.
A simple example would be that given the data of football players, we will use K-means clustering and label them according to their similarity. Thus, these clusters could be based on the strikers preference to score on free kicks or successful tackles, even when the algorithm is not given pre-defined labels to start with.
K-means clustering would be beneficial to traders who feel that there might be similarities between different assets which cannot be seen on the surface.
Naive Bayes theorem: Now, if you remember basic probability, you would know that Bayes theorem was formulated in a way where we assume we have prior knowledge of any event that related to the former event.
For example, to check the probability that you will be late to the office, one would like to know if you face any traffic on the way.
However, Naive Bayes classifier algorithm assumes that two events are independent of each other and thus, this simplifies the calculations to a large extent. Initially thought of nothing more than an academic exercise, Naive Bayes has shown that it works remarkably well in the real world as well.
Naive Bayes algorithm can be used to find simple relationships between different parameters without having complete data.
Recurrent Neural Networks (RNN): Did you know Siri and Google Assistant use RNN in their programming? RNNs are essentially a type of neural network which have a memory attached to each node which makes it easy to process sequential data i.e. one data unit is dependent on the previous one.
A way to explain the advantage of RNN over a normal neural network is that we are supposed to process a word character by character. If the word is “trading”, a normal neural network node would forget the character “t” by the time it moves to “d” whereas a recurrent neural network will remember the character as it has its own memory.
So these were the top machine learning algorithms, that you should focus on in coming time.
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