Technology has transformed humans’ interaction with machines. It has also innovated proactive and reactive responses from machines based on highly advanced data-crunching algorithms and instructions.

As technology evolves, you need to understand and develop new ways to solve problems using Machine Learning (ML). No single algorithm can solve every problem. While using a hold-out ‘test set’ of data to evaluate performance, it is important to try many different algorithms. Many factors affect your choice of algorithm, such as the size and structure of your dataset.

What are Machine Learning Algorithms?

Machine Learning Algorithms are computer programs that can interpret and learn from datasets. They evolve with experiential learning without requiring any human intervention.

For example, they can learn a target function (f) that maps input variables (x) to an output variable (y) and the equation would read as:

 y = f(x)

Here, y is the prediction you would like to make in the future, given the new examples of input variables x. f  is an unknown value, so we use data from Machine Learning Algorithms to determine its value.

There are three different categories of Machine Learning Algorithms:

1. Supervised Algorithms: Here, the training data set has input and output. During sessions, the model adjusts its variables to map the input to the corresponding output.

2. Unsupervised Algorithms: In these algorithms, there is no target outcome. Rather, algorithms cluster datasets for different groups.

3. Reinforcement Algorithms: These algorithms are trained to make decisions. Based on these decisions, the algorithm trains itself, considering the success or error in the output. Over time, reinforcement algorithms train themselves to make reliable predictions.

Categories of Machine Learning Algorithms

Machine Learning will play a major role in data analytics, Big Data and cloud computing. Here are the ten most commonly and widely used Machine Learning Algorithms.

1. Linear Regression

It is one of the most well-known algorithms in machine learning and statistics. Linear regression is represented by an equation that uses data points to find the best fit line to model the data. In short, a relationship is established between dependent and independent variables by fitting them to a line. In an equation, this looks like:

y = a*x + b

In this instance, y is the dependent variable, a is the slope, x is the independent variable and b is the intercept. You can derive a and b by minimising the sum of the squared difference of distance between data points and the regression line.

2. Logistic Regression

This algorithm is used to estimate discrete values from a set of independent variables. On a graph, logistic regression looks like a big S and fits all values with the range of 0 to 1. It is used where you might expect a ‘this or that’ form of output, such as an occurrence where you might want to determine whether it will rain or not. It can be represented as the following equation where b0 and b1 are constants:

y = f^(b0 + b1*x) / (1 + e^(b0 + b1*x))

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3. Decision Tree

Decision trees are important algorithms for predictive modelling machine learning. The representation of this type of algorithm is through a branching out tree into binary variables. They can be used to determine both categorical and continuous dependent variables. For example, credit card companies might use different communication messaging for different segments of their audience. Decision trees can help with segmentation and determine - who is above 30 years of age vs below, a current cardholder vs potential customer, married or single, etc.

4. Support Vector Machines (SVM)

SVM is a classification-type algorithm that uses a hyperplane or line known as a classifier to separate the data points. Using an SVM, you plot raw data as points in an n-dimensional space, where n is the total number of features you have. Each feature’s value is then tied to a particular coordinate, which makes it easy to classify the data. The distance between the line and the closest data points is called the margin. The optimal hyperplane is the one with the largest margin. The points along the classifier are called the support vectors, which help find values for the coefficients that maximise the margin.

5. KNN (K-Nearest Neighbours)

This is useful for both classification and regression problems. The model representation for the KNN algorithm is the entire training dataset. Predictions are generated for a new data point by browsing through the whole training set for k number of similar instances (called the neighbours) and summarising the output variable for them. It’s like talking to a person’s friends to get to know more about them.

6. Naive Bayes

Naive Bayes is a simple algorithm but is known to outperform highly sophisticated classification methods. It is based on the ‘Bayes’ Theorem’ in probability. It is called naive because it works on the assumption that each input variable is independent. This model comprises two types of probabilities that can be calculated directly from your training dataset:

1. The probability of each class
2. The conditional probability for each class with respect to each x value.

Additional Read: What is Big Data Analytics and Why Is It Important?

7. Learning Vector Quantisation

One downside of the KNN algorithm is that you need to look at your entire dataset the whole time. The Learning Vector Quantization (LVQ) algorithm is a neural network algorithm that lets you choose the number of instances and the exact nature of those instances that you should hang on to. LVQ is represented by a collection of codebook vectors. LVQ requires less storage and memory compared to KNN.

8. K-Means

K-Means is an unsupervised algorithm that is used to solve clustering problems. Datasets are classified into k number of clusters so that all the data points within one cluster are homogenous and heterogenous from the data in the others. First, the algorithm picks a certain number of points called centroids, let’s say that number is k. Every data point forms a cluster with centroids closest to it. This helps determine the closest distance for each data point.

9. Random Forest

A collection of decision trees form the algorithm that is known as the Random Forest algorithm. Every tree attempts to estimate a classification which is called a ‘vote’. Each vote from each tree is considered, and then the most voted classification is chosen. Every tree in a random forest algorithm is grown to the most substantial extent possible.

10. Dimensionality Reduction Algorithms

Sometimes, datasets contain multiple variables that can become very hard to handle. With multiple data collection sources today, datasets often have thousands of variables that are difficult to handle and include unnecessary variables. In these cases, it is nearly impossible to identify the variables that are important for your predictions. It is where Dimensionality Reduction algorithms are used. They use Random Forest, Decision Tree and other Machine Learning Algorithms to identify the most critical variables and other necessary details.

Build a career in Machine Learning

Machine Learning (ML) is among the most lucrative fields today. According to one survey, Machine Learning saw a growth of 344% in 2019 and a median salary of US $146,085 per annum. If you want to build a career in ML, you should start today. Enrol in an online training program with Koenig.

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