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A machine learning algorithm is a mathematical phrase which signifies data in the context of a problem. The goal is to transform the data into insight. For instance, if an online retailer would like to anticipate sales for the upcoming quarter, they can use the algorithm which predicts sales based on previous sales.

Another example can be seen by
windmill creators. They may visually watch out significant equipment and supply
the video data via an algorithm. As a result, it can be trained to identify
dangerous cracks. Then, what is the **machine
learning methods** used to create all of these algorithms or models?

The complexity and speed of this field generate new techniques which potentially overwhelming for novice data scientists. If you are a beginner, you may find it hard to learn about every possible method. However, you can start from these simple methods that we are going to discuss below.

This method is part of supervised machine learning. It helps to explain or predict a certain numerical value depending on a selection of prior data. For instance, it is used to predict a property price regarding the past pricing data for related properties. Linear regression is the simplest technique in this method.

This one of the simplest **machine learning methods** use the
mathematical equation of the line (y=m*x+b) to form a data set. This linear
regression model can be trained with lots of data pairs (x,y) by computing the
position and slope of a line which reduces the overall distance between all
data points and the line.

For example, this regression method can be used to forecast the energy consumption of particular buildings by collecting the age of the building, square feet, and a number of stories. Because there is more than one input like age, square feet, and others, you can employ a variety of linear regressions.

Aside from linear regression, there
are some other techniques available. This method runs the range of simple to
complex techniques like polynomial regression, regularized linear regression,
decision trees, random forest regression, and many others. But, you can always start
with the simple **machine learning methods**
first.

This is another class of supervised ML. Classification method explains or forecasts a class value. For instance, this method can assist to predict whether an online customer will purchase a particular product or not. The output can be a buyer or not buyer: yes or no.

The classification method isn’t restricted to these classes. This method can help to evaluate whether a specified image contains a truck or a car. The output will be three dissimilar values: 1) the image includes a truck, 2) the image includes a car, or 3) the image doesn’t contain either a car or a truck.

If you are wondering the simplest **machine learning methods** here, logistic
regression is the one. It may sound like a regression technique, but it is not.
Logistic regression guesses the probability of an occurrence of an occasion
regarding one or more inputs. It can be used in a variety of applications as
well.

For example, a logic regression can receive inputs in the form of two exam scores for a student. This data is then used to estimate the probability whether the student can be admitted to a certain college or not. Since it is utilized to estimate a probability, the output is either 0 or 1.

Clustering method is a category under unsupervised ML. Their objective is to collect or cluster observations which have similar characteristics. This method doesn’t employ output information for training. Instead, it lets the algorithm determine the output. Here, we can only utilize visualization to monitor the solution quality.

The most popular **machine learning methods** in this
category is K-Means. The “K” here implies the number of clusters we create.
But, you should note that various techniques are available to select the K
value. You can use the elbow method and some other techniques.

Generally, K-Means can randomly select K centers within the data, allocate each data point to the nearest created centers, and re-compute each cluster’s center. Then, if the centers don’t alter, the process is done. But, the process can return to step two if the centers persist to modify.

Aside from K-Means, there are some other techniques available in the clustering method. As you explore it further, you will find many helpful algorithms like Mean Shift Clustering, Density-Based Spatial Clustering of Applications with Noise, Agglomerative Hierarchical Clustering, Expectation-Maximization Clustering with Mixture Models, and many others.

This is another one of **machine learning methods** to learn. As
the name suggests, this method employs dimensionality reduction to get rid of
the least significant information from a data collection. In practice, there
can be data collections with hundreds or even thousands of columns. Thus,
minimizing the total number is crucial.

For example, images can contain thousands of pixels, not all of them matter to the analysis. Then, when analyzing microchips within the manufacturing process, we may encounter thousands of measurements and tests applied to each chip. At this point, you will call for dimensionality reduction algorithms to make it manageable.

The most popular **machine learning methods** in this
category is Principal Component Analysis. This method diminishes the dimension
of the feature space by discovering new vectors which improve the data linear
variation. It can minimize the data noticeably and without losing too much
information when the linear associations of the data are powerful.

In conclusion, each machine
learning method has its own characteristic and function. Since this field is
growing rapidly, there are lots of methods available. If you are just a novice
data scientist, you can start with something simple like the **machine learning methods** mentioned
above.

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