How is machine learning used in data science?
In this technological era, there exists a lot of data that is needed to be analyzed and interpreted. Computers are using artificial intelligence that mimics human actions to deal with data. Now the question is How is machine learning used in data science?
Machine learning is one of the types of artificial intelligence that enables the computer to automatically analyze data by learning from it and by identifying its patterns and trends. Data science helps in extracting knowledge from data using 7 scientific techniques, which are classification, clustering, regression, dimensionality reduction, ensemble methods, neural networks, and transfer learning.
You will get to know about them in detail but before digging in further, you must know the two machine learning categories to understand its algorithms, they are; supervised and unsupervised machine learning.
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Supervised Machine Learning
Supervised machine learning is used to predict or explain an already known data piece. It is done by utilizing the old input and output data to predict new output based on the input. In the service business, you can predict the number of new users through the supervised machine learning technique that is going to sign up for the service next month.
Unsupervised Machine Learning
Unsupervised learning is a machine learning methodology that does not use a training dataset to monitor models. Models, on the other hand, use the data to uncover secret patterns and perspectives. It is comparable to the learning that occurs in the human brain when learning new information. Unsupervised learning aims to uncover a dataset’s overall logic, group data based on similarities, and display the dataset in a compressed file.
Now let’s move on to understand how is machine learning used in data science.
7 Ways Machine Learning Is Used in Data Science
Classification
The classification method is a type of supervised machine learning as it predicts the class value. It means that when there’s an image of a girl or boy given, it would evaluate whether it’s a boy or girl in the image. And three possibilities will arise;
- The image carries a boy
- The image carries a girl
- The image carries neither a boy nor a girl
Logistic regression is the most basic classification algorithm. It appears to be a regression technique; however, it is not. Depending on one or more inputs, logistic regression calculates the likelihood of an event occurring.
For instance, in logistic regression two exam grades will be taken of a single student to predict if he will be admitted to the college or not, depending upon the probability. One of the grades will be considered as input and the other will be the output. As the estimate is the probable number, the output will be 1 and 0 when 1 will represent certainty. Then, it can be predicted the student will be admitted to the college if the probability will turn out to be more than 0.5, and if it will be less than that the student won’t get admission.
Clustering
The process of clustering is marked in the category of unsupervised machine learning. It is because of their method of grouping the observations according to similar characteristics. The output information is not utilized for the training process instead the algorithm is left to place the output. In this approach, visualizations are used to run the final solution.
A popular classification method is used which chooses the K center in the data, absolutely randomly; and even computes the center of each cluster in data points. Basically, ‘K’ in the K center refers to the number of clusters that are chosen by the user to be created.
When you explore clustering, you run into some useful algorithms, including cluster-hierarchical clustering, medium-medium shift clustering, and many others.
Regression
The branch of supervised machine learning covers regression approaches. They assist developers in explaining or forecasting a specific numeric value based on previous data sets, such as predicting a property’s price based on earlier pricing data for similar features.
The simplest method is linear regression, which involves modeling a dataset using mathematical equations of the line (y = m *x + b). With the support of different sets of data (x, y), a linear regression model can be built by measuring the slope and direction of a line that minimizes the total distance among all the data points and the line. To put it another way, the intercept (b) and slope (m) are calculated for a line that best approximates data’s observations.
Regression techniques can be simple like linear regression and at the same time, they can be complex like polynomial regression. But don’t get confused, begin with basic linear regression, master those techniques, and then move further.
Dimensionality Reduction
In this technique, the least important information or data, like, some unnecessary tables, is eliminated from the dataset. while working on it, it can be noted that datasets usually contain hundreds and thousands of columns, and there are a bunch of unimportant ones that need to be removed.
The column reduction is quite necessary. For instance, when the microchips are tested during the production process, they run through multiple tests and measurements, and many of them give unnecessary information. That is why it is so crucial to remove the unnecessary items through a dimensionality reduction algorithm, this way it would be easy to manage the dataset.
Principal Component Analysis (PCA) is known to be the most known method for reducing dimensionality. It limits the size of the function space by finding different innovative vectors that maximize the linear variance of the data. When the linear data correlations are high, PCA can considerably reduce the data size without sacrificing too much detail. In fact, you can easily determine the full scope of knowledge loss and make necessary adjustments.
Taking another known method as an example, i.e. t-stochastic, the t-stochastic integration of the neighbor (t-SNE) method, allows for a non-linear dimensionality reduction. T-SNE is most commonly used for data visualization, but it can also be used for machine learning tasks such as reducing the space between clustering and functions, to name a few.
Ensemble Methods
In the ensemble method, different models are assembled to get improved results. The result we get from the ensemble method is more accurate than the result from an individual model. This is the same as if you decide to manufacture your own bike. You find different parts of it that can perform certain tasks but when you assemble them in your bike, all the parts, and their functions will add up in the performance of your bike.
The ensemble method enables the system to get rid of the biasedness of an individual model. This is vital because any individual model may perform well in one circumstance and may fail in another situation. But in a combined/ assembled system, this fault can be eradicated. For example, a random forest algorithm is formed by combining many data sets of decision trees. In this way, the quality of any random forest can be predicted accurately then estimating any single decision tree.
Neural Networks and Deep Learning
Neural networking is concerned with non-linear data. Unlike linear and logistic regression of linear models, neural networks capture nonlinear data by the addition of parametric layers in the model.
The term deep learning is extracted from neurons network with various concealed layers and covers a wide range of architectures.
It is usually not easy to estimate the evolution of learning completely. Because the researchers and industry communities have now increased their effort in learning to double and create new procedures daily.
If you require maximum performance, there is a need for a huge amount of data and great computing powers for the technique of deep learning. Because this method lets us self-adjust many parameters with massive architectures. It is understandable that why the professionals of deep learning require powerful computers equipped with GPUs (graphic processing units)
In general, the method of deep learning is very successful in the domain of images, text, audio, and videos. PyTorch and Tensorflow are some software packages used for deep learning.
Transfer Learning
Transfer learning, as the name suggests, is the method of reusing a part of the neural network, previously formed and applying this to any new but similar task. This means, when a neural network is created by using data from the specific task, then you may ‘transfer’ any part from its layers and easily attach them with new layers that include the latest data. When you add a few layers, it becomes quicker for the neural network to accept and learn the new task.
For instance, let’s assume that a computer scientist is working for the retail sector. He had spent a lot of time, maybe years, in designing and building a premium quality model for any purpose, say to classify images as shirts, T-shirts, and polos. Now, a new task is assigned to him similar to that one, in which he had to categorize dresses like jeans, pants, dress pants, and cargos. Will he make a new model? Definitely, no, he will transfer new information into the previous model and effectively perform the task.
The major advantage of transfer learning is that it requires fewer data to create a neural network, unlike deep learning. That is why deep learning is expensive, time-taking and it is difficult to find data for it.
Now, let’s again look at the above example. Suppose for the design of T-shirts, the data scientist used ten hidden layers in the neural network. After some experiments, he observed that eight out of ten layers can be transferred and combined with new layers to design pants. The pant model will have nine hidden layers. The outputs and inputs of these two tasks are very different. But, by reusing/ transferring, he may summarize the related information for both tasks.
Conclusion
Now you have come to know about the 7 ways of How is machine learning used in data science?
These procedures are majorly used by data scientists and developers to overcome the daily challenges of business. These algorithms can also be used in creating decent machine learning programs to help in business.
References
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