Machine learning and deep learning are some of the hottest buzzwords right now. As more businesses utilize data to generate actionable insights, machine learning processes have made it possible to analyze this data with an astounding deal of detail and depth, yielding high-value insight that can really have a critical impact. The growing focus on artificial intelligence leads organizations all over the world to turn to machine learning in order to make the most high-impact use of data possible.
What is machine learning?
In the realm of artificial intelligence, machine learning is a subset that focuses on a particular goal, where a learning algorithm is used to help computers perform tasks without the need for specific programing or configuration. The analysis process using machine learning usually begins with defining a set of loose rules and feeding structured data to the algorithm, where it eventually learns, over time, to interpret, and analyze that data.
What is deep learning?
Deep learning is a further subset of machine learning, which is based on artificial neural networks. Deep learning usually takes more computing resources than machine learning, needs less human intervention, and can work with unstructured data, images, and videos among other data types. Deep learning also usually requires larger datasets to train and takes much longer to get fully trained.
How do we apply machine learning or deep learning?
If you want to employ machine learning or deep learning in your data analysis, one thing you must do is to prepare the data thoroughly before feeding it to the algorithm. This is crucial if you want accurate and business-critical results from your analysis. Moreover, it can make the inherent processes of machine learning and deep learning much easier for the learning algorithms. For machine learning and deep learning, it is not enough to just have good quality data. You also need to ensure that you use the right data scale, format, and data features.
Here are the steps of the process of data preparation for machine learning and deep learning.
While there might be some cases where including all the data you have available might make sense, in most cases it is a smarter approach to whittle down the data into the most meaningful chunks. A good place to start is to take a look at all the available data and to select the most meaningful or relevant subsets. This is directly related to the problem you are trying to solve or the question you are trying to answer with your machine learning analysis. The best data is the data that will help you answer these questions, so you need to make certain assumptions regarding your data at the very outset and record those assumptions for later reference.
Here are some questions to ask:
- What is the extent of data you have available to you? Where is it located? What are the formats?
- Do you have all the data that you want? Is some data not available that you needed to be available for the analysis? Can you derive or simulate that data?
- What data will not be central to solving the problem? Can you exclude that data before analysis?
- Do you have the adequate unstructured data, including all the relevant text, audio, video, and images for deep learning use cases?
With these questions, you can end up with a tailored selection that is prime for preparing.
This step involves putting the selected data into a meaningful format that makes it possible for the machine learning analysis to easily access and process the data. Here, you will be formatting, cleaning, and sampling the data.
Formatting – If the data is not in a format that is suitable for the further analysis, you need to transform it into a workable format. This will depend on your analysis use case and the kind of machine learning tool that you are planning to use. The data might be in a relational database and you might need it in a flat file format. The data might be in a proprietary format and you would need a more open format. This is the stage to make those changes so that you can present a familiar format for your analysis.
Cleaning – This is the most important step in order to maintain standards of data quality. You need to take a good look at the data to find out inconsistencies, missing data, and outliers. Any incomplete data instances that might adversely affect the result of your analysis will need to be removed from the dataset. If you have important data that is missing or incomplete, you can extrapolate in order to fill those gaps. If you are using any kind of personal or sensitive data or attributes, you can anonymize these at this stage. Look for any misleading trends or skews and take care of them. With all these processes complete, your data set will now be consistent.
Sampling – If you have more data than you need, it can result in longer running times for your analysis and a higher load on your hardware. It might delay your results significantly. In these kinds of cases, it can be a salient idea to fetch a small, representative sample from the data which can be processed faster and with considerable system load, while giving you similar results. You can also use sampling to prototype your analysis.
The next step is to transform the data. In fact, you might have to create several transformations of your data, refining the process until you come to the final version. Here are the common transformation tasks that you would have to perform, according to the needs of the use case:
If the processed data contains attributes that have a mix of different scales for quantities, it might be important to transform them to the same scale as many machine learning algorithms prefer this. Typically, this is done by assigning a smallest and largest value to a given feature and using a numerical representation for the scale. You can use processes like Gaussian normalization for this.
If your data has features that are complex and nuanced, it might be a lot easier for the machine learning algorithm to understand and parse those features if they are broken down into much simpler component parts. If you find data fields that have multiple dimensions or properties, you can decompose them into the relevant constituents to make the process easier and more intuitive for the analysis.
There might be data features that can be aggregated into a single feature to make the data more meaningful and relevant towards your problem. For instance, if you are using customer interaction data, there might be separate instances for all logins from a particular customer which you can aggregate into just the number of logins over that period of time. The extra data can then be discarded without consequence.
Some Key Terms
Generally, machine learning and deep learning are used for predictive modeling — making use of historical data to generate a learned prediction of new data. This mode of analysis can be broken down as a mathematical problem, which approximates a mapping function using a set of input variables to create output variables. This is known as functional approximation. The algorithm is meant to find the right mapping function to achieve this workload. These function approximation tasks are usually of two separate types: A classification problem concerns predicting a label, whereas a regression problem is about predicting a quantity or value.
In a classification problem, the output variables are called labels or categories. For a particular observation, the mapping function is supposed to predict the label. In such cases, examples need to be classified into one of multiple classes, and can have real or discrete input variables. A problem can have two classes (binary classification) or many (multi-class classification). A problem with multiple assigned classes is called a multi-label classification problem.
In a regression problem, the output variables are continuous, usually being a real value like an integer or floating-point value. Such problems require the prediction of a quantity. A problem with multiple input variables is called a multivariate regression problem, while one with input variables ordered by time is known as a time series forecasting problem.
Used in deep learning analysis, neural networks are usually applied to classification problems but can also be used, in special cases, for regression problems. This is a set of algorithms that aim to understand underlying relationships and connections in a dataset using a process that tries to mimic the operation of the human brain.