As technology companies continue to innovate, more and more organizations large and small are turning to machine-learning to punch up their portfolio of enterprise software and to make their operations more efficient. ML is the perfect tool to transform your business – if you know how to use it effectively.
But, the complexity of applying ML in a business environment can often be overwhelming; the technology is powerful, but it’s not always straightforward or easy to understand. Quite often, businesses find themselves in a quandary, as they’re unable to access crucial ‘ML knowledge’ or have the experience to properly apply ML.
The principles of ML are both robust and complex as they incorporate diverse core concepts like algorithms, data pre-processing, and feature engineering. At first glace, these principles can be intimidating, particularly for organizations with limited experience.
To start, let’s look at the core functions of ML. supervised machine learning (SML), unsupervised machine learning (UML) and reinforcement learning (RL) are the fundamental building blocks of a complete ML system. Supervised machine learning allows you to discriminate among different classes of objects on the basis of their known labels. UML focusses on recognizing the general underlying patterns and behaviors in the unlabeled data – it does not focus on distinguishing between different classes of objects. Reinforcement learning, on the other hand, helps you identify which actions should be taken in order to maximize rewards for the system.
Let us now look at the data pre-processing steps, beginning with data acquisition. Acquiring data is like building a strong foundation – without the appropriate data, you won’t be able to build the rest of your ML model. Following the data acquisition step, you’ll need to move on to data cleaning and Feature Engineering.
Data cleaning is the step that immediately follows data acquisition. It involves handling missing values, invalid values, and outliers in the data set. After you complete the cleaning, it is time to ensure your data is ready for ML. To do this, you’ll need to understand the types of data you’re dealing with and choose the most appropriate methods for their pre-processing.
Get your data ready for ML by feature engineering. Feature engineering involves creating additional input features from the existing data. The goal is to create a data set that can be used in ML models and can be used to measure the performance of a model.
Of course, no ML model is complete without an algorithm. In this step, you’ll need to choose the right algorithm to apply to your data. For instance, you could use linear programming, if you want a solution in which all the variables are continuous; or you could use a decision tree algorithm, if you want to consider making decisions based on multiple criteria.
Once you’ve chosen the right algorithms, the next step is to apply the ML in practice, and to create a ML-powered model based on your data. For this, you must consider how to tune the hyperparameters in your model. To help with the tuning, you should also assess the performance of your model using the data that was used in the training phase.
Finally, deploy the model. This includes creating an interface that allows users to interact with the model and the associated analytics.
As the technology continues to evolve, ML is becoming increasingly accessible and user-friendly. With the right knowledge and help, enterprises can fully exploit the power of ML for their business. By understanding the core concepts, the data pre-processing steps, and getting familiar with the algorithms, you can easily get started with ML. With the right guidance and preparation, you’ll be able to create ML systems that drive your enterprise operations forward.
