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These 6 factors can be crucial when using Machine Learning for Applications.
While the term "machine learning" generally relates to understanding structures or patterns in data, it can also refer to a very diverse set of activities and techniques. Most of us have experienced machine learning in our everyday lives with natural language processing (Alexa, Siri), image recognition (Facebook, Pinterest), purchase recommendations (Amazon) and search optimization (Google). These approaches generally use many different types of algorithms (e.g., neural networks, decision trees, clustering, support vector machines, etc.)
Industrial operations, on the other hand, need more specialized approaches that can provide actionable insights to reduce downtime as well as improve throughput, operator safety, and product quality. The increased access to operational data, combined with the spread of computing, connectivity, and storage, has created the perfect environment for transforming industrial operations.
Finding patterns within the data is not always an easy task. It requires a lot of hard work because the patterns are often buried deep in the data. To make sure that you can find the patterns that you are looking for no matter how deep they are buried it is essential to have good knowledge of machine learning. The machine learning has the capacity to improve the power and responsiveness of the applications. It is essential for developers to have good knowledge of machine learning if they want to enhance the customer experience and provide best product recommendations and offer content that is highly personalized.
Here are six key factors that play an important role in successfully incorporating machine learning into their applications.
1. More data
The data always becomes more accurate when there is more data on the algorithm. You should avoid sub-sampling because it will help in getting the best data. There is an intuitive characterization regarding the prediction error when it comes to machine learning. If the data is limited it will not be able to support the complexity of the model required solving the problem.
2. Keep the given problem in mind
When you are selecting the machine learning method it is important to keep the given problem in mind. It plays an important role in determining the level of success. Make sure that you are using the algorithm that is most suitable for the characteristics of the data. It will help in getting the most accurate results.
3. Parameters of the method
The parameters of the method are not always the easy for the non-data scientists to understand it. When it comes to the modern machine learning algorithms there are always some knobs that need tweaking. Each algorithm has multiple parameter settings. The experience and intuition can help a lot in understanding the parameters.
4. The quality of the data
The quality of the machine learning depends on the quality of the data. In the collection of the data is not proper then it will be difficult to create machine learning models that are general and predictive. It is essential that the data is reviewed carefully. The data should allow the experts of the subject matter to get a proper insight into the data. It will also provide insight into data generation process which can help in identifying data quality issues that are connected to the features, records, sampling or values.
5. Features in the data
The impacts of predictability depend on understanding the features in the data. It is crucial for machine learning is to consider the raw data in a rich feature space. Knowing the features will help a lot with the learning processes.
6. Objective/loss function
The success of the application depends a lot on the choice of appropriate objective/loss function. The machine learning algorithms are mostly formulated as problems that are optimized. Adjusting the objective function according to the nature of the business is essential for machine learning success.