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It is the current availability of large data sets that made possible the widespread use of machine learning for solving business problems, but there are two main conditions for this technology to work – the quality of the original data and the debugged model that processes it. If they are done, then the machine learning implementation process can take as little as three steps. Machine learning step by step should be learned in details. Machine learning consulting services by Applandeo are great choice even for the most demandable users.
Gathering data
Decide on the task and find a process whose work pattern you can describe in detail. Remember, an algorithm does not replace a person and does not make decisions for you. Therefore, you should not automate those processes that require taking into account a large number of randomly occurring factors.
Delegate predictable things to machine learning: determining the type of document or the range of acceptable changes in sensor readings.
The machine learning process is well suited for optimizing routine tasks. In this case, its accuracy is close to maximum. Therefore, it is used to classify information about goods, since this process is not associated with the presence of many changing variables.
Every day we receive data, and every day algorithms process and classify it, helping to avoid mistakes in the early stages. This process has high speed and accuracy, since almost all incoming information is already familiar.
Preparing that data
For machine learning to work successfully, it is important to have so-called “role models”, and therefore you need to prepare enough of them in advance: for each group with which the algorithm will compare new samples. And the more correct and varied examples you use, the better the result will be.
Choosing a model
After describing the process in words, it remains to translate the algorithm into the form that will be familiar to the computer – for example, using one of the now popular programming languages - R or Python. And after the initial training of the model, check it for accuracy and select the optimal parameters.
Training
Machine learning steps by Applandeo include training. So, to introduce machine learning into business processes:
identify the process to be optimized. For example, sorting incoming documents;
find examples of data that will form the basis of training the algorithm;
create an algorithm. Popular programming languages such as Python are great places to experiment with in the field of artificial intelligence;
test and adjust the model for maximum accuracy. And don’t forget to regularly adapt it to the changes.
Evaluation
One of the best ways to learn math in analysis and machine learning is to build a simple neural network from scratch. You will use linear algebra to represent the network and mathematical analysis to optimize it. Specifically, you will create a gradient descent from scratch. No need to worry about the nuances of neural networks. It’s good if you just follow the instructions and write the code. And remember: the sooner you start working on real projects, the sooner you will master it. Either way, you can always revert to the syntax if you need to.
Using the Model and Presenting Results
Once you master the basic syntax and learn the basics of libraries, you can already start building projects yourself. Thanks to these projects, you will be able to learn about new things, as well as create a portfolio for further job search.
You can find many new things, but it is important to find those projects that will awaken the light in you. However, right before this happy moment of finding your dream job, you must learn to handle errors in your programs excellently.
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