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Machine learning is quite wide. Reading this article will change help you to understand the basic approach to solving real word problems. It will provide an overview of the most frequent challenges for which machine learning may be utilized. And, at the same time, provide you with a foundation for approaching future machine learning proof of concept initiatives.
First, we'll define certain terms.
What distinguishes machine learning, artificial intelligence, and data science?
Clearly, you can see that neither ML nor AI is a subset of Data Science, and Data Science is a subset of neither of these. There is much more to Data Science than just AI and ML.
There is much more to AI and ML than just Data Science. There are ML techniques used in Data Science for performing particular tasks and solving specific problems.
There are AI concepts — that are NOT ML techniques — employed in the field of Data Science.
Text mining (an intersection of AI and Data Science, but not ML) is an AI technology that uses Natural Language Processing to transform the raw (unstructured) text in documents and databases into normalized, structured data suitable for analysis or to drive Machine Learning algorithms.
We'll keep things essential to minimize misunderstanding. You have written this article.
6 measures to take before starting your next machine learning project
Definition of the problem – What business problem are we attempting to solve? What is the best way to express it as a machine learning problem?
Data – If machine learning is derived from data, what data do we have? How does it correspond to the problem definition? Is our data organized or unorganized? Streaming or static?
Evaluation — What constitutes success? Is a 95% accurate machine learning model sufficient?
Features – Which elements of our data will we utilize for our model? How does what we already know affect this?
Modeling – What model should you use? How can you make it better? How does it compare to other models?
Experimentation – Is there anything more we could try? Is our deployed model performing as expected? How