How can you use machine learning to create value in non-technical roles?

Take time and communicate with the specific department or person. Understand their role, operations, and benefits. Find the patterns between these and understand how they cooperate. Sometimes they need better algorithms, and sometimes need a higher rate. As you know the basics of AI, and the fact that the complexity of the solution depends on the complexity of the question, you can suggest solutions. Maybe a better regression model, maybe a deep learning process, or a much higher quality or quantity of data. It all depends on the problem specification you have done at first. Best, Houman

The first step is to have a good idea of the problem. Thoroughly understand the problem, then only we can think how the problem to be approached. For example, if you are working as a data scientist, then you could give insights on any data only if you understand the problem to be solved. So, if a company wants to increase its sales, then it need to analyse its previous data which could be achieved by machine learning.

The data exploration phase is crucial even for non-technical folks applying machine learning. One real experiance with my model results were way off. Investigation revealed quality issues in the data that required corrections - things like duplicate records, incorrect product categories, and misleading date formats that skewed the analysis. Cleaning and carefully examining the data revealed subtleties that the ML algorithm missed. Domain knowledge was key to noticing and fixing those problems. The same data prep rigor needed for quality analysis applies when leveraging ML.

The next step is to make the data to be more ready for further steps. In this step, we pre-process the data like clean and transform it into more structured format. There are certain tools like Excel helps us in this step. This could be used by any non-tech role person with ease .

The third step here is to choose model that gives accurate result to our training. There are many machine Learning models like classification, regression etc., which need to be selected based on the output we want. Choose a model which maximizes the accuracy and works with less errors.

The next step is to check the results given by the model trained. We can use various curves to show the result. There are certain metrics like precision, Recall which can be calculated and can be checked as, how well the model has performed.

Interpreting ML model results requires domain expertise even in non-technical roles. Domain knowledge is key to noticing issues. More broadly, evaluating model quality means going beyond standard measures like accuracy. You need to check if the results align with expectations, make sense given the problem context, and satisfy various constraints. This often benefits from human judgment to balance data-driven insights with real-world feasibility. ML augments but cannot replace human expertise even when assessing performance. Close collaboration is essential to create value responsibly.

The final step is to explain and communicate the information you gained from the model to your customers. You have explain how it is going to help the customers, how it maximizes the profit and how how much returns it produces on their investment.

Machine learning can bring value to non-technical roles by automating repetitive tasks and providing data-driven insights. For example, as a marketer can use ML to analyze customer data and identify behavioral patterns. This allowed me to create more personalized marketing campaigns that performed better. ML can also help with things like processing paperwork, analyzing legal documents, even suggesting creative ideas. The key is to clearly define the problem you want to solve, supply quality data, and interpret the ML outputs thoughtfully. With the right framing, ML can augment human capabilities in almost any role. There are challenges to using it responsibly, but the benefits are substantial.