Machine Learning in Business: 5 things a Data Science course won’t teach you

Machine Learning in Business: 5 things a Data Science course won’t teach you AI News, AI, AI tools, Guillaume Colley, Innovation, itinai.com, LLM, t.me/itinai, Towards Data Science - Medium **AI Solutions for Middle Managers** *Machine Learning in Business: 5 Practical Insights for Success* In the world of Applied Machine Learning, certain crucial aspects are often overlooked in formal Data Science education. These insights are aimed at helping junior and mid-level data scientists enhance their career and achieve better results in real-world business applications. **1. Thoughtful Target Selection** When applying Machine Learning to real-world business problems, it's crucial to focus on the right target. The target must represent a behavior, not just a data point. For example, in a clothing retailer scenario, the target could be binary (purchase or not), continuous (purchase amount), or trend-based (buying more than usual). **2. Dealing with Imbalanced Data** Real-life data is often imbalanced, and it's important to know how to handle this. Techniques like undersampling, oversampling, or choosing to do nothing can help mitigate the impact of imbalances. **3. Real-Life Testing** Retaining unseen (testing) data in its original distribution is essential to evaluate the real-life performance of a machine learning model. Avoid re-balancing testing data before splitting into training and testing data. **4. Meaningful Performance Metrics** Accuracy, ROC curve, and Area Under the ROC Curve may not be suitable for imbalanced class models. Precision and Area under the Precision and Recall Curve are more appropriate for scenarios where the behavior of interest is represented by the minority class. **5. Rethinking the Importance of Scores** Instead of using default cut-off values, it’s better to approach binary classification models as ranking tools. Additionally, presenting ranks or deciles to business stakeholders can provide a clearer understanding of performance across different models. Choosing the right target, proper measurement framework, and thoughtful communication strategies are essential for building successful machine learning models in a business context. *Spotlight on a Practical AI Solution:* Consider the AI Sales Bot from [itinai.com/aisalesbot](https://itinai.com/aisalesbot) designed to automate customer engagement 24/7 and manage interactions across all customer journey stages. *Discover how AI can redefine your sales processes and customer engagement. Explore solutions at [itinai.com](https://itinai.com).* **Useful Links:** - AI Lab in Telegram [@aiscrumbot](https://t.me/aiscrumbot) – free consultation - [Machine Learning in Business: 5 things a Data Science course won’t teach you](https://towardsdatascience.com/machine-learning-in-business-5-things-a-data-science-course-wont-teach-you-2f4b7f0e7d1d) - [Towards Data Science – Medium](https://towardsdatascience.com/) - Twitter – [@itinaicom](https://twitter.com/itinaicom) If you want to evolve your company with AI, stay competitive, and use Machine Learning in Business to your advantage, contact us at hello@itinai.com. For continuous insights into leveraging AI, stay tuned on our Telegram channel or Twitter.