Using ML To Solve Problems For A Manufacturing Company

Link to Colab Notebook: colab.research...
This video demonstrates how to apply machine learning (ML) to solve real-world problems in a manufacturing and distribution company. The speaker explains the two fundamental tasks of ML algorithms: prediction and classification. They then illustrate these concepts with three practical examples:
Particle Swarm Optimization (PSO): This optimization algorithm predicts the optimal number of workers and machines needed to maximize productivity and minimize costs. The speaker provides a simple visualization to showcase the results, highlighting the efficiency and cost-effectiveness of using ML compared to traditional enterprise software.
Monte Carlo Simulation: This probability-based algorithm simulates a common scenario in distribution: managing inventory with a 4-week lead time from the manufacturer. The simulation helps determine the optimal balance between minimizing inventory holding costs and customer wait times.
Random Forest Classifier: This classification algorithm analyzes a synthetic dataset of widgets to identify correlations between widget attributes (weight, size, feature, color) and their defectiveness. The speaker emphasizes the importance of selecting the right ML algorithm for each specific problem.
The video concludes by emphasizing the advantages of using specialized ML algorithms over general-purpose AI models like ChatGPT, particularly in terms of cost-effectiveness and efficiency. The speaker encourages viewers to like and subscribe for more content on practical ML applications.