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And eCommerce companies have a ton of use cases where ML can help. The problem is, with more ML models and systems in production, you need to set up more infrastructure to reliably manage everything. And because of that, many companies decide to centralize this effort in an internal ML platform. But how to build it?
Introduction Machine Learning ( ML ) is revolutionising industries, from healthcare and finance to retail and manufacturing. As businesses increasingly rely on ML to gain insights and improve decision-making, the demand for skilled professionals surges. This growth signifies Python’s increasing role in ML and related fields.
Statistics Understand descriptive statistics (mean, median, mode) and inferential statistics (hypothesistesting, confidence intervals). Python’s rich ecosystem offers several libraries, such as Scikit-learn and TensorFlow, which simplify the implementation of ML algorithms.
Here is the tabular representation of the same: Technical Skills Non-technical Skills Programming Languages: Python, SQL, R Good written and oral communication Data Analysis: Pandas, Matplotlib, Numpy, Seaborn Ability to work in a team ML Algorithms: Regression Classification, Decision Trees, Regression Analysis Problem-solving capability Big Data: (..)
Accordingly, you need to make sense of the data that you derive from the various sources for which knowledge in probability, hypothesistesting, regression analysis is important. Statistical skills: having a clear idea regarding the procedures of different tasks requires you to have a thorough understanding of statistics.
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