Summary
Python programming makes a huge contribution in AI- and ML-related domains. In this chapter, we have had only a glimpse of that. Quality data plays a very important role in ML. Whether collecting data via web scraping and storing it or providing scraped data on the fly to an ML model, prepared data is in demand. The better the quality of the data – and the more precise the data is – that we provide to ML algorithms, and for plotting charts, the more accurate results, visualizations, and descriptive plots we can expect.
We have now learned about ML concepts and various aspects of ML by exploring them. We have also learned how to implement ML models and collect the results, if required, from various processes. To summarize, we now have an overview of how to use scikit-learn and conduct sentiment analysis. ML is data-driven and quality data is a basic requirement for ML models to provide accuracy.
In the next chapter, we will learn about a few further steps...