This week we will discuss how to combine multiple binary classification algorithms of the same kind, CNN in our case, to create a rudimentary ensemble learning model, and the advantages of SERP data in ensemble learning.

Finally, the machine learning tool is open-sourced at serpapi/automatic-images-classifier-generator. Feel free to use, contribute, and have fun.

This week we will do the final step necessary to release the source code to the public, unit testing machine learning tool.

Python is easy. Machine Learning can get easy with Python. Relatively speaking, Machine Learning Models are easier to develop in Python. It also supports rich libraries with Machine Learning Algorithms for Natural Language Processing, Data Visualization, SERP and Preprocessing.

This week we will discuss about how to automate the creation of algorithms for machine learning models training. Just like previous weeks, optimization of training will not be the main concern. The aim is to get as close as we can get to automated machine learning.

The term refers to scalability in expanding image dataset to be used in Machine Learning training process, and expansion or retraining of the machine learning model in scale with minimal effort. AI solutions with large models need effective workflows achieve model development.