Machine Learning in Python - Extras

Machine Learning applications are everywhere nowadays from Google Translate and NLP API,to Recommendation Systems used by YouTube,Netflix and Amazon,Udemy and more. As we have come to know, data science and machine learning is quite important to the success of any business and sector- so what does it take to build machine learning systems that works? In performing machine learning and data science projects, the normal workflow is that you have a problem you want to solve, hence you perform data collection,data preparation,feature engineering,model building and evaluation and then you deploy your model. However that is not all there is, there is a lot more to this life cycle. In this course we will be introducing to you some extra things that is not covered in most machine learning courses - such as working with pipelines specifically Scikit-learn pipelines, Spark Pipelines,etc and working with imbalanced dataset,etc We will also explore other ML frameworks beyond Scikit-learn,Tensorflow or Pytorch such as TuriCreate, Creme for online machine learning and more.