How to start in Machine Learning World (and stay in time)- Part II

I hope the previous part (Part I) was useful for you or made any impact in your current life because I know how much effort requires start anything new and keep into, but the main reason of this kind of stories are remarke the importance about data science and machine learning in IT progress world where data and datasets are the main dish in menu. The world is changing and the focus in AI too. In this chat, Andrew Ng (Deep Learning specialist, Founder Landing AI and Deeplearning.AI) share the skills he see as fundamental to the next generation of machine learning practitioners (link chat video). He talk about the "old vision or approach" in model-centric: Passionately work on new algorithms, mathematical formulas, meta-architectures, convolutional layer stacking with normalization and all the study of inferential models and their components. But today most architectures are tested with optimal results, it is known that the application of a convolutional architecture is key to later achieve classification, object detection or segmentation, the power of LSTM (long short term memory) is known to language processing applications such as time series (real-time vehicle self-driving). So continuing on the path of algorithm-oriented improvements is no relevant.