You can find lots of information on Kaggle about competing, but it is difficult to know what is relevant and also very expensive in terms of time and effort – so we put all the essential knowledge into one book. Konrad: My favorite part is Chapter 12 on simulation competitions. Reinforcement learning is a field I have been getting into over the last few years – unlike computer vision or NLP, it has yet to reach wider appeal outside academic circles. It was an interesting and educational experience to try and distill what I have learned into a useful introduction to that fascinating domain. Luca: I enjoyed writing about the history of Kaggle and the professional opportunities it offers.

Luca Massaron is a data scientist and a research director specialized in multivariate statistical analysis, machine learning and customer insight with over a decade of experience in solving real world problems and in generating value for stakeholders by applying reasoning, statistics, data mining and algorithms. From being a pioneer of Web audience analysis in Italy to achieving the rank of top ten data scientist at competitions held by kaggle.com, he has always been passionate about everything regarding data and analysis and about demonstrating the potentiality of data-driven knowledge discovery to both experts and non-experts. Favouring simplicity over unnecessary sophistication, he believes that a lot can be achieved in data science just by doing the essential.

Konrad is a data science manager with experience stretching longer than he likes to ponder on. He holds a PhD in statistics from Vrije Universiteit Amsterdam, where he focused on problems of extreme dependency modeling in credit risk. He slowly moved from classic statistics towards machine learning and into the business applications world. Konrad worked in a variety of financial institutions on an array of data problems and visited all the stages of a data product cycle: from translating: business requirements ("what do they really need"), through data acquisition ("spreadsheets and flat files? He currently leads a central data science team at Adevinta.

Global corporations face a difficult task in predicting credit ratings. In this case, Massaron employs deep learning techniques to forecast credit ratings for global corporations. Luca Massaron, Senior Data Scientist, Kaggle Master and Google Developer Expert on ML, spoke at the Deep Learning Devcon 2021, organised by The Association of Data Scientists, earlier this month. In his talk on "Deep Learning for Credit Rating", Massaron covered topics, such as how deep learning techniques may be used to forecast credit ratings for worldwide business organisations; obligations are compared to the most widely used traditional machine-learning approaches, such as linear models and tree-based classifiers. According to Massaron, in an article titled "An artificial intelligence approach to shadow rating," the study's objective was to demonstrate that neural networks may be a more effective technique for calibrating and predicting ratings than other modelling approaches currently used in the banking industry. Luca proceeded further by explaining the significance of credit ratings.

"Never overlook a Kaggle competition when it doesn't award prizes/ranking, it may have even more interesting stuff (like xDeepFM) for you."- The session "Deep Learning For Tabular Data" was presented at the DLDC 2020, also known as the Deep Learning DevCon 2020 by Luca Massaron, who is Senior Data Scientist and Kaggle Master. Deep Learning DevCon 2020 is the conference of the year that is hosted by the Association of Data Scientists in partnership with Analytics India Magazine. Scheduled for 29th and 30th October, the DLDC conference brought together the leading experts as well as the best minds of deep learning and machine learning industry from around the globe. In this session, Massaron started discussing a brief on what deep learning and deep neural networks are and why it is relevant.