Get yourself trained on Python Machine Learning with this Online Training Python Machine Learning Tips, Tricks, and Techniques.
Online Training Python Machine Learning Tips, Tricks, and Techniques
Machine learning allows us to interpret data structures and fit that data into models to identify patterns and make predictions. Python makes this easier with its huge set of libraries that can be easily used for machine learning. In this course, you will learn from a top Kaggle master to upgrade your Python skills with the latest advancements in Python.It is essential to keep upgrading your machine learning skills as there are immense advancements taking place every day. In this course, you will get hands-on experience of solving real problems by implementing cutting-edge techniques to significantly boost your Python Machine Learning skills and, as a consequence, achieve optimized results in almost any project you are working on.Each technique we cover is itself enough to improve your results. However; combining them together is where the real magic is. Throughout the course, you will work on real datasets to increase your expertise and keep adding new tools to your machine learning toolbox.By the end of this course, you will know various tips, tricks, and techniques to upgrade your machine learning algorithms to reduce common problems, all the while building efficient machine learning models.About the AuthorValeriy Babushkin has done an M. Sc. and has 5+ years’ experience in industrial data science and academia. He is a Kaggle competition master and a 2018 IEEE SP Cup finalist. He has been a Data Science Team Lead at Yandex (the largest search engine in Russia; it outperforms Google) and runs an online taxi service (he acquired Uber in Russia and 15 other countries) and the biggest e-commerce platform in Russia.He was also a Head of Data Science at Monetha. Monetha is creating a universal, transferable, immutable trust, and reputation system combined with a payment solution. Finally, he is decentralized and empowered by the Ethereum Blockchain.
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As a society, we spend hundreds of billions of dollars measuring the return on our financial assets. Yet, at the same time, we still haven’t found convincing ways of measuring the return on our investments in developing people.
And I get it: If my bank account pays me 1% a year, I can measure it to the penny. We’ve been collectively trained to expect neat and precise ROI calculations on everything, so when it’s applied to something as seemingly squishy as how effectively people are learning in the workplace, the natural inclination is to throw up our hands and say it can’t be done. But we need to figure this out. In a world where skills beat capital, the winners and losers of the next 30 years will be determined by their ability to attract and develop great talent.
Fortunately, corporate learning & development (L&D), like most business functions, is evolving quickly. We can embrace some level of ambiguity and have rigor when measuring the ROI of learning. It just might look a little different than an M.B.A. would expect to see in an Excel model.