Get yourself trained on Python Machine Learning with this Online Training Python Machine Learning and Troubleshooting: 2-in-1.
Online Training Python Machine Learning and Troubleshooting: 2-in-1
Given the constantly increasing amounts of data they’re faced with, programmers and data scientists have to come up with better solutions to make machines smarter and reduce manual work along with finding solutions to the obstacles faced in between. Python comes to the rescue to craft better solutions and process them effectively. This comprehensive 2-in-1 course teaches you how to perform different machine learning tasks along with fixing common machine learning problems you face in your day-to-day tasks. You will also understand how the key machine learning algorithms can be trained for classification and regression. Further to get a complete hold on the technology, you will work with supervised and unsupervised learning. This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible. The first course, Learn Machine Learning in 3 Hours, begins with focusing on key machine learning algorithms and how they can be trained for classification and regression. You will then work with supervised and unsupervised learning to help to get to grips with both types of algorithms. By the end of this course, you will be adept at using the concepts and algorithms involved in machine learning. In the second course, Troubleshooting Python Machine Learning, we have systematically researched common machine learning problems documented online around data wrangling, debugging models such as Random Forests and SVMs, and visualizing tricky results. We leverage statistics from Stack Overflow, Medium, and GitHub to get a cross-section of what data scientists struggle with. We have collated for you the top issues, such as retrieving the most important regression features and explaining your results after clustering, and their corresponding solutions. We present these case studies in a problem-solution format, making it very easy for you to incorporate this into your knowledge. Taking this course will help you to precisely debug your models and research pipelines, so you can focus on pitching new ideas and not fixing old bugs. By the end of this Learning Path, you’ll learn to implement different machine learning tasks along with fixing common machine learning problems you face in your day-to-day tasks. Meet Your Expert(s): We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth: After taking a Physics degree at Oxford, Thomas Snell entered the Biophysics industry. Performing numerical simulation; from there, took a numerical simulation PhD in Geophysics. During his PhD, he developed a keen interest in machine learning, eventually founding two open source projects: a cryptocurrency trader and an evolutionary system to design quantum algorithms. Shortly after sharing these projects with the open source community, he worked as a data scientist while finishing his PhD, developing a system to cluster job data and predict career paths for groups of individuals. Colibri is a technology consultancy company founded in 2015 by James Cross and Ingrid Funie. The company works to help its clients navigate the rapidly changing and complex world of emerging technologies, with deep expertise in areas such as big data, data science, machine learning, and cloud computing. Over the past few years it has worked with some of the world’s largest and most prestigious companies, including a tier 1 investment bank, a leading management consultancy group, and one of the world’s most popular soft drinks companies, helping each of them make better sense of its data and process it in more intelligent ways. The company lives by its motto: Data -> Intelligence -> Action.Rudy Lai is the founder of QuantCopy, a sales acceleration startup using AI to write sales emails to prospects. By taking in leads from your pipelines, QuantCopy researches them online and generates sales emails from that data. It also has a suite of email automation tools to schedule, send, and track email performance – key analytics that all feed back into how our AI generated content. Prior to founding QuantCopy, Rudy ran HighDimension.IO, a machine learning consultancy, where he experienced firsthand the frustrations of outbound sales and prospecting. As a founding partner, he helped startups and enterprises with HighDimension.IO’s Machine-Learning-as-a-Service, allowing them to scale up data expertise in the blink of an eye.
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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.