Get yourself trained on Machine Learning, Data with this Online Training Machine Learning, Data Science and Deep Learning with Python.
Online Training Machine Learning, Data Science and Deep Learning with Python
New!Updated for TensorFlow 1.10Machine Learning and artificial intelligence (AI)is everywhere; if you want to know how companies like Google, Amazon, and evenUdemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That’s just the average! And it’s not just about money – it’s interesting work too!If you’ve got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitionersin the tech industry – and prepare you for a move into this hot career path. This comprehensive machine learning tutorial includes over 80lectures spanning 12hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. Ill draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesnt.Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. Its then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference.You won’t find academic, deeply mathematical coverage of these algorithms in this course – the focus is on practical understanding and application of them. At the end, you’ll be given a final project to apply what you’ve learned!The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We’ll cover the machine learning, AI, and data mining techniques real employers are looking for, including:Deep Learning / Neural Networks (MLP’s, CNN’s, RNN’s) with TensorFlow and KerasSentiment analysisImage recognition and classificationRegression analysisK-Means ClusteringPrincipal Component AnalysisTrain/Test and cross validationBayesian MethodsDecision Trees and Random ForestsMultivariate RegressionMulti-Level ModelsSupport Vector MachinesReinforcement LearningCollaborative FilteringK-Nearest NeighborBias/Variance TradeoffEnsemble LearningTerm Frequency / Inverse Document FrequencyExperimental Design and A/B Tests …and much more! There’s also an entire section on machine learning with Apache Spark, which lets you scale up these techniques to “big data” analyzed on a computing cluster. And you’ll also get access to this course’s Facebook Group, where you can stay in touch with your classmates.If you’re new to Python, don’t worry – the course starts with a crash course. If you’ve done some programming before, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC’s; the sample code will also run on MacOS or Linux desktop systems, but I can’t provide OS-specific support for them.If youre a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry this course will teach you the basic techniques used by real-world industry data scientists. These are topics any successful technologist absolutely needs to know about, so what are you waiting for?Enroll now!”I started doing your course in 2015… Eventually I got interested and never thought that I will be working for corporate before a friend offered me this job. I am learning a lot which was impossible to learn in academia and enjoying it thoroughly. To me, your course is the one that helped me understand how to work with corporate problems. How to think to be a success in corporate AI research. I find you the most impressive instructor in ML, simple yet convincing.” – Kanad Basu, PhD
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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.