Get yourself trained on Machine Learning A-Z: with this Online Training Machine Learning A-Z: Hands-On Python & R In Data Science.
Online Training Machine Learning A-Z: Hands-On Python & R In Data Science
Interested in the field of Machine Learning?Then this course is for you!This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory,algorithms and coding libraries in a simple way.We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.This course isfun and exciting, but at the same time we dive deep into Machine Learning. It is structured the following way:Part 1 – Data PreprocessingPart 2 – Regression: Simple Linear Regression, Multiple Linear Regression,PolynomialRegression,SVR, Decision Tree Regression,Random Forest RegressionPart 3 – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification,RandomForest ClassificationPart 4 – Clustering: K-Means,Hierarchical ClusteringPart 5 – Association Rule Learning: Apriori,EclatPart 6 – Reinforcement Learning:Upper Confidence Bound,Thompson SamplingPart 7 – Natural Language Processing: Bag-of-words modelandalgorithms for NLPPart 8 – Deep Learning: Artificial Neural Networks,Convolutional Neural NetworksPart 9 – Dimensionality Reduction: PCA, LDA, Kernel PCAPart 10 – Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search,XGBoostMoreover, the course is packed with practical exercises which are based on real-lifeexamples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.And as a bonus, this course includes bothPython and Rcode templates which you can download and use on your own projects.
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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.