Get yourself trained on Machine Learning Mastery with this Online Training Machine Learning Mastery (Integrated Theory+Practical HW).
Online Training Machine Learning Mastery (Integrated Theory+Practical HW)
Data Science is a multidisciplinary field that deals with the study of data. Data scientists have the ability to take data, understand it, process it, and extract information from it, visualize the information and communicate it. Data scientists are well-versed in multiple disciplines including mathematics, statistics, economics, business, and computer science, as well as the unique ability to ask interesting and challenging data questions based on formal or informal theory to spawn valuable and meticulous insights. This course introduces students to this rapidly growing field and equips them with its most fundamental principles, tools, and mindset.Students will learn the theories, techniques, and tools they need to deal with various datasets. We will start with Regression, one of the basic models, and progress as we evaluate and assessing different models. We will start from the initial stages of data science and advance to higher levels where students can write their own algorithm from scratch to build a model. We will see end to end and work with practical datasets at the end of each module. Students will be issued with tutorials and explanation of all the exercises to help you learn faster and enable you to link theory using hands on exercises.This course teaches advanced theory including some mathematics with practical exercises to promote deeper understanding.Learning OutcomesAt the end of the course the students will:Have an in-depth understanding of the concepts of Machine LearningBe able to grasp, understand, and write machine learning code from scratchUse Builtin Libraries available to build machine learning modelsBe able to analyze, build, and assess models on any datasetBe able to interpret and understand the black box behind modelUnderstand the applications of data science by exhibiting the ability to work on different datasets and interpreting them.What is the working system of this course?Strong concepts and theory linked to practical at the end of each moduleEasy Lectures for those starting from scratchIllustration and examplesHands-on exercises with tutorials Detailed explanations of how models workWhat does this course cover?Introduction to machine learning: Overview of supervised and unsupervised learningRegression from scratch – Gradient Descent, Cost Function , ModellingUsing Machine learning builtin libraryFeature ScalingMultivariate RegressionPolynomial RegressionOver-fitting, Under-fitting and GeneralizationBias Variance TradeoffCross Validation Strategy and Hyper-parameter tuningGrid SearchLearning CurvesDecision Trees and introduction to other algorithms including neural networkExercises after each moduleAfter completing the course, you will have enough knowledge and confidence to code machine learning algorithms from scratch and to use built-in library. This course is for all interested in learning data science and machine learning, there is no such pre req. This course is different from other courses in a manner that it teaches to code algorithms and also exposes you to the mathematics behind machine learning, this even includes tutorials at the end of each module so that students can do side by side practice with the instructor. It exposes you to practical real world datasets to work on and get started with new problems.
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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.