Description
Interested in Machine Learning, and Deep Learning and preparing for your interviews or research? Then, this course is for you! The course is designed to provide the fundamentals of machine learning and deep learning. It is targeted toward newbies, scholars, students preparing for interviews, or anyone seeking to hone the data science skills necessary. In this course, we will cover the basics of machine learning, and deep learning and cover a few case studies. This short course provides a broad introduction to machine learning, and deep learning. We will present a suite of tools for exploratory data analysis and machine learning modeling. We will get started with python and machine learning and provide case studies using keras and sklearn.### MACHINE LEARNING ###1.) Advanced Statistics and Machine LearningCovarianceEigen Value DecompositionPrincipal Component AnalysisCentral Limit TheoremGaussian DistributionTypes of Machine LearningParametric ModelsNon-parametric Models2.) Training Machine Learning ModelsSupervised Machine LearningRegressionClassificationLinear RegressionGradient DescentNormal EquationsLocally Weighted Linear RegressionRidge RegressionLasso RegressionOther classifier models in sklearnLogistic RegressionMapping non-linear functions using linear techniquesOverfitting and RegularizationSupport Vector MachinesDecision Trees3.) Artificial Neural NetworksForward PropagationBackward PropagationActivation functionsHyperparametersOverfittingDropout4.) Training Deep Neural NetworksDeep Neural NetworksConvolutional Neural NetworksRecurrent Neural Networks (GRU and LSTM)5.) Unsupervised LearningClustering (k-Means)6.) Implementation and Case StudiesGetting started with Python and Machine LearningCase Study - Keras Digit ClassifierCase Study - Load ForecastingSo what are you waiting for? Learn Machine Learning, and Deep Learning in a way that will enhance your knowledge and improve your career! Thanks for joining the course. I am looking forward to seeing you. let's get started!
