Overview

Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy 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!Machine Learning (Complete course Overview)FoundationsIntroduction to Machine LearningIntroApplication of machine learning in different fields.Advantage of using Python libraries. (Python for machine learning).Python for AI & MLPython BasicsPython functions, packages, and routines.Working with Data structure, arrays, vectors & data frames. (Intro Based with some examples)Jupyter notebook- installation & functionPandas, NumPy, Matplotib, SeabornApplied StastisticsDescriptive statisticsProbability & Conditional ProbabilityHypothesis TestingInferential StatisticsProbability distributions - Types of distribution - Binomial, Poisson & Normal distributionMachine LearningSupervised LearningMultiple variable Linear regressionRegressionIntroduction to RegressionSimple linear regressionModel Evaluation in Regression ModelsEvaluation Metrics in Regression ModelsMultiple Linear RegressionNon-Linear RegressionNaïve bayes classifiersMultiple regressionK-NN classificationSupport vector machinesUnsupervised LearningIntro to ClusteringK-means clusteringHigh-dimensional clusteringHierarchical clusteringDimension Reduction-PCAClassificationIntroduction to ClassificationK-Nearest NeighboursEvaluation Metrics in ClassificationIntroduction to decision tressBuilding Decision TressInto Logistic regressionLogistic regression vs Linear RegressionLogistic Regression trainingSupport vector machineEnsemble TechniquesDecision TreesBaggingRandom ForestsBoostingFeaturization, Model selection & TuningFeature engineeringModel performanceML pipelineGrid search CVK fold cross-validationModel selection and tuningRegularising Linear modelsBootstrap samplingRandomized search CVRecommendation SystemsIntroduction to recommendation systemsPopularity based modelHybrid modelsContent based recommendation systemCollaborative filteringAdditional ModulesEDAPandas-profiling libraryTime series forecastingARIMA ApproachModel DeploymentKubernetesCapstone ProjectIf you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path.Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It's 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!Our Learner's Review: Excellent course. Precise and well-organized presentation. The complete course is filled with a lot of learning not only theoretical but also practical examples. Mr. Risabh is kind enough to share his practical experiences and actual problems faced by data scientists/ML engineers. The topic of "The ethics of deep learning" is really a gold nugget that everyone must follow. Thank you, 1stMentor and SelfCode Academy for this wonderful course.

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