Mar 28 2023 The Complete Visual Guide To Machine Learning & Data Science BaDshaH LEARNING / e-learning - Tutorials 09:09 0 Published 3/2023MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 3.20 GB | Duration: 8h 51mExplore Data Science & Machine Learning topics with simple, step-by-step demos and user-friendly Excel models (NO code!) What you'll learnBuild foundational machine learning & data science skills WITHOUT writing complex codePlay with interactive, user-friendly Excel models to learn how machine learning techniques actually workEnrich datasets using feature engineering techniques like one-hot encoding, scaling and discretizationPredict categorical outcomes using classification models like K-nearest neighbors, naïve bayes, and decision treesBuild accurate forecasts and projections using linear and non-linear regression modelsApply powerful techniques for clustering, association mining, outlier detection, and dimensionality reductionLearn how to select and tune models to optimize performance, reduce bias, and minimize driftExplore unique, hands-on case studies to simulate how machine learning can be applied to real-world casesRequirementsThis is a beginner-friendly course (no prior knowledge or math/stats background required)We'll use Microsoft Excel (Office 365) for some course demos, but participation is optionalDescriptionThis course is for everyday people looking for an intuitive, beginner-friendly introduction to the world of machine learning and data science.Build confidence with guided, step-by-step demos, and learn foundational skills from the ground up. Instead of memorizing complex math or learning a new coding language, we'll break down and explore machine learning techniques to help you understand exactly how and why they work.Follow along with simple, visual examples and interact with user-friendly, Excel-based models to learn topics like linear and logistic regression, decision trees, KNN, naïve bayes, hierarchical clustering, sentiment analysis, and more – without writing a SINGLE LINE of code.This course combines 4 best-selling courses from Maven Analytics into a single masterclass:PART 1: Univariate & Multivariate ProfilingPART 2: Classification ModelingPART 3: Regression & ForecastingPART 4: Unsupervised LearningPART 1: Univariate & Multivariate ProfilingIn Part 1 we'll introduce the machine learning workflow and common techniques for cleaning and preparing raw data for analysis. We'll explore univariate analysis with frequency tables, histograms, kernel densities, and profiling metrics, then dive into multivariate profiling tools like heat maps, violin & box plots, scatter plots, and correlation:Section 1: Machine Learning Intro & LandscapeMachine learning process, definition, and landscapeSection 2: Preliminary Data QAVariable types, empty values, range & count calculations, left/right censoring, etc.Section 3: Univariate ProfilingHistograms, frequency tables, mean, median, mode, variance, skewness, etc.Section 4: Multivariate ProfilingViolin & box plots, kernel densities, heat maps, correlation, etc.Throughout the course, we'll introduce real-world scenarios to solidify key concepts and simulate actual data science and business intelligence cases. You'll use profiling metrics to clean up product inventory data for a local grocery, explore Olympic athlete demographics with histograms and kernel densities, visualize traffic accident frequency with heat maps, and more.PART 2: Classification ModelingIn Part 2 we'll introduce the supervised learning landscape, review the classification workflow, and address key topics like dependent vs. independent variables, feature engineering, data splitting and overfitting. From there we'll review common classification models like K-Nearest Neighbors (KNN), Naïve Bayes, Decision Trees, Random Forests, Logistic Regression and Sentiment Analysis, and share tips for model scoring, selection, and optimization:Section 1: Intro to ClassificationSupervised learning & classification workflow, feature engineering, splitting, overfitting & underfittingSection 2: Classification ModelsK-nearest neighbors, naïve bayes, decision trees, random forests, logistic regression, sentiment analysisSection 3: Model Selection & TuningHyperparameter tuning, imbalanced classes, confusion matrices, accuracy, precision & recall, model driftYou'll help build a simple recommendation engine for Spotify, analyze customer purchase behavior for a retail shop, predict subscriptions for an online travel company, extract sentiment from a sample of book reviews, and more.PART 3: Regression & ForecastingIn Part 3 we'll introduce core building blocks like linear relationships and least squared error, and practice applying them to univariate, multivariate, and non-linear regression models. We'll review diagnostic metrics like R-squared, mean error, F-significance, and P-Values, then use time-series forecasting techniques to identify seasonality, predict nonlinear trends, and measure the impact of key business decisions using intervention analysis:Section 1: Intro to RegressionSupervised learning landscape, regression vs. classification, prediction vs. root-cause analysisSection 2: Regression Modeling 101Linear relationships, least squared error, univariate & multivariate regression, nonlinear transformationSection 3: Model DiagnosticsR-squared, mean error, null hypothesis, F-significance, T & P-values, homoskedasticity, multicollinearitySection 4: Time-Series ForecastingSeasonality, auto correlation, linear trending, non-linear models, intervention analysisYou'll see how regression analysis can be used to estimate property prices, forecast seasonal trends, predict sales for a new product launch, and even measure the business impact of a new website design.PART 4: Unsupervised LearningIn Part 4 we'll explore the differences between supervised and unsupervised machine learning and introduce several common unsupervised techniques, including cluster analysis, association mining, outlier detection and dimensionality reduction. We'll break down each model in simple terms and help you build an intuition for how they work, from K-means and apriori to outlier detection, principal component analysis, and more:Section 1: Intro to Unsupervised Machine LearningUnsupervised learning landscape & workflow, common unsupervised techniques, feature engineeringSection 2: Clustering & SegmentationClustering basics, K-means, elbow plots, hierarchical clustering, dendogramsSection 3: Association MiningAssociation mining basics, apriori, basket analysis, minimum support thresholds, markov chainsSection 4: Outlier DetectionOutlier detection basics, cross-sectional outliers, nearest neighbors, time-series outliers, residual distributionSection 5: Dimensionality ReductionDimensionality reduction basics, principle component analysis (PCA), scree plots, advanced techniquesYou'll see how K-means can help identify customer segments, how apriori can be used for basket analysis and recommendation engines, and how outlier detection can spot anomalies in cross-sectional or time-series datasets.__________Ready to dive in? Join today and get immediate, LIFETIME access to the following:9+ hours of on-demand videoML Foundations ebook (350+ pages)Downloadable Excel project filesExpert Q&A forum30-day money-back guaranteeIf you're an analyst or aspiring data professional looking to build the foundation for a successful career in machine learning or data science, you've come to the right place.Happy learning!-Josh & ChrisOverviewSection 1: Getting StartedLecture 1 Course Structure & OutlineLecture 2 READ ME: Important Notes for New StudentsLecture 3 DOWNLOAD: Course ResourcesLecture 4 Setting ExpectationsSection 2: PART 1: QA & Data ProfilingLecture 5 Part 1: QA & Data ProfilingSection 3: Intro to the ML LandscapeLecture 6 Intro to Machine LearningLecture 7 When is ML the right fit?Lecture 8 The Machine Learning ProcessLecture 9 The Machine Learning LandscapeSection 4: Preliminary Data QALecture 10 IntroductionLecture 11 Why QA?Lecture 12 Variable TypesLecture 13 Empty ValuesLecture 14 Range CalculationsLecture 15 Count CalculationsLecture 16 Left & Right Censored DataLecture 17 Table StructureLecture 18 CASE STUDY: Preliminary QALecture 19 BEST PRACTICES: Preliminary QASection 5: Univariate ProfilingLecture 20 IntroductionLecture 21 Categorical VariablesLecture 22 DiscretizationLecture 23 Nominal vs. OrdinalLecture 24 Categorical DistributionsLecture 25 Numerical VariablesLecture 26 Histograms & Kernel DensitiesLecture 27 CASE STUDY: HistogramsLecture 28 Normal DistributionLecture 29 CASE STUDY: Normal DistributionLecture 30 Univariate Data ProfilingLecture 31 ModeLecture 32 MeanLecture 33 MedianLecture 34 PercentileLecture 35 VarianceLecture 36 Standard DeviationLecture 37 SkewnessLecture 38 BEST PRACTICES: Univariate ProfilingSection 6: Multivariate ProfilingLecture 39 IntroductionLecture 40 Categorical-CategoricalLecture 41 CASE STUDY: Heat MapsLecture 42 Categorical-NumericalLecture 43 Multivariate Kernel DensitiesLecture 44 Violin PlotsLecture 45 Box PlotsLecture 46 Limitations of Categorical DistributionsLecture 47 Numerical-NumericalLecture 48 CorrelationLecture 49 Correlation vs. CausationLecture 50 Visualizing Third DimensionLecture 51 CASE STUDY: CorrelationLecture 52 BEST PRACTICES: Multivariate ProfilingLecture 53 Looking Ahead to Part 2Section 7: PART 2: Classification ModelingLecture 54 Part 2: Classification ModelingSection 8: Intro to ClassificationLecture 55 Supervised vs. Unsupervised LearningLecture 56 Classification vs. RegressionLecture 57 RECAP: Key ConceptsLecture 58 Classification 101Lecture 59 Classification WorkflowLecture 60 Feature EngineeringLecture 61 Data SplittingLecture 62 OverfittingSection 9: Classification ModelsLecture 63 Common Classification ModelsLecture 64 Intro to K-Nearest Neighbors (KNN)Lecture 65 KNN ExamplesLecture 66 CASE STUDY: KNNLecture 67 Intro to Naïve BayesLecture 68 Naïve Bayes | Frequency TablesLecture 69 Naïve Bayes | Conditional ProbabilityLecture 70 CASE STUDY: Naïve BayesLecture 71 Intro to Decision TreesLecture 72 Decision Trees | Entropy 101Lecture 73 Entropy & Information GainLecture 74 Decision Tree ExamplesLecture 75 Random ForestsLecture 76 CASE STUDY: Decision TreesLecture 77 Intro to Logistic RegressionLecture 78 Logistic Regression ExampleLecture 79 False Positives vs. False NegativesLecture 80 Logistic Regression EquationLecture 81 The Likelihood FunctionLecture 82 Multivariate Logistic RegressionLecture 83 CASE STUDY: Logistic RegressionLecture 84 Intro to Sentiment AnalysisLecture 85 Cleaning Text DataLecture 86 "Bag of Words" AnalysisLecture 87 CASE STUDY: Sentiment AnalysisSection 10: Model Selection & TuningLecture 88 Intro to Selection & TuningLecture 89 HyperparametersLecture 90 Imbalanced ClassesLecture 91 Confusion MatrixLecture 92 Accuracy, Precision & RecallLecture 93 Multi-class Confusion MatrixLecture 94 Multi-class ScoringLecture 95 Model SelectionLecture 96 Model DriftLecture 97 Looking ahead to Part 3Section 11: PART 3: Regression & ForecastingLecture 98 Part 3: Regression & ForecastingSection 12: Intro to RegressionLecture 99 Supervised vs. Unsupervised LearningLecture 100 RECAP: Key ConceptsLecture 101 Regression 101Lecture 102 Feature Engineering for RegressionLecture 103 Prediction vs. Root-Cause AnalysisSection 13: Regression Modeling 101Lecture 104 Intro to Regression ModelingLecture 105 Linear RelationshipsLecture 106 Least Squared ErrorLecture 107 Univariate Linear RegressionLecture 108 CASE STUDY: Univariate Linear RegressionLecture 109 Multiple Linear RegressionLecture 110 Non-Linear RegressionLecture 111 CASE STUDY: Non-Linear RegressionSection 14: Model DiagnosticsLecture 112 Intro to Model DiagnosticsLecture 113 Sample Model OutputLecture 114 R-SquaredLecture 115 Mean Error Metrics (MSE, MAE, MAPE)Lecture 116 HomoskedasticityLecture 117 Null HypothesisLecture 118 F-SignificanceLecture 119 T-Values & P-ValuesLecture 120 MulticollinearityLecture 121 Variance Inflation FactorLecture 122 RECAP: Sample Model OutputSection 15: Time-Series ForecastingLecture 123 Intro to ForecastingLecture 124 SeasonalityLecture 125 Auto Correlation FunctionLecture 126 CASE STUDY: Seasonality with ACFLecture 127 One-Hot EncodingLecture 128 CASE STUDY: Seasonality with One-Hot EncodingLecture 129 Linear TrendingLecture 130 CASE STUDY: Seasonality with Linear TrendLecture 131 SmoothingLecture 132 CASE STUDY: SmoothingLecture 133 Non-Linear TrendsLecture 134 CASE STUDY: Non-Linear TrendLecture 135 Intervention AnalysisLecture 136 CASE STUDY: Intervention AnalysisLecture 137 Looking Ahead to Part 4Section 16: PART 4: Unsupervised LearningLecture 138 Part 4: Unsupervised LearningSection 17: Intro to Unsupervised MLLecture 139 Supervised vs. Unsupervised LearningLecture 140 Common Unsupervised TechniquesLecture 141 Unsupervised ML WorkflowLecture 142 RECAP: Feature EngineeringLecture 143 KEY TAKEAWAYS: Intro to Unsupervised MLSection 18: Clustering & SegmentationLecture 144 IntroductionLecture 145 Clustering BasicsLecture 146 Intro to K-MeansLecture 147 WSS & Elbow PlotsLecture 148 K-Means FAQsLecture 149 CASE STUDY: K-MeansLecture 150 Intro to Hierarchical ClusteringLecture 151 Anatomy of a DendrogramLecture 152 Hierarchical Clustering FAQsLecture 153 KEY TAKEAWAYS: Clustering & SegmentationSection 19: Association Mining & Basket AnalysisLecture 154 IntroductionLecture 155 Association Mining BasicsLecture 156 The Apriori AlgorithmLecture 157 Basket Analysis ExamplesLecture 158 Minimum Support ThresholdsLecture 159 Infrequent ItemsetsLecture 160 Multiple Item SetsLecture 161 CASE STUDY: AprioriLecture 162 Markov ChainsLecture 163 CASE STUDY: Markov ChainsLecture 164 KEY TAKEAWAYS: Association MiningSection 20: Outlier DetectionLecture 165 IntroductionLecture 166 Outlier Detection BasicsLecture 167 Cross-Sectional OutliersLecture 168 Cross-Sectional Outlier ExampleLecture 169 CASE STUDY: Cross-Sectional OutlierLecture 170 Time-Series OutliersLecture 171 Time-Series Outlier ExampleLecture 172 KEY TAKEAWAYS: Outlier DetectionSection 21: Dimensionality ReductionLecture 173 IntroductionLecture 174 Dimensionality Reduction BasicsLecture 175 Principle Component AnalysisLecture 176 PCA ExampleLecture 177 Interpreting ComponentsLecture 178 Scree PlotsLecture 179 Advanced TechniquesLecture 180 KEY TAKEAWAYS: Dimensionality ReductionSection 22: Wrapping UpLecture 181 Series ConclusionLecture 182 BONUS LESSONAnyone looking to learn the foundations of machine learning through interactive, beginner-friendly demos,Data Analysts or BI experts looking to transition into data science or build a fundamental understanding of machine learning,R or Python users seeking a deeper understanding of the models and algorithms behind their code,Excel users who want to learn and apply powerful tools for predictive analyticsHomepagehttps://www.udemy.com/course/visual-guide-to-machine-learning/Download From 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