The Complete Visual Guide To Machine Learning & Data Science

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reose Profesionál Založený: 22 apríl 2014 Príspevky: 120792 Poďakovaní 1 v 1 Príspevkoch	Zaslal: St marec 29, 2023 1:47 am    Predmet: The Complete Visual Guide To Machine Learning & Data Sci Free Download The Complete Visual Guide To Machine Learning & Data Science Published 3/2023 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 3.20 GB | Duration: 8h 51m Explore Data Science & Machine Learning topics with simple, step-by-step demos and user-friendly Excel models (NO code!) Free Download What you'll learn Build foundational machine learning & data science skills WITHOUT writing complex code Play with interactive, user-friendly Excel models to learn how machine learning techniques actually work Enrich datasets using feature engineering techniques like one-hot encoding, scaling and discretization Predict categorical outcomes using classification models like K-nearest neighbors, naïve bayes, and decision trees Build accurate forecasts and projections using linear and non-linear regression models Apply powerful techniques for clustering, association mining, outlier detection, and dimensionality reduction Learn how to select and tune models to optimize performance, reduce bias, and minimize drift Explore unique, hands-on case studies to simulate how machine learning can be applied to real-world cases Requirements This 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 optional Description This 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 & Chris Overview Section 1: Getting Started Lecture 1 Course Structure & Outline Lecture 2 READ ME: Important Notes for New Students Lecture 3 DOWNLOAD: Course Resources Lecture 4 Setting Expectations Section 2: PART 1: QA & Data Profiling Lecture 5 Part 1: QA & Data Profiling Section 3: Intro to the ML Landscape Lecture 6 Intro to Machine Learning Lecture 7 When is ML the right fit? Lecture 8 The Machine Learning Process Lecture 9 The Machine Learning Landscape Section 4: Preliminary Data QA Lecture 10 Introduction Lecture 11 Why QA? Lecture 12 Variable Types Lecture 13 Empty Values Lecture 14 Range Calculations Lecture 15 Count Calculations Lecture 16 Left & Right Censored Data Lecture 17 Table Structure Lecture 18 CASE STUDY: Preliminary QA Lecture 19 BEST PRACTICES: Preliminary QA Section 5: Univariate Profiling Lecture 20 Introduction Lecture 21 Categorical Variables Lecture 22 Discretization Lecture 23 Nominal vs. Ordinal Lecture 24 Categorical Distributions Lecture 25 Numerical Variables Lecture 26 Histograms & Kernel Densities Lecture 27 CASE STUDY: Histograms Lecture 28 Normal Distribution Lecture 29 CASE STUDY: Normal Distribution Lecture 30 Univariate Data Profiling Lecture 31 Mode Lecture 32 Mean Lecture 33 Median Lecture 34 Percentile Lecture 35 Variance Lecture 36 Standard Deviation Lecture 37 Skewness Lecture 38 BEST PRACTICES: Univariate Profiling Section 6: Multivariate Profiling Lecture 39 Introduction Lecture 40 Categorical-Categorical Lecture 41 CASE STUDY: Heat Maps Lecture 42 Categorical-Numerical Lecture 43 Multivariate Kernel Densities Lecture 44 Violin Plots Lecture 45 Box Plots Lecture 46 Limitations of Categorical Distributions Lecture 47 Numerical-Numerical Lecture 48 Correlation Lecture 49 Correlation vs. Causation Lecture 50 Visualizing Third Dimension Lecture 51 CASE STUDY: Correlation Lecture 52 BEST PRACTICES: Multivariate Profiling Lecture 53 Looking Ahead to Part 2 Section 7: PART 2: Classification Modeling Lecture 54 Part 2: Classification Modeling Section 8: Intro to Classification Lecture 55 Supervised vs. Unsupervised Learning Lecture 56 Classification vs. Regression Lecture 57 RECAP: Key Concepts Lecture 58 Classification 101 Lecture 59 Classification Workflow Lecture 60 Feature Engineering Lecture 61 Data Splitting Lecture 62 Overfitting Section 9: Classification Models Lecture 63 Common Classification Models Lecture 64 Intro to K-Nearest Neighbors (KNN) Lecture 65 KNN Examples Lecture 66 CASE STUDY: KNN Lecture 67 Intro to Naïve Bayes Lecture 68 Naïve Bayes | Frequency Tables Lecture 69 Naïve Bayes | Conditional Probability Lecture 70 CASE STUDY: Naïve Bayes Lecture 71 Intro to Decision Trees Lecture 72 Decision Trees | Entropy 101 Lecture 73 Entropy & Information Gain Lecture 74 Decision Tree Examples Lecture 75 Random Forests Lecture 76 CASE STUDY: Decision Trees Lecture 77 Intro to Logistic Regression Lecture 78 Logistic Regression Example Lecture 79 False Positives vs. False Negatives Lecture 80 Logistic Regression Equation Lecture 81 The Likelihood Function Lecture 82 Multivariate Logistic Regression Lecture 83 CASE STUDY: Logistic Regression Lecture 84 Intro to Sentiment Analysis Lecture 85 Cleaning Text Data Lecture 86 "Bag of Words" Analysis Lecture 87 CASE STUDY: Sentiment Analysis Section 10: Model Selection & Tuning Lecture 88 Intro to Selection & Tuning Lecture 89 Hyperparameters Lecture 90 Imbalanced Classes Lecture 91 Confusion Matrix Lecture 92 Accuracy, Precision & Recall Lecture 93 Multi-class Confusion Matrix Lecture 94 Multi-class Scoring Lecture 95 Model Selection Lecture 96 Model Drift Lecture 97 Looking ahead to Part 3 Section 11: PART 3: Regression & Forecasting Lecture 98 Part 3: Regression & Forecasting Section 12: Intro to Regression Lecture 99 Supervised vs. Unsupervised Learning Lecture 100 RECAP: Key Concepts Lecture 101 Regression 101 Lecture 102 Feature Engineering for Regression Lecture 103 Prediction vs. Root-Cause Analysis Section 13: Regression Modeling 101 Lecture 104 Intro to Regression Modeling Lecture 105 Linear Relationships Lecture 106 Least Squared Error Lecture 107 Univariate Linear Regression Lecture 108 CASE STUDY: Univariate Linear Regression Lecture 109 Multiple Linear Regression Lecture 110 Non-Linear Regression Lecture 111 CASE STUDY: Non-Linear Regression Section 14: Model Diagnostics Lecture 112 Intro to Model Diagnostics Lecture 113 Sample Model Output Lecture 114 R-Squared Lecture 115 Mean Error Metrics (MSE, MAE, MAPE) Lecture 116 Homoskedasticity Lecture 117 Null Hypothesis Lecture 118 F-Significance Lecture 119 T-Values & P-Values Lecture 120 Multicollinearity Lecture 121 Variance Inflation Factor Lecture 122 RECAP: Sample Model Output Section 15: Time-Series Forecasting Lecture 123 Intro to Forecasting Lecture 124 Seasonality Lecture 125 Auto Correlation Function Lecture 126 CASE STUDY: Seasonality with ACF Lecture 127 One-Hot Encoding Lecture 128 CASE STUDY: Seasonality with One-Hot Encoding Lecture 129 Linear Trending Lecture 130 CASE STUDY: Seasonality with Linear Trend Lecture 131 Smoothing Lecture 132 CASE STUDY: Smoothing Lecture 133 Non-Linear Trends Lecture 134 CASE STUDY: Non-Linear Trend Lecture 135 Intervention Analysis Lecture 136 CASE STUDY: Intervention Analysis Lecture 137 Looking Ahead to Part 4 Section 16: PART 4: Unsupervised Learning Lecture 138 Part 4: Unsupervised Learning Section 17: Intro to Unsupervised ML Lecture 139 Supervised vs. Unsupervised Learning Lecture 140 Common Unsupervised Techniques Lecture 141 Unsupervised ML Workflow Lecture 142 RECAP: Feature Engineering Lecture 143 KEY TAKEAWAYS: Intro to Unsupervised ML Section 18: Clustering & Segmentation Lecture 144 Introduction Lecture 145 Clustering Basics Lecture 146 Intro to K-Means Lecture 147 WSS & Elbow Plots Lecture 148 K-Means FAQs Lecture 149 CASE STUDY: K-Means Lecture 150 Intro to Hierarchical Clustering Lecture 151 Anatomy of a Dendrogram Lecture 152 Hierarchical Clustering FAQs Lecture 153 KEY TAKEAWAYS: Clustering & Segmentation Section 19: Association Mining & Basket Analysis Lecture 154 Introduction Lecture 155 Association Mining Basics Lecture 156 The Apriori Algorithm Lecture 157 Basket Analysis Examples Lecture 158 Minimum Support Thresholds Lecture 159 Infrequent Itemsets Lecture 160 Multiple Item Sets Lecture 161 CASE STUDY: Apriori Lecture 162 Markov Chains Lecture 163 CASE STUDY: Markov Chains Lecture 164 KEY TAKEAWAYS: Association Mining Section 20: Outlier Detection Lecture 165 Introduction Lecture 166 Outlier Detection Basics Lecture 167 Cross-Sectional Outliers Lecture 168 Cross-Sectional Outlier Example Lecture 169 CASE STUDY: Cross-Sectional Outlier Lecture 170 Time-Series Outliers Lecture 171 Time-Series Outlier Example Lecture 172 KEY TAKEAWAYS: Outlier Detection Section 21: Dimensionality Reduction Lecture 173 Introduction Lecture 174 Dimensionality Reduction Basics Lecture 175 Principle Component Analysis Lecture 176 PCA Example Lecture 177 Interpreting Components Lecture 178 Scree Plots Lecture 179 Advanced Techniques Lecture 180 KEY TAKEAWAYS: Dimensionality Reduction Section 22: Wrapping Up Lecture 181 Series Conclusion Lecture 182 BONUS LESSON Anyone 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 analytics Homepage Stavte si za 50€ úplne bez rizika. 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