Explore Data Science & Machine Learning topics with simple, step-by-step demos and user-friendly Excel models (NO code!)

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

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