The Complete Visual Guide To Machine Learning & Data Science

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