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The Complete Visual Guide to Machine Learning and Data Science


1: The Complete Visual Guide to Machine Learning and Data Science

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

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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 and Multivariate Profiling

  • PART 2: Classification Modeling

  • PART 3: Regression and Forecasting

  • PART 4: Unsupervised Learning

PART 1: Univariate and Multivariate Profiling

In 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 and box plots, scatter plots, and correlation:

  • Section 1: Machine Learning Intro and Landscape

    Machine learning process, definition, and landscape

    Section 2: Preliminary Data QA

    Variable types, empty values, range and count calculations, left/right censoring, etc.

    Section 3: Univariate Profiling

    Histograms, frequency tables, mean, median, mode, variance, skewness, etc.

    Section 4: Multivariate Profiling

    Violin & 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.

The Complete Visual Guide to Machine Learning and Data Science | Udemy

PART 2: Classification Modeling

In 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 Classification

    Supervised learning and classification workflow, feature engineering, splitting, overfitting and underfitting

    Section 2: Classification Models

    K-nearest neighbors, naïve bayes, decision trees, random forests, logistic regression, sentiment analysis

    Section 3: Model Selection and Tuning

    Hyperparameter tuning, imbalanced classes, confusion matrices, accuracy, precision and recall, model drift

You’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 and Forecasting

In 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:

The Complete Visual Guide to Machine Learning and Data Science | Udemy

  • Section 1: Intro to Regression

    Supervised learning landscape, regression vs. classification, prediction vs. root-cause analysis

    Section 2: Regression Modeling 101

    Linear relationships, least squared error, univariate and multivariate regression, nonlinear transformation

    Section 3: Model Diagnostics

    R-squared, mean error, null hypothesis, F-significance, T and P-values, homoskedasticity, multicollinearity

    Section 4: Time-Series Forecasting

    Seasonality, auto correlation, linear trending, non-linear models, intervention analysis

You’ll see how regression analysis can be used to estimate property pricesforecast seasonal trendspredict sales for a new product launch, and even measure the business impact of a new website design.

PART 4: Unsupervised Learning

In Part 4 we’ll explore the differences between supervised and unsupervised machine learning and introduce several common unsupervised techniques, including cluster analysisassociation miningoutlier 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:

The Complete Visual Guide to Machine Learning and Data Science | Udemy

  • Section 1: Intro to Unsupervised Machine Learning

    Unsupervised learning landscape and workflow, common unsupervised techniques, feature engineering

    Section 2: Clustering and Segmentation

    Clustering basics, K-means, elbow plots, hierarchical clustering, dendograms

    Section 3: Association Mining

    Association mining basics, apriori, basket analysis, minimum support thresholds, markov chains

    Section 4: Outlier Detection

    Outlier detection basics, cross-sectional outliers, nearest neighbors, time-series outliers, residual distribution

    Section 5: Dimensionality Reduction

    Dimensionality reduction basics, principle component analysis (PCA), scree plots, advanced techniques

You'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 video

  • ML Foundations ebook (350+ pages)

  • Downloadable Excel project files

  • Expert Q and A forum

  • 30-day money-back guarantee

If 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.

Maven Analytics

Joshua MacCarty

Online Course CoupoNED based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners.