Machine Learning & Data Science: The Complete Visual Guide (Udemy.com)

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 Profiling

• PART 2: Classification Modeling

• PART 3: Regression & Forecasting

• PART 4: Unsupervised Learning

  
  

PART 1: Univariate & 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 & box plots, scatter plots, and correlation:

• Section 1: Machine Learning Intro & Landscape

  Machine learning process, definition, and landscape

  
  

• Section 2: Preliminary Data QA

  Variable types, empty values, range & 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.

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 & classification workflow, feature engineering, splitting, overfitting & underfitting

  
  

• Section 2: Classification Models

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

  
  

• Section 3: Model Selection & Tuning

  Hyperparameter tuning, imbalanced classes, confusion matrices, accuracy, precision & 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 & 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:

• 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 & multivariate regression, nonlinear transformation

  
  

• Section 3: Model Diagnostics

  R-squared, mean error, null hypothesis, F-significance, T & 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 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 Learning

In 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 Learning

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

  
  

• Section 2: Clustering & 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