[Udemy Course] The Complete Visual Guide to Machine Learning & Data Science by Maven Analytics, Chris Dutton, Joshua MacCarty

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

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

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.

💢💢💢💢💢💢💢💢

Product Details: https://bit.ly/46n1Tg6

File size: 3.4GB

Payment and delivery:

1. Please provide your EMAIL address in “message:” during checkout.

2. The files will be sent to you after payment has been confirmed.

🔥 All files will be delivered online.

🔥 Download for Lifetime Access

Kindly PM us if you are looking for other ebooks/ Video Courses.

Enjoy learning!