Machine Learning & Data Science: The Complete Visual Guide
Machine Learning & Data Science: The Complete Visual Guide, available at $94.99, has an average rating of 4.61, with 182 lectures, 15 quizzes, based on 387 reviews, and has 3831 subscribers.
You will learn about 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 ideal for individuals who are Anyone looking to learn the foundations of machine learning through interactive, beginner-friendly demos or Data Analysts or BI experts looking to transition into data science or build a fundamental understanding of machine learning or R or Python users seeking a deeper understanding of the models and algorithms behind their code or Excel users who want to learn and apply powerful tools for predictive analytics It is particularly useful for Anyone looking to learn the foundations of machine learning through interactive, beginner-friendly demos or Data Analysts or BI experts looking to transition into data science or build a fundamental understanding of machine learning or R or Python users seeking a deeper understanding of the models and algorithms behind their code or Excel users who want to learn and apply powerful tools for predictive analytics.
Enroll now: Machine Learning & Data Science: The Complete Visual Guide
Summary
Title: Machine Learning & Data Science: The Complete Visual Guide
Price: $94.99
Average Rating: 4.61
Number of Lectures: 182
Number of Quizzes: 15
Number of Published Lectures: 182
Number of Published Quizzes: 15
Number of Curriculum Items: 197
Number of Published Curriculum Objects: 197
Original Price: $129.99
Quality Status: approved
Status: Live
What You Will 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
Who Should Attend
- 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
Target Audiences
- 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
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:
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PART 1:Univariate & Multivariate Profiling
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PART 2:Classification Modeling
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PART 3:Regression & Forecasting
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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:
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Section 1: Machine Learning Intro & Landscape
Machine learning process, definition, and landscape
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Section 2: Preliminary Data QA
Variable types, empty values, range & count calculations, left/right censoring, etc.
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Section 3: Univariate Profiling
Histograms, frequency tables, mean, median, mode, variance, skewness, etc.
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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:
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Section 1: Intro to Classification
Supervised learning & classification workflow, feature engineering, splitting, overfitting & underfitting
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Section 2: Classification Models
K-nearest neighbors, naïve bayes, decision trees, random forests, logistic regression, sentiment analysis
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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:
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Section 1: Intro to Regression
Supervised learning landscape, regression vs. classification, prediction vs. root-cause analysis
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Section 2: Regression Modeling 101
Linear relationships, least squared error, univariate & multivariate regression, nonlinear transformation
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Section 3: Model Diagnostics
R-squared, mean error, null hypothesis, F-significance, T & P-values, homoskedasticity, multicollinearity
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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:
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Section 1: Intro to Unsupervised Machine Learning
Unsupervised learning landscape & workflow, common unsupervised techniques, feature engineering
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Section 2: Clustering & Segmentation
Clustering basics, K-means, elbow plots, hierarchical clustering, dendograms
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Section 3: Association Mining
Association mining basics, apriori, basket analysis, minimum support thresholds, markov chains
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Section 4: Outlier Detection
Outlier detection basics, cross-sectional outliers, nearest neighbors, time-series outliers, residual distribution
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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 anomaliesin cross-sectional or time-series datasets.
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Ready to dive in? Join today and get immediate, LIFETIME access to the following:
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9+ hours of on-demand video
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ML Foundations ebook (350+ pages)
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Downloadable Excel project files
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Expert Q&A forum
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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.
Happy learning!
-Josh& Chris
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Looking for our full business intelligence stack? Search for “Maven Analytics“ to browse our full course library, including Excel, Power BI, MySQL, Tableau and Machine Learning courses!
See why our courses are among the TOP-RATED on Udemy:
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Course Curriculum
Chapter 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
Chapter 2: PART 1: QA & Data Profiling
Lecture 1: Part 1: QA & Data Profiling
Chapter 3: Intro to the ML Landscape
Lecture 1: Intro to Machine Learning
Lecture 2: When is ML the right fit?
Lecture 3: The Machine Learning Process
Lecture 4: The Machine Learning Landscape
Chapter 4: Preliminary Data QA
Lecture 1: Introduction
Lecture 2: Why QA?
Lecture 3: Variable Types
Lecture 4: Empty Values
Lecture 5: Range Calculations
Lecture 6: Count Calculations
Lecture 7: Left & Right Censored Data
Lecture 8: Table Structure
Lecture 9: CASE STUDY: Preliminary QA
Lecture 10: BEST PRACTICES: Preliminary QA
Chapter 5: Univariate Profiling
Lecture 1: Introduction
Lecture 2: Categorical Variables
Lecture 3: Discretization
Lecture 4: Nominal vs. Ordinal
Lecture 5: Categorical Distributions
Lecture 6: Numerical Variables
Lecture 7: Histograms & Kernel Densities
Lecture 8: CASE STUDY: Histograms
Lecture 9: Normal Distribution
Lecture 10: CASE STUDY: Normal Distribution
Lecture 11: Univariate Data Profiling
Lecture 12: Mode
Lecture 13: Mean
Lecture 14: Median
Lecture 15: Percentile
Lecture 16: Variance
Lecture 17: Standard Deviation
Lecture 18: Skewness
Lecture 19: BEST PRACTICES: Univariate Profiling
Chapter 6: Multivariate Profiling
Lecture 1: Introduction
Lecture 2: Categorical-Categorical
Lecture 3: CASE STUDY: Heat Maps
Lecture 4: Categorical-Numerical
Lecture 5: Multivariate Kernel Densities
Lecture 6: Violin Plots
Lecture 7: Box Plots
Lecture 8: Limitations of Categorical Distributions
Lecture 9: Numerical-Numerical
Lecture 10: Correlation
Lecture 11: Correlation vs. Causation
Lecture 12: Visualizing Third Dimension
Lecture 13: CASE STUDY: Correlation
Lecture 14: BEST PRACTICES: Multivariate Profiling
Lecture 15: Looking Ahead to Part 2
Chapter 7: PART 2: Classification Modeling
Lecture 1: Part 2: Classification Modeling
Chapter 8: Intro to Classification
Lecture 1: Supervised vs. Unsupervised Learning
Lecture 2: Classification vs. Regression
Lecture 3: RECAP: Key Concepts
Lecture 4: Classification 101
Lecture 5: Classification Workflow
Lecture 6: Feature Engineering
Lecture 7: Data Splitting
Lecture 8: Overfitting
Chapter 9: Classification Models
Lecture 1: Common Classification Models
Lecture 2: Intro to K-Nearest Neighbors (KNN)
Lecture 3: KNN Examples
Lecture 4: CASE STUDY: KNN
Lecture 5: Intro to Naïve Bayes
Lecture 6: Naïve Bayes | Frequency Tables
Lecture 7: Naïve Bayes | Conditional Probability
Lecture 8: CASE STUDY: Naïve Bayes
Lecture 9: Intro to Decision Trees
Lecture 10: Decision Trees | Entropy 101
Lecture 11: Entropy & Information Gain
Lecture 12: Decision Tree Examples
Lecture 13: Random Forests
Lecture 14: CASE STUDY: Decision Trees
Lecture 15: Intro to Logistic Regression
Lecture 16: Logistic Regression Example
Lecture 17: False Positives vs. False Negatives
Lecture 18: Logistic Regression Equation
Lecture 19: The Likelihood Function
Lecture 20: Multivariate Logistic Regression
Lecture 21: CASE STUDY: Logistic Regression
Lecture 22: Intro to Sentiment Analysis
Lecture 23: Cleaning Text Data
Lecture 24: "Bag of Words" Analysis
Lecture 25: CASE STUDY: Sentiment Analysis
Instructors
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Maven Analytics
Empowering everyday people with life-changing data skills -
Chris Dutton
Best-Selling Instructor, Founder @ Maven Analytics -
Joshua MacCarty
Lead ML Instructor
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 1 votes
- 3 stars: 21 votes
- 4 stars: 92 votes
- 5 stars: 272 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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