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.
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