Complete Visual Guide to Machine Learning

Instructor: Maven Analytics

What you'll learn

  •   Build foundational machine learning and data science skills without learning complex math or code.
  •   Demystify common forecasting, classification and unsupervised models, including KNN, decision trees, linear and logistic regression, PCA and more
  •   Learn techniques for selecting and tuning models to optimize performance, reduce bias, and minimize drift

    • Skills you'll gain

  •   Workflow Management
  •   Exploratory Data Analysis
  •   Dimensionality Reduction
  •   Predictive Modeling
  •   Feature Engineering
  •   Unsupervised Learning
  •   Classification And Regression Tree (CART)
  •   Regression Analysis
  •   Machine Learning
  •   Quality Assurance
  •   Anomaly Detection
  •   Data Science
  •   Statistical Analysis
  •   Supervised Learning
  •   Time Series Analysis and Forecasting
  •   Random Forest Algorithm
  •   Histogram
  •   Data Mining
  •   Data Analysis
  •   Data Quality

    • There are 5 modules in this course

    Instead of memorizing complex math or writing code, we'll use simple, visual examples and Excel-based models to break down foundational machine learning concepts and help you build an intuition for exactly how they work. PART 1: QA & Data 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 matrices. 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. 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. 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, from K-means and apriori to outlier detection, principal component analysis, and more. Throughout the course, we’ll introduce real-world scenarios and to solidify key concepts and simulate actual data science use cases. You’ll visualize Olympic athlete demographics and traffic accident rates, use regression to estimate property prices and predict product sales, apply clustering models to identify customer segments, and even measure the business impact of a new website design. If you're an analyst or aspiring data professional looking to build the foundation for a successful career in machine learning or data science, this is the course for you!

    PART 1: Data QA & Profiling

    PART 2: Classification Modeling

    PART 3: Regression & Forecasting

    PART 4: Unsupervised Learning

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