The Complete Visual Guide to Machine Learning & Data Science

The Complete Visual Guide to Machine Learning & Data Science

 The Complete Visual Guide to Machine Learning & Data Science - 
Explore Data Science & Machine Learning topics with simple, step-by-step demos and user-friendly Excel models (NO code!)


Sure! Here’s a brief summary of “The Complete Visual Guide to Machine Learning & Data Science” course on Udemy 1:


This course is designed for everyday people looking for an intuitive, beginner-friendly introduction to the world of machine learning and data science. It aims to 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, this course breaks down and visualizes complex concepts in an easy-to-understand way.


The course covers topics such as:


  • Introduction to Machine Learning
  • Data Preprocessing
  • Regression
  • Classification
  • Clustering
  • Association Rule Learning
  • Reinforcement Learning
  • Natural Language Processing
  • Deep Learning

The course also includes practical exercises and quizzes to help you test your knowledge.


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


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