Are you looking for a beginner-friendly introduction to the world of machine learning and data science? The Complete Visual Guide to Machine Learning & Data Science may be just what you need! This top-rated course offers step-by-step demos and user-friendly Excel models to help you explore key topics in data science and machine learning.
Course Description
This course is designed for everyday people who are interested in learning foundational skills in machine learning and data science. The course is broken down into four parts:
- Univariate & Multivariate Profiling
- Classification Modeling
- Regression & Forecasting
- Unsupervised Learning
In each section, you'll explore different topics and techniques, from exploring frequency tables to building recommendation engines and identifying outliers.
The Course Content
The course contains over nine hours of on-demand video content, as well as a 350+ page eBook, downloadable Excel project files, and an expert Q&A forum. Throughout the course, you'll get to follow along with real-world examples that help you build confidence in your skills.
Part 1: Univariate & Multivariate Profiling
The first part of the course introduces you to the machine learning workflow and common techniques for cleaning and preparing raw data for analysis. You'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.
Throughout the section, you'll work with 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 two of the course, you'll be introduced to 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, you'll explore common classification models like K-Nearest Neighbors (KNN), Naïve Bayes, Decision Trees, Random Forests, Logistic Regression, and Sentiment Analysis, learning tips for model scoring, selection, and optimization.
In this section of the course, 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
Part three of the course introduces core building blocks like linear relationships and least squared error, and practice applying them to univariate, multivariate, and non-linear regression models. You'll review diagnostic metrics like R-squared, mean error, F-significance, and P-Values and use time-series forecasting techniques to identify seasonality, predict nonlinear trends, and measure the impact of key business decisions using intervention analysis.
You'll learn 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 using regression analysis techniques.
Part 4: Unsupervised Learning
In part four of the course, you'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. You'll learn to build an intuition for how they work, from K-means and apriori to outlier detection, principal component analysis, and more.
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.
Course Rating and Reviews
The Complete Visual Guide to Machine Learning & Data Science has an aggregate rating of 4.75498 out of 5 and over 102 reviews. Several reviews rave about the "beginner-friendly" nature of the course, and others praise the "organizational clarity" and "concise explanations."
If you're an aspiring data professional or analyst looking to build the foundation for a successful career in machine learning or data science, be sure not to miss out on the opportunity to take this course. Enroll today and get lifetime access to the content, so you can learn at your own pace and convenience.
