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 & LandscapeMachine learning process, definition, and landscape
- Section 2: Preliminary Data QAVariable types, empty values, range & count calculations, left/right censoring, etc.
- Section 3: Univariate ProfilingHistograms, frequency tables, mean, median, mode, variance, skewness, etc.
- Section 4: Multivariate ProfilingViolin & 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.
