What you'll learn:
- Build foundational machine learning & data science skills, without writing complex code
- Use intuitive, user-friendly tools like Microsoft Excel to introduce & demystify machine learning tools & techniques
- Enrich datasets by using feature engineering techniques like one-hot encoding, scaling, and discretization
- Predict categorical outcomes using classification models like K-nearest neighbors, naïve bayes, decision trees, and more
- Apply techniques for selecting & tuning classification models to optimize performance, reduce bias, and minimize drift
- Calculate metrics like accuracy, precision and recall to measure model performance
HEADS UP!
This course is now part of The Complete Visual Guide to Machine Learning &Data Science, which combines all 4 Machine Learning courses from Maven Analytics. This course, along with the other individual courses in the series, will be retired soon.
If you're excited to explore Data Science & Machine Learning but anxious about learning complex programming languages or intimidated by terms like "naive bayes", "logistic regression", "KNN" and "decision trees", you're in the right place.
This course is PART 2 of a 4-PART SERIES designed to help you build a strong, foundational understanding of Machine Learning:
PART 1:QA & Data Profiling
PART2:Classification Modeling
PART3:Regression & Forecasting
PART4:Unsupervised Learning
This course makes data science approachable to everyday people, and is designed to demystify powerful Machine Learning tools &techniques without trying to teach you a coding language at the same time.
Instead, we'll use familiar, user-friendly tools like Microsoft Excel to break down complex topics and help you understand exactly HOW and WHY machine learning works before you dive into programming languages like Python or R. Unlike most Data Science and Machine Learning courses, you won't write a SINGLELINEof code.
COURSEOUTLINE:
In this Part 2 course, 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 including 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 landscape
Classification workflow
Feature engineering
Data 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 selection & drift
Throughout the course we’ll introduce case studies to solidify key concepts and tie them back to real world scenarios. You’ll help build a recommendation engine for Spotify, analyze customer purchase behavior for a retail shop, predict subscriptions for a travel company, extract sentiment from customer reviews, and much more.
If you’re ready to build the foundation for a successful career in Data Science, this is the course for you!
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Join todayand get immediate, lifetime accessto the following:
High-quality, on-demand video
Machine Learning:Classificationebook
DownloadableExcel project file
ExpertQ&Aforum
30-day money-back guarantee
Happy learning!
-Josh M. (Lead Machine Learning Instructor, Maven Analytics)
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