Machine Learning 2: Classification
Learn powerful classification models for data-driven predictions, including decision trees, logistic regression, KNN, and more.
This course is PART 2 of a 4-PART SERIES designed to help you build a fundamental understanding of machine learning:
- QA & Data Profiling
- Regression & Forecasting
- Unsupervised Learning
In this 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.
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.
NOTE: This is NOT a coding course, and doesn't cover programming languages like Python or R. Our goal is to use familiar tools like Excel to demystify complex topics and explain exactly how they work.
If you’re ready to build the foundation for a successful career in data science, this is the course for you.
- Course Structure & Outline
- About this Series
- DOWNLOAD: Course Resources
- Setting Expectations
- Supervised vs. Unsupervised Learning
- Classification vs. Regression
- RECAP: Key Concepts
- Classification 101
- Classification Workflow
- Feature Engineering
- Data Splitting
- QUIZ: Intro to Classification
- Common Classification Models
- Intro to K-Nearest Neighbors (KNN)
- KNN Examples
- CASE STUDY: KNN
- Intro to Naïve Bayes
- Naïve Bayes | Frequency Tables
- Naïve Bayes | Conditional Probability
- CASE STUDY: Naïve Bayes
- Intro to Decision Trees
- Decision Trees | Entropy 101
- Entropy & Information Gain
- Decision Tree Examples
- Random Forests
- CASE STUDY: Decision Trees
- Intro to Logistic Regression
- Logistic Regression Example
- False Positives vs. False Negatives
- Logistic Regression Equation
- The Likelihood Function
- Multivariate Logistic Regression
- CASE STUDY: Logistic Regression
- Intro to Sentiment Analysis
- Cleaning Text Data
- Bag of Words Analysis
- CASE STUDY: Sentiment Analysis
- QUIZ: Classification Models
- Intro to Selection & Tuning
- Imbalanced Classes
- Confusion Matrix
- Accuracy, Precision & Recall
- Multi-class Confusion Matrix
- Model Selection
- Model Drift
- Looking Ahead to Part 3
- Course Feedback Survey
- Share the love!
- Next Steps
WHO SHOULD TAKE THIS COURSE?
Data Analysts or BI experts looking to transition into a data science role or build a fundamental understanding of core ML topics
R or Python users seeking a deeper understanding of the models and algorithms behind their code
Anyone looking to learn the basics of machine learning through hands-on demos and intuitive, crystal clear explanations
WHAT ARE THE COURSE REQUIREMENTS?
- We'll use Microsoft Excel (Office 365 Pro Plus) for demos, but you are not required to follow along
Start learning for FREE, no credit card required!
Every subscription includes access to the following course materials
- Interactive Project files
- Downloadable e-books
- Graded quizzes and assessments
- 1-on-1 Expert support
- 100% satisfaction guarantee
- Verified credentials & accredited badges
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