Machine Learning 2: Classification

This course is PART 2 of a 4-PART SERIES designed to help you build a fundamental understanding of machine learning:

1. QA & Data Profiling
2. Classification
3. Regression & Forecasting
4. 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 CURRICULUM:

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