Machine Learning for BI, PART 2: Classification Modeling

Demystify the world of Machine Learning and build core Data Science & predictive analytics skills, without writing code!

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. 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.

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

You May Also Need This Course

Machine Learning for BI, PART 3: Regression & Forecasting

Machine Learning for BI, PART 4: Unsupervised Learning

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