This is a beginner-friendly course (no prior knowledge or math/stats background required)
We’ll use Microsoft Excel (Office 365) for some course demos, but participation is optional
This is PART 2 of our Machine Learning for BI series (we recommend taking PART 1: Data Profiling & QA first)
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
- PART 2: Classification
- PART 3: Regression & Forecasting
- PART 4: Unsupervised Learning (Coming Soon!)
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 SINGLE LINE of code.
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!
Join today and get immediate, lifetime access to the following:
- High-quality, on-demand video
- Machine Learning: Classification ebook
- Downloadable Excel project file
- Expert Q&A forum
- 30-day money-back guarantee
-Josh M. (Lead Machine Learning Instructor, Maven Analytics)
Looking for our full business intelligence stack? Search for “Maven Analytics“ to browse our full course library, including Excel, Power BI, MySQL, and Tableau courses!
See why our courses are among the TOP-RATED on Udemy:
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Who this course is for:
- Anyone looking to learn the basics of machine learning through real-world demos and intuitive, crystal clear explanations
- Data Analysts or BI experts looking to transition into data science or build a fundamental understanding of machine learning
- R or Python users seeking a deeper understanding of the models and algorithms behind their code
- Excel users who want to learn powerful tools for predictive analytics
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