August 31 2021

Machine Learning for BI, PART 2: Classification Modeling

Machine Learning for BI, PART 2: Classification Modeling
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 49 lectures (2h 31m) | Size: 566.4 MB

Demystify Machine Learning and build foundational Data Science skills for classification & prediction, without any code!

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 eeering 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

This is a bner-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 .

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 eeering, 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 Sennt Analysis, and share tips for model scoring, selection, and optimization.

Section 1: Intro to Classification

Supervised Learning landscape

Classification workflow

Feature eeering

Data splitting

Overfitting & Underfitting

Section 2: Classification Models

K-Nearest Neighbors

Naïve Bayes

Decision Trees

Random Forests

Logistic Regression

Sennt 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 ee for Spotify, analyze customer purchase behavior for a retail shop, predict subscriptions for a travel company, extract sennt 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, life access to the following:

High-quality, on-demand video

Machine Learning: Classification ebook

able Excel project file

Expert Q&A forum

30-day money-back guarantee

Happy learning!

-Josh M. (Lead Machine Learning Instructor, Maven Analytics)

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

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