Machine Learning for Data Analysis: Classification Modeling

Machine Learning for Data Analysis: Classification Modeling

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 Modeling

• PART 3: Regression & Forecasting

• PART 4: Unsupervised 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.

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.

COURSE OUTLINE:

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!

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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

Happy learning!

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

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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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Machine Learning made simple with Excel! Classification for advanced data analysis & business intelligence (no coding!)

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What you will 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

Rating: 4.57547

Level: All Levels

Duration: 2.5 hours

Instructor: Maven Analytics