Machine Learning for Data Analysis: Classification Modeling

Overview

Machine Learning made simple with Excel! Classification for advanced data analysis & business intelligence (no coding!) What you'll learn:

Build foundational machine learning & data science skills, without writing complex codeUse intuitive, user-friendly tools like Microsoft Excel to introduce & demystify machine learning tools & techniquesEnrich datasets by using feature engineering techniques like one-hot encoding, scaling, and discretizationPredict categorical outcomes using classification models like K-nearest neighbors, naïve bayes, decision trees, and moreApply techniques for selecting & tuning classification models to optimize performance, reduce bias, and minimize driftCalculate metrics like accuracy, precision and recall to measure model performance HEADS UP!This course is now part of The Complete Visual Guide to Machine Learning &Data Science, which combines all 4 Machine Learning courses from Maven Analytics.

This course, along with the other individual courses in the series, will be retired soon.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 ProfilingPART2:

Classification ModelingPART3:

Regression & ForecastingPART4:

Unsupervised LearningThis 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 SINGLELINEof code.COURSEOUTLINE:

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 ClassificationSupervised Learning landscapeClassification workflowFeature engineeringData splittingOverfitting &UnderfittingSection 2:

Classification ModelsK-Nearest NeighborsNaïve BayesDecision TreesRandom ForestsLogistic RegressionSentiment AnalysisSection 3:

Model Selection & TuningHyperparameter tuningImbalanced classesConfusion matricesAccuracy, Precision &recallModel selection & driftThroughout 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 todayand get immediate, lifetime accessto the following:

High-quality, on-demand videoMachine Learning:

ClassificationebookDownloadableExcel project fileExpertQ&Aforum30-day money-back guaranteeHappy learning!-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, andTableaucourses!See why our courses are among the TOP-RATEDon Udemy:

"Some of the BESTcourses I've ever taken.

I've studied several programming languages, Excel, VBA and web dev, and Maven is among the very best I've seen!" Russ C."This is my fourth course from Maven Analytics and my fourth 5-star review, so I'm running out of things to say. I wish Maven was in my life earlier!" Tatsiana M."Maven Analytics should become the new standard for all courses taught on Udemy!" Jonah M.

Syllabus

• Introduction to Classification
Overview of Classification in Machine Learning
Differences between Classification and Regression
• Preparing Data for Classification
Data Preprocessing Techniques
Handling Missing Values
Feature Scaling and Transformation
Class Imbalance and Resampling Methods
• Understanding Classification Algorithms
Decision Trees
k-Nearest Neighbors (k-NN)
Support Vector Machines (SVM)
Logistic Regression
Naive Bayes
Neural Networks and Deep Learning for Classification
• Advanced Classification Techniques
Ensemble Methods: Bagging, Boosting, and Random Forest
Gradient Boosting Machines (GBM) and XGBoost
• Model Evaluation and Performance Metrics
Confusion Matrix
Precision, Recall, and F1 Score
ROC Curve and AUC
Cross-Validation
• Feature Selection and Dimensionality Reduction
Principal Component Analysis (PCA)
Feature Importance
• Implementing Classification Models
Using Python for Machine Learning
Scikit-learn Library for Classification
Model Training and Testing
• Real-World Applications of Classification
Application in Finance, Healthcare, and Marketing
Case Studies
• Best Practices and Tips for Classification Projects
Dealing with Overfitting and Underfitting
Hyperparameter Tuning
• Future Trends and Research Directions
Explainable AI and Interpretability in Classification
Recent Advances in Classification Algorithms
• Final Project
Designing and Implementing a Classification Model for a Real-World Dataset

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Taught by

Maven Analytics and Joshua MacCarty

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Subjects

Data Science