Overview
Machine Learning made simple with Excel! Classification for advanced data analysis & business intelligence (no coding!)
Syllabus
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- 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
Taught by
Maven Analytics and Joshua MacCarty
Tags
On-Demand
