Description
Classification – Fundamentals & Practical Applications – CFI Education, Paul van Loon – Classification – Fundamentals & Practical Applications – CFI Education, Classification – Fundamentals & Practical Applications – CFI Education download, Paul van Loon – Classification – Fundamentals & Practical Applications – CFI Education review, Classification – Fundamentals & Practical Applications – CFI Education free torent
Paul van Loon – Classification – Fundamentals & Practical Applications – CFI Education
Classification – Fundamentals & Practical Applications
Level 4
Approx 2.5h to complete
100% online and self-paced
This course provides a comprehensive overview of classification problems, solutions, and interpretations along with walkthroughs of real-world scenarios.
Overview
Classification – Fundamentals & Practical Applications
Classification problems are one of the most common scenarios we face in data science. This course will help you understand and apply common algorithms to make predictions and drive decision-making in business. Whether you’re an aspiring data scientist, studying analytics, or have a focus on business intelligence, this course will give you a comprehensive overview of classification problems, solutions, and interpretations.
From Logistic Regression to KNN and SVM models, you’ll learn how to implement techniques in Excel and Python and how to create loops to run models in parallel. Since model evaluation is so important, we’ll dedicate a whole chapter to interpreting model outputs with evaluation metrics and the confusion matrix. With this, you’ll learn about false negatives, and false positives, and consider the impacts these may have on specific business scenarios. Finally, we’ll give you a brief insight into more advanced classification techniques such as feature importance, SHAP values, and PDP plots.
Classification Fundamentals Learning Objectives
Upon completing this course, you will be able to:
Distinguish between classic classification techniques including their implicit assumptions and practical use-cases
Perform simple logistic regression calculations in Excel & RegressIt
Create basic classification models in Python using statsmodels and sklearn modules
Evaluate and interpret the performance of classification model outputs and parameters
Who Should Take This Course? Whether you’re an aspiring data scientist, studying analytics, or have a focus on business intelligence, this classification course will serve as your comprehensive introduction to this fascinating subject. You’ll learn all the key terminology to allow you to talk data science with your teams, benign implementing analysis, and understand how data science can help your business.
What you’ll learn
Classification Introduction
Course Introduction
Learning Objectives
Downloadable Files
Classification Overview
What is Classification?
The Machine Learning Ecosystem
Types of Classification – Binary
Types of Classification – Multi-class
Types of Classification – Multi-label
Common Classification Use Cases
Visualizing Classification
Classification Algorithms
Logistic Regression Basics
Logistic Regression Basics
Visualizing Logistic Regression
Logistic Regression Assumptions
Probability, Odds and Log Odds
Interpreting Log Odds and Coefficients
Interpretation Scenario
Logistic Regression in Excel
Python – Logistic Regression 1
Python – Logistic Regression 2
Classification Algorithms
Algorithms Overview
Naïve Bayes
Naïve Bayes – Example
K-Nearest Neighbors
K-Nearest Neighbors – Example
Support Vector Machines
Decision Trees
Decision Trees – Example
Random Forests
Python – Import & Explore Data
Predictive Modeling Part 1
Predictive Modeling Part 2
Classification Model Evaluation
Model Evaluation Basics
Confusion Matrix
Evaluation Metrics
Evaluation Example
Precision Vs Recall
Balancing Precision and Recall with F-score
Is Accuracy the Best Choice
The ROC Curve & AUC
Underfitting and Overfitting
Python – ROC Curve
Python – ROC Interpretation
Interpretability
Interpretability Vs Explainability
Feature Importance
Partial Dependence Plots
SHAP Values for Individual Observations
Setting Up Evaluation Loops
Python – Evaluation Metrics
Python – Confusion Matrix
Conclusion
Course Conclusion
Qualified Assessment
Qualified Assessment
This Course is Part of the Following Programs
Why stop here? Expand your skills and show your expertise with the professional certifications, specializations, and CPE credits you’re already on your way to earning.
