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via Udemy |
Go to Course: https://www.udemy.com/course/data-science-credit-card-fraud-detection-model-building/
Certainly! Here's a comprehensive review and recommendation for the Coursera course on Credit Card Fraud Detection using Machine Learning: --- **Course Review and Recommendation: Credit Card Fraud Detection with Machine Learning** If you're interested in mastering the art of building high-accuracy fraud detection models and enhancing your data science skills, this Coursera course is an outstanding choice. Designed with a hands-on approach, the course guides you through every crucial step of developing a robust machine learning model for classifying transactions as Fraudulent or Legitimate. **What You'll Learn:** - The course covers the entire process from data exploration, analysis, and preparation to model building and evaluation. - It introduces essential concepts like RepeatedKFold, StratifiedKFold, SMOTE, ADASYN, and Random Oversampling techniques for handling imbalanced datasets. - You'll gain practical experience working with multiple algorithms, including Logistic Regression, KNN, Decision Trees, Random Forest, XGBoost, and SVM. - The instructor provides detailed, step-by-step instructions, including coding functions for each model, enabling easy replication and understanding. - Emphasis on performance evaluation methods such as Confusion Matrix, Classification Reports, ROC AUC, and cross-validation techniques ensures a thorough grasp of model assessment. **Course Structure:** Segregated into clear, manageable Tasks, the course systematically guides you through: - Data loading and understanding - Feature engineering and selection - Model training and comparison - Handling class imbalance with advanced sampling techniques - Hyperparameter tuning for optimization - Extracting insights for final inference **Pros:** - Practical, project-based learning with real datasets - Comprehensive coverage of machine learning models and techniques for fraud detection - Tools and resources like datasets, code notebooks, and project files provided at the end - Certificate of completion adds value to your professional profile **Cons:** - Requires basic knowledge of Python and machine learning concepts for maximum benefit - The depth might be overwhelming for absolute beginners, but manageable with prior familiarity **Who Should Enroll?** - Data scientists and analysts aiming to specialize in fraud detection - Machine learning enthusiasts interested in financial applications - Professionals seeking hands-on project experience and certification **Final Verdict:** This course is highly recommended for anyone looking to deepen their understanding of machine learning applications in finance, specifically credit card fraud detection. Its detailed walkthrough, extensive resource provision, and focus on real-world applicability make it a valuable investment in your data science journey. **Ready to Dive In?** Click the enroll button, grab your favorite coffee, and start mastering one of the most in-demand skills of the 21st century. Happy learning! --- Let me know if you'd like me to tailor this further or create a shorter version!
In this course I will cover, how to develop a Credit Card Fraud Detection model to categorize a transaction as Fraud or Legitimate with very high accuracy using different Machine Learning Models. This is a hands on project where I will teach you the step by step process in creating and evaluating a machine learning model.This course will walk you through the initial data exploration and understanding, data analysis, data preparation, model building and evaluation. We will explore RepeatedKFold, StratifiedKFold, Random Oversampler, SMOTE, ADASYN concepts and then use multiple ML algorithms to create our model and finally focus into one which performs the best on the given dataset.I have splitted and segregated the entire course in Tasks below, for ease of understanding of what will be covered.Task 1 : Installing Packages.Task 2 : Importing Libraries.Task 3 : Loading the data from source.Task 4 : Understanding the dataTask 5 : Checking the class distribution of the target variableTask 6 : Finding correlation and plotting Heat MapTask 7 : Performing Feature engineering.Task 8 : Train Test SplitTask 9: Plotting the distribution of a variableTask 10: About Confusion Matrix, Classification Report, AUC-ROCTask 11: Created a common function to plot confusion matrixTask 12: About Logistic Regression, KNN, Tree, Random Forest, XGBoost, SVM modelsTask 13: Created a common function to fit and predict on a Logistic Regression modelTask 14: Created a common function to fit and predict on a KNN modelTask 15: Created a common function to fit and predict on a Tree modelsTask 16: Created a common function to fit and predict on a Random Forest modelTask 17: Created a common function to fit and predict on a XGBoost modelTask 18: Created a common function to fit and predict on a SVM modelTask 19: About RepeatedKFold and StratifiedKFold.Task 20: Performing cross validation with RepeatedKFold and Model EvaluationTask 21: Performing cross validation with StratifiedKFold and Model EvaluationTask 22: Proceeding with the model which shows the best result till nowTask 23: About Random Oversampler, SMOTE, ADASYN.Task 24: Performing oversampling with Random Oversampler with StratifiedKFold cross validation and Model Evaluation.Task 25: Performing oversampling with SMOTE and Model Evaluation.Task 26: Performing oversampling with ADASYN and Model Evaluation.Task 27: Hyperparameter Tuning.Task 28: Extracting most important featuresTask 29: Final Inference.Data Analysis, Model Building is one of the most demanded skill of the 21st century. Take the course now, and have a much stronger grasp of Machine learning in just a few hours!You will receive:1. Certificate of completion from AutomationGig.2. All the datasets used in the course are in the resources section.3. The Jupyter notebook and other project files are provided at the end of the course in the resource section.So what are you waiting for?Grab a cup of coffee, click on the ENROLL NOW Button and start learning the most demanded skill of the 21st century. We'll see you inside the course!Happy Learning!![Please note that this course and its related contents are for educational purpose only][Music: bensound]