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Update navigation order for machine learning modules and remove topic lists for improved organization in the Zoomcamp documentation.
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title: "Module 1: Introduction to Machine Learning"
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parent: Machine Learning Zoomcamp
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This module covers the following topics:
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- [1. What Is Machine Learning?](01-what-is-ml.md)
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- [2. ML vs Rule-Based Systems](02-ml-vs-rules.md)
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- [3. Supervised Machine Learning](03-supervised-ml.md)
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- [4. CRISP-DM](04-crisp-dm.md)
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- [5. Model Selection](05-model-selection.md)
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- [6. GitHub Codespaces](06-github-codespaces.md)
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- [7. Introduction to NumPy](07-numpy.md)
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- [8. Linear Algebra Refresher](08-linear-algebra.md)
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- [9. Introduction to Pandas](09-pandas.md)
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- [10. Summary](10-summary.md)
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Module content on GitHub: https://github.com/DataTalksClub/machine-learning-zoomcamp/tree/master/01-intro
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title: "Module 2: Machine Learning for Regression"
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This module covers the following topics:
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- [1. Car Price Prediction](01-car-price-intro.md)
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- [2. Data Preparation](02-data-preparation.md)
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- [3. Exploratory Data Analysis](03-eda.md)
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- [4. Validation Framework](04-validation-framework.md)
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- [5. Linear Regression](05-linear-regression-simple.md)
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- [6. Linear Regression in Vector & Matrix Form](06-linear-regression-vector.md)
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- [7. Training the Model (Normal Equation)](07-linear-regression-training.md)
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- [8. Baseline Model](08-baseline-model.md)
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- [9. Evaluating a Regression Model with RMSE](09-rmse.md)
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- [10. Validating an ML Model](10-car-price-validation.md)
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- [11. Feature Engineering](11-feature-engineering.md)
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- [12. Categorical Variables](12-categorical-variables.md)
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- [13. Regularization](13-regularization.md)
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- [14. Tuning Model](14-tuning-model.md)
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- [15. Using Model](15-using-model.md)
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- [16. Summary](16-summary.md)
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Module content on GitHub: https://github.com/DataTalksClub/machine-learning-zoomcamp/tree/master/02-regression
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title: "Module 3: Machine Learning for Classification"
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# Module 3: Machine Learning for Classification
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This module covers the following topics:
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- [1. Churn Prediction Project](01-churn-project.md)
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- [2. Data Preparation](02-data-preparation.md)
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- [3. Setting Up the Validation Framework](03-validation.md)
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- [4. EDA](04-eda.md)
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- [5. Feature importance: Churn rate and risk ratio](05-risk.md)
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- [6. Feature importance: Mutual information](06-mutual-info.md)
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- [7. Feature importance: Correlation](07-correlation.md)
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- [8. One-hot encoding](08-ohe.md)
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- [9. Logistic Regression](09-logistic-regression.md)
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- [10. Training logistic regression with Scikit-Learn](10-training-log-reg.md)
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- [11. Model interpretation](11-log-reg-interpretation.md)
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- [12. Using the model](12-using-log-reg.md)
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- [13. Summary](13-summary.md)
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Module content on GitHub: https://github.com/DataTalksClub/machine-learning-zoomcamp/tree/master/03-classification
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title: "Module 4: Evaluation Metrics for Classification"
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# Module 04: Evaluation
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This module covers the following topics:
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- [1. Evaluation metrics: session overview](01-overview.md)
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- [2. Accuracy and dummy model](02-accuracy.md)
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- [3. Confusion Table](03-confusion-table.md)
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- [4. Precision and Recall](04-precision-recall.md)
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- [5. ROC Curves](05-roc.md)
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- [6. ROC AUC](06-auc.md)
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- [7. Cross Validation](07-cross-validation.md)
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- [8. Summary](08-summary.md)
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title: "Module 5: Deploying Machine Learning Models"
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- [1. Intro / Session overview](01-intro.md)
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- [2. Saving and loading the model](02-pickle.md)
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- [3. Web services: introduction to Flask](03-flask-intro.md)
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- [4. Serving the churn model with Flask](04-flask-deployment.md)
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- [5. Python virtual environment: Pipenv](05-pipenv.md)
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- [6. Environment management: Docker and Docker Hub](06-docker.md)
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- [7. Deployment to the cloud: AWS Elastic Beanstalk (optional)](07-aws-eb.md)
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- [8. Summary](08-summary.md)
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title: "Module 6: Decision Trees and Ensemble Learning"
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- [1. Credit risk scoring project](01-credit-risk.md)
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- [2. Data cleaning and preparation](02-data-prep.md)
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- [3. Decision trees](03-decision-trees.md)
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- [4. Decision tree learning algorithm](04-decision-tree-learning.md)
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- [5. Decision trees parameter tuning](05-decision-tree-tuning.md)
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- [6. Ensemble learning and random forest](06-random-forest.md)
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- [7. Gradient boosting and XGBoost](07-boosting.md)
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- [8. XGBoost parameter tuning](08-xgb-tuning.md)
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- [9. Selecting the best model](09-final-model.md)
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- [10. Summary](10-summary.md)
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title: "Module 8: Neural Networks and Deep Learning"
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# Module 8: Neural Networks and Deep Learning
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- [1. Fashion classification](01-fashion-classification.md)
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- [1b. Setting up the Environment on Saturn Cloud](01b-saturn-cloud.md)
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- [2. TensorFlow and Keras](02-tensorflow-keras.md)
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- [3. Pre-trained convolutional neural networks](03-pretrained-models.md)
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- [4. Convolutional neural networks](04-conv-neural-nets.md)
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- [5. Transfer learning](05-transfer-learning.md)
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- [6. Adjusting the learning rate](06-learning-rate.md)
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- [7. Checkpointing](07-checkpointing.md)
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- [8. Adding more layers](08-more-layers.md)
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- [9. Regularization and dropout](09-dropout.md)
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- [10. Data augmentation](10-augmentation.md)
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- [11. Training a larger model](11-large-model.md)
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- [12. Using the model](12-using-model.md)
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- [13. Summary](13-summary.md)
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title: "Module 9: Serverless Deep Learning"
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# Module 9: Serverless Deep Learning
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- [1. Introduction to Serverless](01-intro.md)
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- [2. AWS Lambda](02-aws-lambda.md)
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- [3. TensorFlow Lite](03-tensorflow-lite.md)
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- [4. Preparing the code for Lambda](04-preparing-code.md)
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- [5. Preparing a Docker image](05-docker-image.md)
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- [6. Creating the lambda function](06-creating-lambda.md)
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- [7. API Gateway: exposing the lambda function](07-api-gateway.md)
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- [8. Summary](08-summary.md)
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title: "Module 10: Kubernetes and TensorFlow Serving"
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# Module 10: Kubernetes and TensorFlow Serving
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- [1. Overview](01-overview.md)
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- [2. TensorFlow Serving](02-tensorflow-serving.md)
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- [3. Creating a pre-processing service](03-preprocessing.md)
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- [4. Running everything locally with Docker-compose](04-docker-compose.md)
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- [5. Introduction to Kubernetes](05-kubernetes-intro.md)
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- [6. Deploying a simple service to Kubernetes](06-kubernetes-simple-service.md)
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- [7. Deploying TensorFlow models to Kubernetes](07-kubernetes-tf-serving.md)
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- [8. Deploying to EKS](08-eks.md)
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- [9. Summary](09-summary.md)
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title: "Module 11: KServe"
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# Module 11: KServe
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- [1. Overview](01-overview.md)
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- [2. Running KServe locally](02-kserve-local.md)
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- [3. Deploying a Scikit-Learn model with KServe](03-kserve-sklearn.md)
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- [4. Deploying custom Scikit-Learn images with KServe](04-kserve-custom-image.md)
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- [5. Serving TensorFlow models with KServe](05-tensorflow-kserve.md)
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- [6. KServe transformers](06-kserve-transformers.md)
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- [7. Deploying with KServe and EKS](07-kserve-eks.md)

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