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22title : " Module 1: Introduction to Machine Learning"
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44parent : Machine Learning Zoomcamp
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1010This module covers the following topics:
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12- - [ 1. What Is Machine Learning?] ( 01-what-is-ml.md )
13- - [ 2. ML vs Rule-Based Systems] ( 02-ml-vs-rules.md )
14- - [ 3. Supervised Machine Learning] ( 03-supervised-ml.md )
15- - [ 4. CRISP-DM] ( 04-crisp-dm.md )
16- - [ 5. Model Selection] ( 05-model-selection.md )
17- - [ 6. GitHub Codespaces] ( 06-github-codespaces.md )
18- - [ 7. Introduction to NumPy] ( 07-numpy.md )
19- - [ 8. Linear Algebra Refresher] ( 08-linear-algebra.md )
20- - [ 9. Introduction to Pandas] ( 09-pandas.md )
21- - [ 10. Summary] ( 10-summary.md )
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2312Module content on GitHub: https://github.com/DataTalksClub/machine-learning-zoomcamp/tree/master/01-intro
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22title : " Module 2: Machine Learning for Regression"
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1010This module covers the following topics:
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12- - [ 1. Car Price Prediction] ( 01-car-price-intro.md )
13- - [ 2. Data Preparation] ( 02-data-preparation.md )
14- - [ 3. Exploratory Data Analysis] ( 03-eda.md )
15- - [ 4. Validation Framework] ( 04-validation-framework.md )
16- - [ 5. Linear Regression] ( 05-linear-regression-simple.md )
17- - [ 6. Linear Regression in Vector & Matrix Form] ( 06-linear-regression-vector.md )
18- - [ 7. Training the Model (Normal Equation)] ( 07-linear-regression-training.md )
19- - [ 8. Baseline Model] ( 08-baseline-model.md )
20- - [ 9. Evaluating a Regression Model with RMSE] ( 09-rmse.md )
21- - [ 10. Validating an ML Model] ( 10-car-price-validation.md )
22- - [ 11. Feature Engineering] ( 11-feature-engineering.md )
23- - [ 12. Categorical Variables] ( 12-categorical-variables.md )
24- - [ 13. Regularization] ( 13-regularization.md )
25- - [ 14. Tuning Model] ( 14-tuning-model.md )
26- - [ 15. Using Model] ( 15-using-model.md )
27- - [ 16. Summary] ( 16-summary.md )
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2912Module content on GitHub: https://github.com/DataTalksClub/machine-learning-zoomcamp/tree/master/02-regression
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22title : " Module 3: Machine Learning for Classification"
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88# Module 3: Machine Learning for Classification
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1010This module covers the following topics:
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12- - [ 1. Churn Prediction Project] ( 01-churn-project.md )
13- - [ 2. Data Preparation] ( 02-data-preparation.md )
14- - [ 3. Setting Up the Validation Framework] ( 03-validation.md )
15- - [ 4. EDA] ( 04-eda.md )
16- - [ 5. Feature importance: Churn rate and risk ratio] ( 05-risk.md )
17- - [ 6. Feature importance: Mutual information] ( 06-mutual-info.md )
18- - [ 7. Feature importance: Correlation] ( 07-correlation.md )
19- - [ 8. One-hot encoding] ( 08-ohe.md )
20- - [ 9. Logistic Regression] ( 09-logistic-regression.md )
21- - [ 10. Training logistic regression with Scikit-Learn] ( 10-training-log-reg.md )
22- - [ 11. Model interpretation] ( 11-log-reg-interpretation.md )
23- - [ 12. Using the model] ( 12-using-log-reg.md )
24- - [ 13. Summary] ( 13-summary.md )
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26- Module content on GitHub: https://github.com/DataTalksClub/machine-learning-zoomcamp/tree/master/03-classification
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22title : " Module 4: Evaluation Metrics for Classification"
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88# Module 04: Evaluation
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1010This module covers the following topics:
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12- - [ 1. Evaluation metrics: session overview] ( 01-overview.md )
13- - [ 2. Accuracy and dummy model] ( 02-accuracy.md )
14- - [ 3. Confusion Table] ( 03-confusion-table.md )
15- - [ 4. Precision and Recall] ( 04-precision-recall.md )
16- - [ 5. ROC Curves] ( 05-roc.md )
17- - [ 6. ROC AUC] ( 06-auc.md )
18- - [ 7. Cross Validation] ( 07-cross-validation.md )
19- - [ 8. Summary] ( 08-summary.md )
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22title : " Module 5: Deploying Machine Learning Models"
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1010This module covers the following topics:
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12- - [ 1. Intro / Session overview] ( 01-intro.md )
13- - [ 2. Saving and loading the model] ( 02-pickle.md )
14- - [ 3. Web services: introduction to Flask] ( 03-flask-intro.md )
15- - [ 4. Serving the churn model with Flask] ( 04-flask-deployment.md )
16- - [ 5. Python virtual environment: Pipenv] ( 05-pipenv.md )
17- - [ 6. Environment management: Docker and Docker Hub] ( 06-docker.md )
18- - [ 7. Deployment to the cloud: AWS Elastic Beanstalk (optional)] ( 07-aws-eb.md )
19- - [ 8. Summary] ( 08-summary.md )
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22title : " Module 6: Decision Trees and Ensemble Learning"
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1010This module covers the following topics:
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12- - [ 1. Credit risk scoring project] ( 01-credit-risk.md )
13- - [ 2. Data cleaning and preparation] ( 02-data-prep.md )
14- - [ 3. Decision trees] ( 03-decision-trees.md )
15- - [ 4. Decision tree learning algorithm] ( 04-decision-tree-learning.md )
16- - [ 5. Decision trees parameter tuning] ( 05-decision-tree-tuning.md )
17- - [ 6. Ensemble learning and random forest] ( 06-random-forest.md )
18- - [ 7. Gradient boosting and XGBoost] ( 07-boosting.md )
19- - [ 8. XGBoost parameter tuning] ( 08-xgb-tuning.md )
20- - [ 9. Selecting the best model] ( 09-final-model.md )
21- - [ 10. Summary] ( 10-summary.md )
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22title : " Module 8: Neural Networks and Deep Learning"
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88# Module 8: Neural Networks and Deep Learning
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1010This module covers the following topics:
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12- - [ 1. Fashion classification] ( 01-fashion-classification.md )
13- - [ 1b. Setting up the Environment on Saturn Cloud] ( 01b-saturn-cloud.md )
14- - [ 2. TensorFlow and Keras] ( 02-tensorflow-keras.md )
15- - [ 3. Pre-trained convolutional neural networks] ( 03-pretrained-models.md )
16- - [ 4. Convolutional neural networks] ( 04-conv-neural-nets.md )
17- - [ 5. Transfer learning] ( 05-transfer-learning.md )
18- - [ 6. Adjusting the learning rate] ( 06-learning-rate.md )
19- - [ 7. Checkpointing] ( 07-checkpointing.md )
20- - [ 8. Adding more layers] ( 08-more-layers.md )
21- - [ 9. Regularization and dropout] ( 09-dropout.md )
22- - [ 10. Data augmentation] ( 10-augmentation.md )
23- - [ 11. Training a larger model] ( 11-large-model.md )
24- - [ 12. Using the model] ( 12-using-model.md )
25- - [ 13. Summary] ( 13-summary.md )
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22title : " Module 9: Serverless Deep Learning"
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88# Module 9: Serverless Deep Learning
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1010This module covers the following topics:
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12- - [ 1. Introduction to Serverless] ( 01-intro.md )
13- - [ 2. AWS Lambda] ( 02-aws-lambda.md )
14- - [ 3. TensorFlow Lite] ( 03-tensorflow-lite.md )
15- - [ 4. Preparing the code for Lambda] ( 04-preparing-code.md )
16- - [ 5. Preparing a Docker image] ( 05-docker-image.md )
17- - [ 6. Creating the lambda function] ( 06-creating-lambda.md )
18- - [ 7. API Gateway: exposing the lambda function] ( 07-api-gateway.md )
19- - [ 8. Summary] ( 08-summary.md )
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22title : " Module 10: Kubernetes and TensorFlow Serving"
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88# Module 10: Kubernetes and TensorFlow Serving
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1010This module covers the following topics:
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12- - [ 1. Overview] ( 01-overview.md )
13- - [ 2. TensorFlow Serving] ( 02-tensorflow-serving.md )
14- - [ 3. Creating a pre-processing service] ( 03-preprocessing.md )
15- - [ 4. Running everything locally with Docker-compose] ( 04-docker-compose.md )
16- - [ 5. Introduction to Kubernetes] ( 05-kubernetes-intro.md )
17- - [ 6. Deploying a simple service to Kubernetes] ( 06-kubernetes-simple-service.md )
18- - [ 7. Deploying TensorFlow models to Kubernetes] ( 07-kubernetes-tf-serving.md )
19- - [ 8. Deploying to EKS] ( 08-eks.md )
20- - [ 9. Summary] ( 09-summary.md )
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22title : " Module 11: KServe"
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88# Module 11: KServe
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1010This module covers the following topics:
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12- - [ 1. Overview] ( 01-overview.md )
13- - [ 2. Running KServe locally] ( 02-kserve-local.md )
14- - [ 3. Deploying a Scikit-Learn model with KServe] ( 03-kserve-sklearn.md )
15- - [ 4. Deploying custom Scikit-Learn images with KServe] ( 04-kserve-custom-image.md )
16- - [ 5. Serving TensorFlow models with KServe] ( 05-tensorflow-kserve.md )
17- - [ 6. KServe transformers] ( 06-kserve-transformers.md )
18- - [ 7. Deploying with KServe and EKS] ( 07-kserve-eks.md )
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