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| # Machine Learning Zoomcamp | ||
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| <img src="../images/zoomcamp.jpg" /> | ||
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| <p align="center"> | ||
| <a href="https://airtable.com/shryxwLd0COOEaqXo"><img src="https://user-images.githubusercontent.com/875246/185755203-17945fd1-6b64-46f2-8377-1011dcb1a444.png" height="50" /></a> | ||
| </p> | ||
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| * Register at [DataTalks.Club](https://DataTalks.Club) and join the `#course-ml-zoomcamp` channel | ||
| * [Course telegram channel](https://t.me/mlzoomcamp) | ||
| * [Course playlist](https://www.youtube.com/playlist?list=PL3MmuxUbc_hIhxl5Ji8t4O6lPAOpHaCLR) | ||
| * For the 2022 edition, see more info in the [2022 Cohort section](#2022-cohort) | ||
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| ### Syllabus | ||
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| - [Introduction to Machine Learning](#1-introduction-to-machine-learning) | ||
| - [Machine Learning for Regression](#2-machine-learning-for-regression) | ||
| - [Machine Learning for Classification](#3-machine-learning-for-classification) | ||
| - [Evaluation Metrics for Classification](#4-evaluation-metrics-for-classification) | ||
| - [Deploying Machine Learning Models](#5-deploying-machine-learning-models) | ||
| - [Decision Trees and Ensemble Learning](#6-decision-trees-and-ensemble-learning) | ||
| - [Neural Networks and Deep Learning](#8-neural-networks-and-deep-learning) | ||
| - [Serverless Deep Learning](#9-serverless-deep-learning) | ||
| - [Kubernetes and TensorFlow Serving](#10-kubernetes-and-tensorflow-serving) | ||
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| ## Taking the course | ||
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| ### 2022 Cohort | ||
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| We start the course again in September 2022 | ||
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| * [Sign up here](https://airtable.com/shryxwLd0COOEaqXo) | ||
| * Register at [DataTalks.Club](https://DataTalks.Club/slack.html) and join the `#course-ml-zoomcamp` channel | ||
| * Join the [course telegram channel](https://t.me/mlzoomcamp) | ||
| * Subscribe to the [public google calendar](https://calendar.google.com/calendar/?cid=cGtjZ2tkbGc1OG9yb2lxa2Vwc2g4YXMzMmNAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ) (subscribing works from desktop only) | ||
| * [Tweet about it](https://ctt.ac/XZ6b9) | ||
| * Start date: September 5 | ||
| * If you have questions, check [FAQ](https://docs.google.com/document/d/1LpPanc33QJJ6BSsyxVg-pWNMplal84TdZtq10naIhD8/edit) | ||
| * All the materials specific to the 2022 will be in [the 2022 cohort folder](./cohorts/2022/) | ||
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| ### Self-paced mode | ||
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| You can take the course at your own pace. All the materials are freely available, and you can start learning at any time. | ||
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| To take the best out of this course, we recommened this: | ||
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| * Register at [DataTalks.Club](https://DataTalks.Club) and join the `#course-ml-zoomcamp` channel | ||
| * For each module, watch the videos and work through the code | ||
| * If you have any questions, ask them in the `#course-ml-zoomcamp` channel in Slack | ||
| * Do homework. There are solutions, but we advise to first attempt the homework yourself, and after that check the solutions | ||
| * Do at least one project. Two is better. Only this way you can make sure you're really learning. If you need feedback, use the `#course-ml-zoomcamp` channel | ||
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| Of course, you can take each module independently. | ||
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| ### Prerequisites | ||
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| * Prior programming experience (at least 1+ year) | ||
| * Being comfortable with command line | ||
| * No prior exposure to machine learning is required | ||
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| Nice to have but not mandatory | ||
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| * Python (but you can learn it during the course) | ||
| * Prior exposure to linear algebra will be helpful (e.g. you studied it in college but forgot) | ||
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| ## Asking questions | ||
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| The best way to get support is to use [DataTalks.Club's Slack](https://datatalks.club/slack.html). Join the [`#course-ml-zoomcamp`](https://app.slack.com/client/T01ATQK62F8/C0288NJ5XSA) channel. | ||
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| To make discussions in Slack more organized: | ||
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| * Follow [these recommendations](asking-questions.md) when asking for help | ||
| * Read the [DataTalks.Club community guidelines](https://datatalks.club/slack/guidelines.html) | ||
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| ## 1. [Introduction to Machine Learning](01-intro/) | ||
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| - 1.1 [Introduction to Machine Learning](01-intro/01-what-is-ml.md) | ||
| - 1.2 [ML vs Rule-Based Systems](01-intro/02-ml-vs-rules.md) | ||
| - 1.3 [Supervised Machine Learning](01-intro/03-supervised-ml.md) | ||
| - 1.4 [CRISP-DM](01-intro/04-crisp-dm.md) | ||
| - 1.5 [Model Selection Process](01-intro/05-model-selection.md) | ||
| - 1.6 [Setting up the Environment](01-intro/06-environment.md) | ||
| - 1.7 [Introduction to NumPy](01-intro/07-numpy.md) | ||
| - 1.8 [Linear Algebra Refresher](01-intro/08-linear-algebra.md) | ||
| - 1.9 [Introduction to Pandas](01-intro/09-pandas.md) | ||
| - 1.10 [Summary](01-intro/10-summary.md) | ||
| - 1.11 [Homework](01-intro/homework.md) | ||
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| ## 2. [Machine Learning for Regression](02-regression/) | ||
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| - 2.1 [Car price prediction project](02-regression/01-car-price-intro.md) | ||
| - 2.2 [Data preparation](02-regression/02-data-preparation.md) | ||
| - 2.3 [Exploratory data analysis](02-regression/03-eda.md) | ||
| - 2.4 [Setting up the validation framework](02-regression/04-validation-framework.md) | ||
| - 2.5 [Linear regression](02-regression/05-linear-regression-simple.md) | ||
| - 2.6 [Linear regression: vector form](02-regression/06-linear-regression-vector.md) | ||
| - 2.7 [Training linear regression: Normal equation](02-regression/07-linear-regression-training.md) | ||
| - 2.8 [Baseline model for car price prediction project](02-regression/08-baseline-model.md) | ||
| - 2.9 [Root mean squared error](02-regression/09-rmse.md) | ||
| - 2.10 [Using RMSE on validation data](02-regression/10-car-price-validation.md) | ||
| - 2.11 [Feature engineering](02-regression/11-feature-engineering.md) | ||
| - 2.12 [Categorical variables](02-regression/12-categorical-variables.md) | ||
| - 2.13 [Regularization](02-regression/13-regularization.md) | ||
| - 2.14 [Tuning the model](02-regression/14-tuning-model.md) | ||
| - 2.15 [Using the model](02-regression/15-using-model.md) | ||
| - 2.16 [Car price prediction project summary](02-regression/16-summary.md) | ||
| - 2.17 [Explore more](02-regression/17-explore-more.md) | ||
| - 2.18 [Homework](02-regression/homework.md) | ||
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| ## 3. [Machine Learning for Classification](03-classification/) | ||
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| - 3.1 [Churn prediction project](03-classification/01-churn-project.md) | ||
| - 3.2 [Data preparation](03-classification/02-data-preparation.md) | ||
| - 3.3 [Setting up the validation framework](03-classification/03-validation.md) | ||
| - 3.4 [EDA](03-classification/04-eda.md) | ||
| - 3.5 [Feature importance: Churn rate and risk ratio](03-classification/05-risk.md) | ||
| - 3.6 [Feature importance: Mutual information](03-classification/06-mutual-info.md) | ||
| - 3.7 [Feature importance: Correlation](03-classification/07-correlation.md) | ||
| - 3.8 [One-hot encoding](03-classification/08-ohe.md) | ||
| - 3.9 [Logistic regression](03-classification/09-logistic-regression.md) | ||
| - 3.10 [Training logistic regression with Scikit-Learn](03-classification/10-training-log-reg.md) | ||
| - 3.11 [Model interpretation](03-classification/11-log-reg-interpretation.md) | ||
| - 3.12 [Using the model](03-classification/12-using-log-reg.md) | ||
| - 3.13 [Summary](03-classification/13-summary.md) | ||
| - 3.14 [Explore more](03-classification/14-explore-more.md) | ||
| - 3.15 [Homework](03-classification/homework.md) | ||
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| ## 4. [Evaluation Metrics for Classification](04-evaluation/) | ||
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| - 4.1 [Evaluation metrics: session overview](04-evaluation/01-overview.md) | ||
| - 4.2 [Accuracy and dummy model](04-evaluation/02-accuracy.md) | ||
| - 4.3 [Confusion table](04-evaluation/03-confusion-table.md) | ||
| - 4.4 [Precision and Recall](04-evaluation/04-precision-recall.md) | ||
| - 4.5 [ROC Curves](04-evaluation/05-roc.md) | ||
| - 4.6 [ROC AUC](04-evaluation/06-auc.md) | ||
| - 4.7 [Cross-Validation](04-evaluation/07-cross-validation.md) | ||
| - 4.8 [Summary](04-evaluation/08-summary.md) | ||
| - 4.9 [Explore more](04-evaluation/09-explore-more.md) | ||
| - 4.10 [Homework](04-evaluation/homework.md) | ||
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| ## 5. [Deploying Machine Learning Models](05-deployment/) | ||
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| - 5.1 [Intro / Session overview](05-deployment/01-intro.md) | ||
| - 5.2 [Saving and loading the model](05-deployment/02-pickle.md) | ||
| - 5.3 [Web services: introduction to Flask](05-deployment/03-flask-intro.md) | ||
| - 5.4 [Serving the churn model with Flask](05-deployment/04-flask-deployment.md) | ||
| - 5.5 [Python virtual environment: Pipenv](05-deployment/05-pipenv.md) | ||
| - 5.6 [Environment management: Docker](05-deployment/06-docker.md) | ||
| - 5.7 [Deployment to the cloud: AWS Elastic Beanstalk (optional)](05-deployment/07-aws-eb.md) | ||
| - 5.8 [Summary](05-deployment/08-summary.md) | ||
| - 5.9 [Explore more](05-deployment/09-explore-more.md) | ||
| - 5.10 [Homework](05-deployment/homework.md) | ||
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| ## 6. [Decision Trees and Ensemble Learning](06-trees/) | ||
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| - 6.1 [Credit risk scoring project](06-trees/01-credit-risk.md) | ||
| - 6.2 [Data cleaning and preparation](06-trees/02-data-prep.md) | ||
| - 6.3 [Decision trees](06-trees/03-decision-trees.md) | ||
| - 6.4 [Decision tree learning algorithm](06-trees/04-decision-tree-learning.md) | ||
| - 6.5 [Decision trees parameter tuning](06-trees/05-decision-tree-tuning.md) | ||
| - 6.6 [Ensemble learning and random forest](06-trees/06-random-forest.md) | ||
| - 6.7 [Gradient boosting and XGBoost](06-trees/07-boosting.md) | ||
| - 6.8 [XGBoost parameter tuning](06-trees/08-xgb-tuning.md) | ||
| - 6.9 [Selecting the best model](06-trees/09-final-model.md) | ||
| - 6.10 [Summary](06-trees/10-summary.md) | ||
| - 6.11 [Explore more](06-trees/11-explore-more.md) | ||
| - 6.12 [Homework](06-trees/homework.md) | ||
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| ## 7. [Production-Ready Machine Learning (Bento ML)](07-bentoml-production/) | ||
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| - 7.1 [Intro/Session Overview](07-bentoml-production/01-intro.md) | ||
| - 7.2 [Building Your Prediction Service with BentoML](07-bentoml-production/02-build-bento-service.md) | ||
| - 7.3 [Deploying Your Prediction Service](07-bentoml-production/03-deploy-bento-service.md) | ||
| - 7.4 [Sending, Receiving and Validating Data](07-bentoml-production/04-validation.md) | ||
| - 7.5 [High-Performance Serving](07-bentoml-production/05-high-performance.md) | ||
| - 7.6 [Bento Production Deployment](07-bentoml-production/06-production-deployment.md) | ||
| - 7.7 [(Optional) Advanced Example: Deploying Stable Diffusion Model](07-bentoml-production/07-stable-diffusion.md) | ||
| - 7.8 [Summary](07-bentoml-production/08-summary.md) | ||
| - 7.9 [Homework](07-bentoml-production/homework.md) | ||
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| ## [Midterm Project](projects/#midterm-project) | ||
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| Putting everything we've learned so far in practice! | ||
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| ## 8. [Neural Networks and Deep Learning](08-deep-learning/) | ||
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| - 8.1 [Fashion classification](08-deep-learning/01-fashion-classification.md) | ||
| - 8.1b [Setting up the Environment on Saturn Cloud](08-deep-learning/01b-saturn-cloud.md) | ||
| - 8.2 [TensorFlow and Keras](08-deep-learning/02-tensorflow-keras.md) | ||
| - 8.3 [Pre-trained convolutional neural networks](08-deep-learning/03-pretrained-models.md) | ||
| - 8.4 [Convolutional neural networks](08-deep-learning/04-conv-neural-nets.md) | ||
| - 8.5 [Transfer learning](08-deep-learning/05-transfer-learning.md) | ||
| - 8.6 [Adjusting the learning rate](08-deep-learning/06-learning-rate.md) | ||
| - 8.7 [Checkpointing](08-deep-learning/07-checkpointing.md) | ||
| - 8.8 [Adding more layers](08-deep-learning/08-more-layers.md) | ||
| - 8.9 [Regularization and dropout](08-deep-learning/09-dropout.md) | ||
| - 8.10 [Data augmentation](08-deep-learning/10-augmentation.md) | ||
| - 8.11 [Training a larger model](08-deep-learning/11-large-model.md) | ||
| - 8.12 [Using the model](08-deep-learning/12-using-model.md) | ||
| - 8.13 [Summary](08-deep-learning/13-summary.md) | ||
| - 8.14 [Explore more](08-deep-learning/14-explore-more.md) | ||
| - 8.15 [Homework](08-deep-learning/homework.md) | ||
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| ## 9. [Serverless Deep Learning](09-serverless/) | ||
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| - 9.1 [Introduction to Serverless](09-serverless/01-intro.md) | ||
| - 9.2 [AWS Lambda](09-serverless/02-aws-lambda.md) | ||
| - 9.3 [TensorFlow Lite](09-serverless/03-tensorflow-lite.md) | ||
| - 9.4 [Preparing the code for Lambda](09-serverless/04-preparing-code.md) | ||
| - 9.5 [Preparing a Docker image](09-serverless/05-docker-image.md) | ||
| - 9.6 [Creating the lambda function](09-serverless/06-creating-lambda.md) | ||
| - 9.7 [API Gateway: exposing the lambda function](09-serverless/07-api-gateway.md) | ||
| - 9.8 [Summary](09-serverless/08-summary.md) | ||
| - 9.9 [Explore more](09-serverless/09-explore-more.md) | ||
| - 9.10 [Homework](09-serverless/homework.md) | ||
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| ## 10. [Kubernetes and TensorFlow Serving](10-kubernetes/) | ||
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| - 10.1 [Overview](10-kubernetes/01-overview.md) | ||
| - 10.2 [TensorFlow Serving](10-kubernetes/02-tensorflow-serving.md) | ||
| - 10.3 [Creating a pre-processing service](10-kubernetes/03-preprocessing.md) | ||
| - 10.4 [Running everything locally with Docker-compose](10-kubernetes/04-docker-compose.md) | ||
| - 10.5 [Introduction to Kubernetes](10-kubernetes/05-kubernetes-intro.md) | ||
| - 10.6 [Deploying a simple service to Kubernetes](10-kubernetes/06-kubernetes-simple-service.md) | ||
| - 10.7 [Deploying TensorFlow models to Kubernetes](10-kubernetes/07-kubernetes-tf-serving.md) | ||
| - 10.8 [Deploying to EKS](10-kubernetes/08-eks.md) | ||
| - 10.9 [Summary](10-kubernetes/09-summary.md) | ||
| - 10.10 [Explore more](10-kubernetes/10-explore-more.md) | ||
| - 10.11 [Homework](10-kubernetes/homework.md) | ||
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| ## 11. [KServe](11-kserve/) (optional) | ||
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| - 11.1 [Overview](11-kserve/01-overview.md) | ||
| - 11.2 [Running KServe locally](11-kserve/02-kserve-local.md) | ||
| - 11.3 [Deploying a Scikit-Learn model with KServe](11-kserve/03-kserve-sklearn.md) | ||
| - 11.4 [Deploying custom Scikit-Learn images with KServe](11-kserve/04-kserve-custom-image.md) | ||
| - 11.5 [Serving TensorFlow models with KServe](11-kserve/05-tensorflow-kserve.md) | ||
| - 11.6 [KServe transformers](11-kserve/06-kserve-transformers.md) | ||
| - 11.7 [Deploying with KServe and EKS](11-kserve/07-kserve-eks.dm) | ||
| - 11.8 [Summary](11-kserve/08-summary.md) | ||
| - 11.9 [Explore more](11-kserve/09-explore-more.md) | ||
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| ## [Capstone Project 1](projects/#capstone-1) | ||
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| Putting everything we've learned so far in practice one more time! | ||
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| ## [Article](article/) | ||
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| Writing an article about something not covered in the course. | ||
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| ## [Capstone project 2](projects/#capstone-2) (optional) | ||
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| For those who love projects! | ||
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| ## [Image classification competition](https://www.kaggle.com/competitions/kitchenware-classification/) | ||
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| If you liked our deep learning module, join us to build a model for classifying cups, glasses, plates, spoons, forks and knives. | ||
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| Submit your learning in public links [here](https://forms.gle/yBo5rfN1WkSqQ5xj8) | ||
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| ## Previous cohorts | ||
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| ### 2021 Cohort | ||
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| * [Homeworks](cohorts/2021) | ||
| * [The 100 leaderboard](cohorts/2021/leaderboard.md) | ||
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| ## Our other courses | ||
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| If you liked this course, you'll like other courses from us: | ||
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| * [Data Engineering Zoomcamp - free 9-week course about Data Engineering](https://github.com/DataTalksClub/data-engineering-zoomcamp/) | ||
| * [MLOps Zoomcamp - free 10-week course about MLOps](https://github.com/DataTalksClub/mlops-zoomcamp) | ||
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| ## Supporters and partners | ||
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| Thanks to the course sponsors for making it possible to run this course | ||
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| <p align="center"> | ||
| <a href="https://www.bentoml.com/"> | ||
| <img height="100" src="../images/bentoml.png"> | ||
| </a> | ||
| </p> | ||
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| Thanks to our friends for spreading the word about the course | ||
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| <p align="center"> | ||
| <a href="https://dphi.tech/"> | ||
| <img height="75" src="https://datatalks.club/images/partners/dphi.png"> | ||
| </a> | ||
| </p> | ||
| Content moved to [https://github.com/DataTalksClub/machine-learning-zoomcamp](https://github.com/DataTalksClub/machine-learning-zoomcamp) |