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The repository contains all the work including projects, notes, and articles related to ML Engineering while I am learning.

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# MACHINE LEARNING ENGINEERING

This repository contains all the coursework I am going through from [Machine Learning Zoomcamp](https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp), which includes projects, notebooks, notes, and homework.

## :book: CONTENT

### 1. [Introduction to Machine Learning](https://github.com/MuhammadAwon/ml-engineering/tree/main/01-intro)

- 1.1 [What is Machine Learning](https://github.com/MuhammadAwon/ml-engineering/tree/main/01-intro#11-what-is-machine-learning)
- 1.2 [Machine Learning vs Rule-Based System](https://github.com/MuhammadAwon/ml-engineering/tree/main/01-intro#12-machine-learning-vs-rule-based-system)
- 1.3 [Supervised Machine Learning](https://github.com/MuhammadAwon/ml-engineering/tree/main/01-intro#13-supervised-machine-learning)
- 1.4 [CRISP-DM](https://github.com/MuhammadAwon/ml-engineering/tree/main/01-intro#14-crisp-dm)
- 1.5 [The Model Selection Process](https://github.com/MuhammadAwon/ml-engineering/tree/main/01-intro#15-the-model-selection-process)
- 1.6 [Setting Up the Environment](https://github.com/MuhammadAwon/ml-engineering/tree/main/01-intro#16-setting-up-the-environment)
- 1.7 [Introduction to NumPy](https://github.com/MuhammadAwon/ml-engineering/tree/main/01-intro#17-introduction-to-numpy)
- 1.8 [Linear Algebra Refresher](https://github.com/MuhammadAwon/ml-engineering/tree/main/01-intro#18-linear-algebra-refresher)
- 1.9 [Introduction to Pandas](https://github.com/MuhammadAwon/ml-engineering/tree/main/01-intro#19-introduction-to-pandas)

### 2. [Machine Learning for Regression](https://github.com/MuhammadAwon/ml-engineering/tree/main/02-regression)

- 2.1 [Car Price Prediction Project](https://github.com/MuhammadAwon/ml-engineering/tree/main/02-regression#21-car-price-prediction-project)
- 2.2 [Data Preparation](https://github.com/MuhammadAwon/ml-engineering/tree/main/02-regression#22-data-preparation)
- 2.3 [Exploratory Data Analysis](https://github.com/MuhammadAwon/ml-engineering/tree/main/02-regression#23-exploratory-data-analysis)
- 2.4 [Setting Up the Validation Framework](https://github.com/MuhammadAwon/ml-engineering/tree/main/02-regression#24-setting-up-the-validation-framework)
- 2.5 [Linear Regression](https://github.com/MuhammadAwon/ml-engineering/tree/main/02-regression#25-linear-regression-simple)
- 2.6 [Linear Regression: Vector Form](https://github.com/MuhammadAwon/ml-engineering/tree/main/02-regression#26-linear-regression-vector)
- 2.7 [Training Linear Regression: Normal Equation](https://github.com/MuhammadAwon/ml-engineering/tree/main/02-regression#27-training-linear-regression-normal-equation)
- 2.8 [Baseline Model for Car Price Prediction Project](https://github.com/MuhammadAwon/ml-engineering/tree/main/02-regression#28-baseline-model-for-car-price-prediction-project)
- 2.9 [Root Mean Squared Error](https://github.com/MuhammadAwon/ml-engineering/tree/main/02-regression#29-root-mean-squared-error)
- 2.10 [Using RMSE on Validation Data](https://github.com/MuhammadAwon/ml-engineering/tree/main/02-regression#210-using-rmse-on-validation-data)
- 2.11 [Feature Engineering](https://github.com/MuhammadAwon/ml-engineering/tree/main/02-regression#211-feature-engineering)
- 2.12 [Categorical Variables](https://github.com/MuhammadAwon/ml-engineering/tree/main/02-regression#212-categorical-variables)
- 2.13 [Regularization](https://github.com/MuhammadAwon/ml-engineering/tree/main/02-regression#213-regularization)
- 2.14 [Tuning the Model](https://github.com/MuhammadAwon/ml-engineering/tree/main/02-regression#214-tuning-the-model)
- 2.15 [Using the Model](https://github.com/MuhammadAwon/ml-engineering/tree/main/02-regression#215-using-the-model-on-test-data)
- 2.16 [Car Price Prediction Project Summary](https://github.com/MuhammadAwon/ml-engineering/tree/main/02-regression#216-car-price-prediction-project-summary)
- 2.17 [Explore More](https://github.com/MuhammadAwon/ml-engineering/tree/main/02-regression#217-explore-more)

### 3. [Machine Learning for Classification](https://github.com/MuhammadAwon/ml-engineering/tree/main/03-classification)

- 3.1 [Chrun Prediction Project](https://github.com/MuhammadAwon/ml-engineering/tree/main/03-classification#31-churn-project)
- 3.2 [Data Preparation](https://github.com/MuhammadAwon/ml-engineering/tree/main/03-classification#32-data-preparation)
- 3.3 [Setting Up the Validation Framework](https://github.com/MuhammadAwon/ml-engineering/tree/main/03-classification#33-setting-up-the-validation-framework)
- 3.4 [EDA](https://github.com/MuhammadAwon/ml-engineering/tree/main/03-classification#34-eda)
- 3.5 [Feature Importance: Churn Rate and Risk Ratio](https://github.com/MuhammadAwon/ml-engineering/tree/main/03-classification#35-feature-importance-churn-rate-and-risk-ratio)
- 3.6 [Feature Importance: Mutual Information](https://github.com/MuhammadAwon/ml-engineering/tree/main/03-classification#36-feature-importance-mutual-information)
- 3.7 [Feature Importance: Correlation](https://github.com/MuhammadAwon/ml-engineering/tree/main/03-classification#37-feature-importance-correlation)
- 3.8 [One-Hot Encoding](https://github.com/MuhammadAwon/ml-engineering/tree/main/03-classification#38-one-hot-encoding)
- 3.9 [Logistic Regression](https://github.com/MuhammadAwon/ml-engineering/tree/main/03-classification#39-logistic-regression)
- 3.10 [Training Logistic Regression with Scikit-Learn](https://github.com/MuhammadAwon/ml-engineering/tree/main/03-classification#310-training-logistic-regression-with-scikit-learn)
- 3.11 [Model Interpretation](https://github.com/MuhammadAwon/ml-engineering/tree/main/03-classification#311-model-interpretation)
- 3.12 [Using the Model](https://github.com/MuhammadAwon/ml-engineering/tree/main/03-classification#312-using-the-model)
- 3.13 [Summary](https://github.com/MuhammadAwon/ml-engineering/tree/main/03-classification#313-summary)
- 3.14 [Explore More](https://github.com/MuhammadAwon/ml-engineering/tree/main/03-classification#314-explore-more)

### 4. [Evaluation Metrics for Classification](https://github.com/MuhammadAwon/ml-engineering/tree/main/04-evaluation)

- 4.1 [Evaluation Metrics: Session Overview](https://github.com/MuhammadAwon/ml-engineering/tree/main/04-evaluation#41-evaluation-metrics-session-overview)
- 4.2 [Accuracy and Dummy Model](https://github.com/MuhammadAwon/ml-engineering/tree/main/04-evaluation#42-accuracy-and-dummy-model)
- 4.3 [Confusion Table](https://github.com/MuhammadAwon/ml-engineering/tree/main/04-evaluation#43-confusion-table)
- 4.4 [Precision and Recall](https://github.com/MuhammadAwon/ml-engineering/tree/main/04-evaluation#44-precision-and-recall)
- 4.5 [ROC Curves](https://github.com/MuhammadAwon/ml-engineering/tree/main/04-evaluation#45-roc-curves)
- 4.6 [ROC AUC](https://github.com/MuhammadAwon/ml-engineering/tree/main/04-evaluation#46-roc-auc)
- 4.7 [Cross-Validation](https://github.com/MuhammadAwon/ml-engineering/tree/main/04-evaluation#47-cross-validation)
- 4.8 [Summary](https://github.com/MuhammadAwon/ml-engineering/tree/main/04-evaluation#48-summary)
- 4.9 [Explore More](https://github.com/MuhammadAwon/ml-engineering/tree/main/04-evaluation#49-explore-more)

### 5. [Deploying Machine Learning Models](https://github.com/MuhammadAwon/ml-engineering/tree/main/05-deployment)

- 5.1 [Session Overview](https://github.com/MuhammadAwon/ml-engineering/tree/main/05-deployment#51-intro--session-overview)
- 5.2 [Saving and Loading the Model](https://github.com/MuhammadAwon/ml-engineering/tree/main/05-deployment#52-saving-and-loading-the-model)
- 5.3 [Web Services: Introduction to Flask](https://github.com/MuhammadAwon/ml-engineering/tree/main/05-deployment#53-web-services-introduction-to-flask)
- 5.4 [Serving the Churn Model with Flask](https://github.com/MuhammadAwon/ml-engineering/tree/main/05-deployment#54-serving-the-churn-model-with-flask)
- 5.5 [Python Virtual Environment: Pipenv](https://github.com/MuhammadAwon/ml-engineering/tree/main/05-deployment#55-python-virtual-environment-pipenv)
- 5.6 [Environment Management: Docker](https://github.com/MuhammadAwon/ml-engineering/tree/main/05-deployment#56-environment-management-docker)
- 5.7 [Deployment to the Cloud: AWS Elastic Beanstalk](https://github.com/MuhammadAwon/ml-engineering/tree/main/05-deployment#57-deployment-to-the-cloud-aws-elastic-beanstalk-optional) (optional)
- 5.8 [Summary](https://github.com/MuhammadAwon/ml-engineering/tree/main/05-deployment#58-summary)
- 5.9 [Explore More](https://github.com/MuhammadAwon/ml-engineering/tree/main/05-deployment#59-explore-more)
- [Deployment Tutorials](https://github.com/MuhammadAwon/ml-engineering/tree/main/05-deployment#deployment-tutorials)

### 6. [Decision Trees and Ensemble Learning](https://github.com/MuhammadAwon/ml-engineering/tree/main/06-trees)

- 6.1 [Credit Risk Scoring Project](https://github.com/MuhammadAwon/ml-engineering/tree/main/06-trees#61-session-overview-credit-risk-scoring-project)
- 6.2 [Data Cleaning and Preparation](https://github.com/MuhammadAwon/ml-engineering/tree/main/06-trees#62-data-cleaning-and-preparation)
- 6.3 [Decision Trees](https://github.com/MuhammadAwon/ml-engineering/tree/main/06-trees#63-decision-trees)
- 6.4 [Decision Tree Learning Algorithm](https://github.com/MuhammadAwon/ml-engineering/tree/main/06-trees#64-decision-tree-learning-algorithm)
- 6.5 [Decision Trees Parameter Turning](https://github.com/MuhammadAwon/ml-engineering/tree/main/06-trees#65-decision-trees-parameter-tuning)
- 6.6 [Ensemble Learning and Random Forest](https://github.com/MuhammadAwon/ml-engineering/tree/main/06-trees#66-ensembles-and-random-forest)
- 6.7 [Gradient Boosting and XGBoost](https://github.com/MuhammadAwon/ml-engineering/tree/main/06-trees#67-gradient-boosting-and-xgboost)
- 6.8 [XGBoost Parameter Tuning](https://github.com/MuhammadAwon/ml-engineering/tree/main/06-trees#68-xgboost-parameter-tuning)
- 6.9 [Selecting the Best Model](https://github.com/MuhammadAwon/ml-engineering/tree/main/06-trees#69-selecting-the-final-model)
- 6.10 [Summary](https://github.com/MuhammadAwon/ml-engineering/tree/main/06-trees#610-summary)
- 6.11 [Explore More](https://github.com/MuhammadAwon/ml-engineering/tree/main/06-trees#611-explore-more)

### 7. [Production-Ready Machine Learning (Bento ML)](https://github.com/MuhammadAwon/ml-engineering/tree/main/07-bentoml-production#7-bentoml-production)

- [7.1 Intro/Session Overview](https://github.com/MuhammadAwon/ml-engineering/tree/main/07-bentoml-production#71-intro-and-overview)
- [7.2 Building Your Prediction Service with BentoML](https://github.com/MuhammadAwon/ml-engineering/tree/main/07-bentoml-production#72-build-bento-service)
- [7.3 Deploying Your Prediction Service](https://github.com/MuhammadAwon/ml-engineering/tree/main/07-bentoml-production#73-deploy-bento-service)
- [7.4 Sending, Receiving and Validating Data](https://github.com/MuhammadAwon/ml-engineering/tree/main/07-bentoml-production#74-sending-receiving-and-validating-data)
- [7.5 High-Performance Serving](https://github.com/MuhammadAwon/ml-engineering/tree/main/07-bentoml-production#75-high-performance-model-serving)
- [7.6 Bento Production Deployment](https://github.com/MuhammadAwon/ml-engineering/tree/main/07-bentoml-production#76-bento-production-deployment)
- [7.7 (Optional) Advanced Example: Deploying Stable Diffusion Model](https://github.com/MuhammadAwon/ml-engineering/tree/main/07-bentoml-production#77-optional-advanced-example-deploying-stable-diffusion-model)
- [7.8 Summary](https://github.com/MuhammadAwon/ml-engineering/tree/main/07-bentoml-production#78-summary)

### 8. [Neural Networks and Deep Learning](https://github.com/MuhammadAwon/ml-engineering/tree/main/08-deep-learning)

- 8.1 [Fashion Classification](https://github.com/MuhammadAwon/ml-engineering/tree/main/08-deep-learning#81-fashion-classification)
- 8.1b [Setting Up the Environment on Saturn Cloud](https://github.com/MuhammadAwon/ml-engineering/tree/main/08-deep-learning#81b-setting-up-the-environment-on-saturn-cloud)
- 8.2 [TensorFlow and Keras](https://github.com/MuhammadAwon/ml-engineering/tree/main/08-deep-learning#82-tensorflow-and-keras)
- 8.3 [Pre-Trained Convolutional Neural Networks](https://github.com/MuhammadAwon/ml-engineering/tree/main/08-deep-learning#83-pre-trained-convolutional-neural-networks)
- 8.4 [Convolution Neural Networks](https://github.com/MuhammadAwon/ml-engineering/tree/main/08-deep-learning#84-convolutional-neural-networks)
- 8.5 [Transfer Learning](https://github.com/MuhammadAwon/ml-engineering/tree/main/08-deep-learning#85-transfer-learning)
- 8.6 [Adjusting the Learning Rate](https://github.com/MuhammadAwon/ml-engineering/tree/main/08-deep-learning#86-adjusting-the-learning-rate)
- 8.7 [Checkpointing](https://github.com/MuhammadAwon/ml-engineering/tree/main/08-deep-learning#87-checkpointing)
- 8.8 [Adding More Layers](https://github.com/MuhammadAwon/ml-engineering/tree/main/08-deep-learning#88-adding-more-layers)
- 8.9 [Regularization and Dropout](https://github.com/MuhammadAwon/ml-engineering/tree/main/08-deep-learning#89-regularization-and-dropout)
- 8.10 [Data Augmentation](https://github.com/MuhammadAwon/ml-engineering/tree/main/08-deep-learning#810-data-augmentation)
- 8.11 [Training a Larger Model](https://github.com/MuhammadAwon/ml-engineering/tree/main/08-deep-learning#811-training-a-larger-model)
- 8.12 [Using the Model](https://github.com/MuhammadAwon/ml-engineering/tree/main/08-deep-learning#812-using-the-model)
- 8.13 [Summary](https://github.com/MuhammadAwon/ml-engineering/tree/main/08-deep-learning#813-summary)
- 8.14 [Explore More](https://github.com/MuhammadAwon/ml-engineering/tree/main/08-deep-learning#814-explore-more)

### 9. [Severless Deep Learning](https://github.com/MuhammadAwon/ml-engineering/tree/main/09-serverless)

- 9.1 [Introduction to Severless](https://github.com/MuhammadAwon/ml-engineering/tree/main/09-serverless#91-introduction-to-severless)
- 9.2 [AWS Lambda](https://github.com/MuhammadAwon/ml-engineering/tree/main/09-serverless#92-aws-lambda)
- 9.3 [TensorFlow Lite](https://github.com/MuhammadAwon/ml-engineering/tree/main/09-serverless#93-tensorflow-lite)
- 9.4 [Preparing the Code for Lambda](https://github.com/MuhammadAwon/ml-engineering/tree/main/09-serverless#94-preparing-the-code-for-lambda)
- 9.5 [Preparing a Docker Image](https://github.com/MuhammadAwon/ml-engineering/tree/main/09-serverless#95-preparing-a-docker-image)
- 9.6 [Creating the Lambda Function](https://github.com/MuhammadAwon/ml-engineering/tree/main/09-serverless#96-creating-the-lambda-function)
- 9.7 [API Gateway: Exposing the Lambda Function](https://github.com/MuhammadAwon/ml-engineering/tree/main/09-serverless#97-api-gateway-exposing-the-lambda-function)
- 9.8 [Summary](https://github.com/MuhammadAwon/ml-engineering/tree/main/09-serverless#98-summary)
- 9.9 [Explore More](https://github.com/MuhammadAwon/ml-engineering/tree/main/09-serverless#99-explore-more)

### 10. [Kubernetes and TensorFlow Serving](https://github.com/MuhammadAwon/ml-engineering/tree/main/10-kubernetes)

- 10.1 [Overview](https://github.com/MuhammadAwon/ml-engineering/tree/main/10-kubernetes#101-overview)
- 10.2 [TensorFlow Serving](https://github.com/MuhammadAwon/ml-engineering/tree/main/10-kubernetes#102-tensorflow-serving)
- 10.3 [Creating a Preprocessing Service](https://github.com/MuhammadAwon/ml-engineering/tree/main/10-kubernetes#103-creating-a-preprocessing-service)
- 10.4 [Running Everything Locally with Docker Compose](https://github.com/MuhammadAwon/ml-engineering/tree/main/10-kubernetes#104-running-everything-locally-with-docker-compose)
- 10.5 [Introduction to Kubernetes](https://github.com/MuhammadAwon/ml-engineering/tree/main/10-kubernetes#105-introduction-to-kubernetes)
- 10.6 [Deploying a Simple Service to Kubernetes](https://github.com/MuhammadAwon/ml-engineering/tree/main/10-kubernetes#106-deploying-a-simple-service-to-kubernetes)
- 10.7 [Deploying TensorFlow Models to Kubernetes](https://github.com/MuhammadAwon/ml-engineering/tree/main/10-kubernetes#107-deploying-tensorflow-models-to-kubernetes)
- 10.8 [Deploying to EKS](https://github.com/MuhammadAwon/ml-engineering/tree/main/10-kubernetes#108-deploying-to-eks)
- 10.9 [Summary](https://github.com/MuhammadAwon/ml-engineering/tree/main/10-kubernetes#109-summary)
- 10.10 [Explore More](https://github.com/MuhammadAwon/ml-engineering/tree/main/10-kubernetes#1010-explore-more)
- [Commands References](https://github.com/MuhammadAwon/ml-engineering/tree/main/10-kubernetes#command-references-used-in-the-session-10)

### 11. [KServe](https://github.com/MuhammadAwon/ml-engineering/tree/main/11-kserve)

Information to be added!

### [Homework](https://github.com/MuhammadAwon/ml-engineering/tree/main/homework)

- [HW1](https://github.com/MuhammadAwon/ml-engineering/blob/main/homework/session1/session1-homework.ipynb)
- [HW2](https://github.com/MuhammadAwon/ml-engineering/blob/main/homework/session2/session2-homework.ipynb)
- [HW3](https://github.com/MuhammadAwon/ml-engineering/blob/main/homework/session3/session3-homework.ipynb)
- [HW4](https://github.com/MuhammadAwon/ml-engineering/blob/main/homework/session4/session4-homework.ipynb)
- [HW5](https://github.com/MuhammadAwon/ml-engineering/tree/main/homework/session5/homework)
- [HW6](https://github.com/MuhammadAwon/ml-engineering/blob/main/homework/session6/session6-homework.ipynb)
- [HW7](https://github.com/MuhammadAwon/ml-engineering/tree/main/homework/session7/homework7)
- [HW8](https://github.com/MuhammadAwon/ml-engineering/blob/main/homework/session8/dino-or-dragon.ipynb)
- [HW9](https://github.com/MuhammadAwon/ml-engineering/tree/main/homework/session9/homework9)
- [HW10](https://github.com/MuhammadAwon/ml-engineering/tree/main/homework/session10)

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