{"repo":"nathadriele/machine-learning-zoomcamp","free":true,"listed":false,"github":"https://github.com/nathadriele/machine-learning-zoomcamp","clone":"git clone https://github.com/nathadriele/machine-learning-zoomcamp.git","description":"The Machine Learning Zoomcamp teaches foundational and advanced ML concepts using tools like NumPy, Pandas, Scikit-Learn, TensorFlow, XGBoost, Flask, Docker, AWS, Kubernetes, and KServe. It covers regression, classification, evaluation metrics, neural networks, deployment strategies, and end-to-end projects to bridge theory and practice.","language":"Jupyter Notebook","stars":22,"topics":["aws","classification","deployment","docker","flask","kserve","kubernetes","metrics","neural-networks","numpy"],"license":null,"category":"deployment-docker-iac","readme_excerpt":"Machine Learning Zoomcamp 1. Introduction to Machine Learning - 1.1 Introduction to Machine Learning - 1.2 ML vs Rule-Based Systems - 1.3 Supervised Machine Learning - 1.4 CRISP-DM - 1.5 Model Selection Process - 1.6 Setting up the Environment - 1.7 Introduction to NumPy - 1.8 Linear Algebra Refresher - 1.9 Introduction to Pandas 2. Machine Learning for Regression - 2.1 Car price prediction project - 2.2 Data preparation - 2.3 Exploratory data analysis - 2.4 Setting up the validation framework - 2.5 Linear regression - 2.6 Linear regression: vector form - 2.7 Training linear regression: Normal equation - 2.8 Baseline model for car price prediction project - 2.9 Root mean squared error - 2.10 Using RMSE on validation data - 2.11 Feature engineering - 2.12 Categorical variables - 2.13 Regularization - 2.14 Tuning the model - 2.15 Using the model - 2.16 Car price prediction project summary 3. Machine Learning for Classification - 3.1 Churn prediction project - 3.2 Data preparation - 3.3 Setting up the validation framework - 3.4 EDA - 3.5 Feature importance: Churn rate and risk ratio - 3.6 Feature importance: Mutual information - 3.7 Feature importance: Correlation - 3.8 One-hot encoding - 3.9 Logistic regression - 3.10 Training logistic regression with Scikit-Learn - 3.11 Model interpretation - 3.12 Using the model 4. Evaluation Metrics for Classification - 4.1 Evaluation metrics: session overview - 4.2 Accuracy and dummy model - 4.3 Confusion table - 4.4 Precision and Recall - ","default_branch":null,"files":null,"tree":[],"storefront":"/r/nathadriele","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/nathadriele/machine-learning-zoomcamp/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}