DatatalksClub ML Zoomcamp course registration and materials LINK
- 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
- 2.1 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
- 3.1 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
- 4.1 Evaluation metrics: session overview
- 4.2 Accuracy and dummy model
- 4.3 Confusion table
- 4.4 Precision and Recall
- 4.5 ROC Curves
- 4.6 ROC AUC
- 4.7 Cross-Validation
- 5.1 Intro / Session overview
- 5.2 Saving and loading the model
- 5.3 Web services: introduction to Flask
- 5.4 Serving the churn model with Flask
- 5.5 Python virtual environment: Pipenv
- 5.6 Environment management: Docker
- 5.7 Deployment to the cloud: AWS Elastic Beanstalk (optional)
- 6.1 Credit risk scoring project
- 6.2 Data cleaning and preparation
- 6.3 Decision trees
- 6.4 Decision tree learning algorithm
- 6.5 Decision trees parameter tuning
- 6.6 Ensemble learning and random forest
- 6.7 Gradient boosting and XGBoost
- 6.8 XGBoost parameter tuning
- 6.9 Selecting the best model
- 8.1 Fashion classification
- 8.1b Setting up the Environment on Saturn Cloud
- 8.2 TensorFlow and Keras
- 8.3 Pre-trained convolutional neural networks
- 8.4 Convolutional neural networks
- 8.5 Transfer learning
- 8.6 Adjusting the learning rate
- 8.7 Checkpointing
- 8.8 Adding more layers
- 8.9 Regularization and dropout
- 8.10 Data augmentation
- 8.11 Training a larger model
- 8.12 Using the model
- 9.1 Introduction to Serverless
- 9.2 AWS Lambda
- 9.3 TensorFlow Lite
- 9.4 Preparing the code for Lambda
- 9.5 Preparing a Docker image
- 9.6 Creating the lambda function
- 9.7 API Gateway: exposing the lambda function
- 10.1 Overview
- 10.2 TensorFlow Serving
- 10.3 Creating a pre-processing service
- 10.4 Running everything locally with Docker-compose
- 10.5 Introduction to Kubernetes
- 10.6 Deploying a simple service to Kubernetes
- 10.7 Deploying TensorFlow models to Kubernetes
- 10.8 Deploying to EKS
- 11.1 Overview
- 11.2 Running KServe locally
- 11.3 Deploying a Scikit-Learn model with KServe
- 11.4 Deploying custom Scikit-Learn images with KServe
- 11.5 Serving TensorFlow models with KServe
- 11.6 KServe transformers
- 11.7 Deploying with KServe and EKS