Machine Learning Zoomcamp

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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
• 4.5 ROC Curves
• 4.6 ROC AUC
• 4.7 Cross-Validation

5. Deploying Machine Learning Models

• 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. Decision Trees and Ensemble Learning

• 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

7. Midterm Project

• 7.1. practical project

8. Neural Networks and Deep Learning

• 8.1 Fashion classification
• 8.1 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. Serverless Deep Learning

• 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. Kubernetes and TensorFlow Serving

• 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. KServe

• 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
• 11.8 Summary
• 11.9 Explore more