course-zoomcamp

Machine Learning Zoomcamp

• Course overview video and slides
• Course playlist
• Register for the course here (you can register for the course at any point of time!)
• Public calendar (subscribing works from desktop only)
• Register at DataTalks.Club and join the #course-ml-zoomcamp channel to talk about the course

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
• 1.10 Summary
• 1.11 Homework

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
• 2.17 Explore more
• 2.18 Homework

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
• 3.13 Summary
• 3.14 Explore more
• 3.15 Homework

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
• 4.8 Summary
• 4.9 Explore more
• 4.10 Homework

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)
• 5.8 Summary
• 5.9 Explore more
• 5.10 Homework

6. Decision Trees and Ensemble Learning

• Credit risk scoring project
• Data cleaning
• Data preparation
• Decision trees
• Decision tree learning algorithm
• impurity
• split
• stopping criteria
• Decision trees parameter tuning
• Ensembles and random forest
• Random forest in sklearn
• Random forest parameter tuning
• Gradient boosting
• eXtreme Gradient Boosting - XGBoost
• training
• watchlist
• XGBoost parameter tuning
• learning rate
• max_depth
• min_child_weight
• Testing the final model
• summary
• explore more
• homework

7. Midterm Project

8. Neural Networks and Deep Learning

• Clothes classification project
• TensorFlow and Keras
• loading the images
• etc
• Using a pre-trained model
• CNNs: convolutional layers
• CNNs: dense layers
• Transfer learning
• Creating the clothes classification model
• Keras functional components
• optimizer
• training the model
• Learning Rate
• Model checkpointing
• Adding more layers
• Dropout
• Data augmentation
• Training a larger clothes classification model
• Using the model with Keras
• summary
• explore more
• homework

9. Serverless Deep Learning

• intro
• serverless and AWS Lambda
• tensorflow-lite
• converting the model to TF-lite
• preparing images
• using the model in TF-lite
• putting everything together in a Lambda function
• preparing the docker image
  • testing the image locally
• pusting the image to ECR
• creating the lambda function
• creating the API gateway
• summary
• explore more
• homework

10. Kubernetes and TensorFlow-Serving

• intro, serving architecture overview
• saved_model format
• tensorflow-serving
  • running TF-serving locally
• communicating with tf-serving from Jupyter
• creating the gateway service
• introduction to Kubernetes
• creating a cluster on AWS (article)
• preparing the images
  • the TF-serving image
  • the gateway image
• deploying to Kubernetes
  • deployment for tf-serving
  • service for tf-serving
• creating the gateway on Kubernetes
  • deploymnet
  • servince - load balancer
• testing it
• deleting the cluster
• summary
• explore more
• homework

11. Kubeflow and KFServing

• intro
• installing Kubeflow on AWS
• preparing the model: uploading to S3
• deploying TF models with KF-serving
• accessing the model
• tranformers
• testing it
• deleting the cluster
• summary
• explore more
• homework - no homework

12. Capstone Project

13. Article