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10 / 120·markdown

01 intro

011. Introduction to Machine Learning
021.1 Introduction to Machine Learning
031.2 ML vs Rule-Based Systems
041.3 Supervised Machine Learning
051.4 CRISP-DM
061.5 Model Selection Process
07Setting up the Environment
081.7 Introduction to NumPy
091.8 Linear Algebra Refresher
101.9 Introduction to Pandas
111.10 Summary

02 regression

122. Machine Learning for Regression
132.1 Car price prediction project
142.2 Data preparation
152.3 Exploratory data analysis
162.4 Setting up the validation framework
172.5 Linear regression
182.6 Linear regression: vector form
192.7 Training linear regression: Normal equation
202.8 Baseline model for car price prediction project
212.9 Root Mean Squared Error (RMSE)
222.10 Computing RMSE on validation data
232.11 Feature engineering
242.12 Categorical variables
252.13 Regularization
262.14 Tuning the model
272.15 Using the model
282.16 Car price prediction project summary
292.17 Explore more
302. Machine Learning for Regression

03 classification

313. Machine Learning for Classification
323.1 Churn prediction project
333.2 Data preparation
343.3 Setting up the validation framework
353.4 EDA
363.5 Feature importance: Churn rate and risk ratio
373.6 Feature importance: Mutual information
383.7 Feature importance: Correlation
393.8 One-hot encoding
403.9 Logistic regression
413.10 Training logistic regression with Scikit-Learn
423.11 Model interpretation
433.12 Using the model
443.13 Summary
453.14 Explore more
46Data preparation
473. Machine Learning for Classification

04 evaluation

484. Evaluation Metrics for Classification
494.1 Evaluation metrics: session overview
504.2 Accuracy and dummy model
514.3 Confusion table
524.4 Precision and Recall
534.5 ROC Curves
544.6 ROC AUC
554.7 Cross-Validation
564.8 Summary
574.9 Explore more
584. Evaluation Metrics for Classification

05 deployment

595. Deploying Machine Learning Models
605.1 Intro / Session overview
615.2 Saving and loading the model
625.3 Web services: introduction to Flask
635.4 Serving the churn model with Flask
645.5 Python virtual environment: Pipenv
655.6 Environment management: Docker
665.7 Deployment to the cloud: AWS Elastic Beanstalk (optional)
675.8 Summary
685.9 Explore more
69starter
70workshop uv fastapi
7105 train churn model

06 trees

726. Decision Trees and Ensemble Learning
736.1 Credit risk scoring project
746.2 Data cleaning and preparation
756.3 Decision trees
766.4 Decision tree learning algorithm
776.5 Decision trees parameter tuning
786.6 Ensemble learning and random forest
796.7 Gradient boosting and XGBoost
806.8 XGBoost parameter tuning
816.9 Selecting the best model
826.10 Summary
836.11 Explore more
846. Decision Trees and Ensemble Learning

08 deep learning

858. Neural Networks and Deep Learning
868.1 Fashion classification
878.2 TensorFlow and Keras
888.3 Pre-trained convolutional neural networks
898.4 Convolutional neural networks
908.5 Transfer learning
918.6 Adjusting the learning rate
928.7 Checkpointing
938.8 Adding more layers
948.9 Regularization and dropout
958.10 Data augmentation
968.11 Training a larger model
978.12 Using the model
988.13 Summary
998.14 Explore more
1008. Neural networks and deep learning

09 serverless

1019. Serverless Deep Learning
1029.1 Introduction to Serverless
1039.2 AWS Lambda
1049.3 TensorFlow Lite
1059.4 Preparing the code for Lambda
1069.5 Preparing a Docker image
1079.6 Creating the lambda function
1089.7 API Gateway: exposing the lambda function
1099.8 Summary
1109.9 Explore more
111Convert Keras to TF-Lite

10 kubernetes

11210. Kubernetes and TensorFlow Serving
11310.1 Overview
11410.2 TensorFlow Serving
11510.3 Creating a pre-processing service
11610.4 Running everything locally with Docker-compose
11710.5 Introduction to Kubernetes
11810.6 Deploying a simple service to Kubernetes
11910.7 Deploying TensorFlow models to Kubernetes
12010.8 Deploying to EKS

Chapter 10

1.9 Introduction to Pandas

1.9 Introduction to Pandas

Notes

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Notes from Peter Ernicke - Part 1/2
Notes from Peter Ernicke - Part 2/2

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Notebook

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Pandas Cheat sheet

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