Machine Learning Bookcamp: Build a Portfolio of Real-Life Projects

Machine Learning Bookcamp MEAP V10
Copyright
Welcome letter
Brief contents
Chapter 1: Introduction to machine learning
	1.1 Machine learning
		1.1.1 Machine learning vs. rule-based systems
		1.1.2 When machine learning isn’t helpful
		1.1.3 Supervised machine learning
	1.2 Machine learning process
		1.2.1 Business understanding step
		1.2.2 Data understanding step
		1.2.3 Data preparation step
		1.2.4 Modeling step
		1.2.5 Evaluation step
		1.2.6 Deployment step
		1.2.7 Iterate
	1.3 Modeling and model validation
	1.4 Summary
Chapter 2: Machine learning for regression
	2.1 Car-price prediction project
		2.1.1 Downloading the dataset
	2.2 Exploratory data analysis
		2.2.1 Exploratory data analysis toolbox
		2.2.2 Reading and preparing data
		2.2.3 Target variable analysis
		2.2.4 Checking for missing values
		2.2.5 Validation framework
	2.3 Machine learning for regression
		2.3.1 Linear regression
		2.3.2 Training linear regression model
	2.4 Predicting the price
		2.4.1 Baseline solution
		2.4.2 RMSE: evaluating model quality
		2.4.3 Validating the model
		2.4.4 Simple feature engineering
		2.4.5 Handling categorical variables
		2.4.6 Regularization
		2.4.7 (Multiple answers are possible)Using the model
	2.5 Next steps
		2.5.1 Exercises
		2.5.2 Other projects
	2.6 Summary
	2.7 Answers to exercises
Chapter 3: Machine learning for classification
	3.1 Churn prediction project
		3.1.1 Telco churn dataset
		3.1.2 Initial data preparation
		3.1.3 Exploratory data analysis
		3.1.4 Feature importance
	3.2 Feature engineering
		3.2.1 One-hot encoding for categorical variables
	3.3 Machine learning for classification
		3.3.1 Logistic regression
		3.3.2 Training logistic regression
		3.3.3 Model interpretation
		3.3.4 Using the model
	3.4 Next steps
		3.4.1 Exercises
		3.4.2 Other projects
	3.5 Summary
	3.6 Answers to exercises
Chapter 4: Evaluation metrics for classification
	4.1 Evaluation metrics
		4.1.1 Classification accuracy
		4.1.2 Dummy baseline
	4.2 Confusion table
		4.2.1 Introduction to confusion table
		4.2.2 Calculating the confusion table with NumPy
		4.2.3 Precision and recall
	4.3 ROC curve and AUC score
		4.3.1 True positive rate and false positive rate
		4.3.2 Evaluating a model at multiple thresholds
		4.3.3 Random baseline model
		4.3.4 The ideal model
		4.3.5 ROC Curve
		4.3.6 Area under the ROC curve (AUC)
	4.4 Parameter tuning
		4.4.1 K-fold cross-validation
		4.4.2 Finding best parameters
	4.5 Next steps
		4.5.1 Exercises
		4.5.2 Other projects
	4.6 Summary
	4.7 Answers to exercises
Chapter 5: Deploying machine learning models
	5.1 Churn prediction model
		5.1.1 Using the model
		5.1.2 Using Pickle to save and load the model
	5.2 Model serving
		5.2.1 Web services
		5.2.2 Flask
		5.2.3 Serving churn model with Flask
	5.3 Managing dependencies
		5.3.1 Pipenv
		5.3.2 Docker
	5.4 Deployment
		5.4.1 AWS Elastic Beanstalk
	5.5 Next steps
		5.5.1 Exercises
		5.5.2 Other projects
	5.6 Summary
Chapter 6: Decision trees and ensemble learning
	6.1 Credit risk scoring project
		6.1.1 Credit scoring dataset
		6.1.2 Data cleaning
		6.1.3 Dataset preparation
	6.2 Decision trees
		6.2.1 Decision tree classifier
		6.2.2 Decision tree learning algorithm
		6.2.3 Parameter tuning for decision tree
	6.3 Random forest
		6.3.1 Training a random forest
		6.3.2 Parameter tuning for random forest
	6.4 Gradient boosting
		6.4.1 XGBoost: extreme gradient boosting
		6.4.2 Model performance monitoring
		6.4.3 Parameter tuning for XGBoost
		6.4.4 Testing the final model
	6.5 Next steps
		6.5.1 Exercises
		6.5.2 Other projects
	6.6 Summary
Chapter 7: Neural networks and deep learning
	7.1 Fashion classification
		7.1.1 GPU vs CPU
		7.1.2 Downloading the clothing dataset
		7.1.3 TensorFlow and Keras
		7.1.4 Loading images
	7.2 Convolutional neural networks
		7.2.1 Using a pre-trained model
		7.2.2 Getting predictions
	7.3 Internals of the model
		7.3.1 Convolutional layers
		7.3.2 Dense layers
	7.4 Training the model
		7.4.1 Transfer learning
		7.4.2 Loading the data
		7.4.3 Creating the model
		7.4.4 Training the model
		7.4.5 Adjusting the learning rate
		7.4.6 Saving the model and checkpointing
		7.4.7 Adding more layers
		7.4.8 Regularization and dropout
		7.4.9 Data augmentation
		7.4.10 Training a larger model
	7.5 Using the model
		7.5.1 Loading the model
		7.5.2 Evaluating the model
		7.5.3 Getting the predictions
	7.6 Next steps
	7.7 Summary
Chapter 8: Serverless deep learning
	8.1 Serverless: AWS Lambda
		8.1.1 TensorFlow-Lite
		8.1.2 Converting the model to TF-Lite format
		8.1.3 Preparing the images
		8.1.4 Using the TensorFlow-Lite model
		8.1.5 Code for the Lambda function
		8.1.6 Preparing the Docker image
		8.1.7 Pushing the image to AWS ECR
		8.1.8 Creating the Lambda function
		8.1.9 Creating the API Gateway
	8.2 Next steps
		8.2.1 Exercises
		8.2.2 Other projects
	8.3 Summary
Chapter 9: Serving models with Kubernetes and Kubeflow
	9.1 Kubernetes and Kubeflow
	9.2 Serving models with TensorFlow-Serving
		9.2.1 Overview of the serving architecture
		9.2.2 The saved_model format
		9.2.3 Running TensorFlow-Serving locally
		9.2.4 Invoking the TF-Serving model from Jupyter
		9.2.5 Creating the Gateway service
	9.3 Model deployment with Kubernetes
		9.3.1 Introduction to Kubernetes
		9.3.2 Creating a Kubernetes cluster on AWS
		9.3.3 Preparing the Docker images
		9.3.4 Deploying to Kubernetes
		9.3.5 Testing it
	9.4 Model deployment with Kubeflow
		9.4.1 9.4.1 Preparing the model: uploading it to S3
		9.4.2 Deploying TensorFlow models with KFServing
		9.4.3 9.4.3 Accessing the model
		9.4.4 KFServing Transformers
		9.4.5 Testing the transformer
		9.4.6 Deleting the EKS cluster
	9.5 Next steps
		9.5.1 Exercises
		9.5.2 Other projects
	9.6 Summary
Appendix A: Preparing the environment
	A.1 Installing Python and Anaconda
		A.1.1 Installing Python and Anaconda on Linux
		A.1.2 Installing Python and Anaconda on Windows
		A.1.3 Installing Python and Anaconda on macOS
	A.2 Running Jupyter
		A.2.1 Running Jupyter on Linux
		A.2.2 Running Jupyter on Windows
		A.2.3 Running Jupyter on MacOS
	A.3 Installing the Kaggle CLI
	A.4 Accessing the source code
	A.5 Installing Docker
		A.5.1 Installing Docker on Linux
		A.5.2 Installing Docker on Windows
		A.5.3 Installing Docker on MacOS
	A.6 Renting a server on AWS
		A.6.1 Registering on AWS
		A.6.2 Accessing billing information
		A.6.3 Creating an EC2 instance
		A.6.4 Connecting to the instance
		A.6.5 Shutting down the instance
		A.6.6 Configuring AWS CLI
	A.7 Summary
Appendix B: Introduction to Python
	B.1 Variables
		B.1.1 Control-flow
		B.1.2 Collections
		B.1.3 Code reusability
		B.1.4 Installing libraries
		B.1.5 Python programs
	B.1.6 Summary
Appendix C: Introduction to NumPy
	C.1.1 NumPy
	C.1.2 NumPy operations
	C.1.3 Linear algebra
	C.1.4 Vector-vector multiplication
	C.1.5 Matrix-matrix multiplication
	C.1.6 Summary
Appendix D: Introduction to Pandas
	D.1 Pandas
		D.1.1 DataFrame
		D.1.2 Series
		D.1.3 Index
		D.1.4 Accessing rows
		D.1.5 Splitting DataFrame
	D.2 Operations
		D.2.1 Element-wise operations
		D.2.2 Filtering
		D.2.3 String operations
		D.2.4 Summarizing operations
		D.2.5 Missing values
		D.2.6 Sorting
		D.2.7 Grouping
	D.3 Summary
Appendix E: AWS SageMaker
	E.1 AWS SageMaker Notebooks
		E.1.1 Increasing the GPU quota limits
		E.1.2 Creating a notebook instance
		E.1.3 Training a model
		E.1.4 Turning off the notebook
	E.2 Summary