Time to flex your machine learning muscles! Take on the carefully designed challenges of the Machine Learning Bookcamp and master essential ML techniques …
Time to flex your machine learning muscles! Take on the carefully designed challenges of the Machine Learning Bookcamp and master essential ML techniques through practical application.
In Machine Learning Bookcamp you will:The only way to learn is to practice! In Machine Learning Bookcamp, you’ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects. Taking you from the basics of machine learning to complex applications such as image analysis, each new project builds on what you’ve learned in previous chapters. You’ll build a portfolio of business-relevant machine learning projects that hiring managers will be excited to see.
Master key machine learning concepts as you build actual projects! Machine learning is what you need for analyzing customer behavior, predicting price trends, evaluating risk, and much more. To master ML, you need great examples, clear explanations, and lots of practice. This book delivers all three!
Machine Learning Bookcamp presents realistic, practical machine learning scenarios, along with crystal-clear coverage of key concepts. In it, you’ll complete engaging projects, such as creating a car price predictor using linear regression and deploying a churn prediction service. You’ll go beyond the algorithms and explore important techniques like deploying ML applications on serverless systems and serving models with Kubernetes and Kubeflow. Dig in, get your hands dirty, and have fun building your ML skills!
Python programming skills assumed. No previous machine learning knowledge is required.
Alexey Grigorev is a principal data scientist at OLX Group. He runs DataTalks.Club, a community of people who love data.
Great theory, real-life examples, and just the right amount of code. An absolute feast for a beginner who wants to enter the realm of machine learning.Chapter 1 Introduction to machine learning
Chapter 1 When machine learning isn’t helpful
Chapter 1 Evaluation
Chapter 2 Machine learning for regression
Chapter 2 Exploratory data analysis
Chapter 2 Target variable analysis
Chapter 2 Machine learning for regression - again
Chapter 2 Linear regression
Chapter 2 Predicting the price
Chapter 2 Validating the model
Chapter 2 Regularization
Chapter 2 Using the model
Chapter 3 Machine learning for classification
Chapter 3 Initial data preparation
Chapter 3 Feature importance, Part 1
Chapter 3 Feature importance, Part 2
Chapter 3 Feature engineering
Chapter 3 Machine learning for classification
Chapter 3 Training logistic regression
Chapter 3 Model interpretation
Chapter 3 Using the model
Chapter 4 Evaluation metrics for classification
Chapter 4 Confusion table
Chapter 4 Precision and recall
Chapter 4 ROC curve and AUC score
Chapter 4 ROC Curve
Chapter 4 Parameter tuning
Chapter 4 Next steps
Chapter 5 Deploying machine learning models
Chapter 5 Model serving
Chapter 5 Managing dependencies
Chapter 5 Docker
Chapter 5 Deployment
Chapter 6 Decision trees and ensemble learning
Chapter 6 Data cleaning
Chapter 6 Decision trees
Chapter 6 Decision tree learning algorithm
Chapter 6 Random forest
Chapter 6 Gradient boosting
Chapter 6 Parameter tuning for XGBoost
Chapter 6 Next steps
Chapter 7 Neural networks and deep learning
Chapter 7 Convolutional neural networks
Chapter 7 Internals of the model
Chapter 7 Training the model
Chapter 7 Training the model - again
Chapter 7 Saving the model and checkpointing
Chapter 7 Data augmentation
Chapter 7 Using the model
Chapter 8 Serverless deep learning
Chapter 8 Preparing the Docker image
Chapter 9 Serving models with Kubernetes and Kubeflow
Chapter 9 Running TensorFlow Serving locally
Chapter 9 Model deployment with Kubernetes
Chapter 9 Deploying to Kubernetes
Chapter 9 Model deployment with Kubeflow
Chapter 9 KFServing transformers