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Practical Machine Learning with Rust Free PDF Download

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This book contains an extensive area of machine learning tricks and techniques; uncovering hidden ideas and tips for different types of data with the help of practical and real-world. A Deep Dive into Building Machine Learning and Deep Learning models. Machine learning gives practical tools to analyze data and to make predictions but also powers the latest updates in artificial intelligence.

This PDF will provide you all about Practical Machine Learning for free that is following:

Basics of Rust:

  •  Why Rust?
  • A Better Reference
  • Rust Installation
  • Package Manager and Cargo
  • Creating New Applications in Rust
  • Variables in Rust
  • Mutation and Shadowing
  • Variable Scoping
  • Data Types
  • Functions
  • Conditions
  • If Conditions
  • Pattern Matching
  • References and Borrowing
  • Mutable References
  • Object-Oriented Programming
  • Structures
  • Traits
  • Methods
  • Enumerations
  • Writing Tests
  • Summary
  • References

Supervised Learning:

  • What Is Machine Learning?
  • Datasets Specific Code
  • Rusty_Machine Library
  • Linear Regression
  • Gaussian Process
  • Generalized Linear Models
  • Evaluation of Regression Models
  • MAE and MSE
  • R-Squared Error
  • Classification Algorithms
  • Iris Dataset
  • Logistic Regression
  • Decision Trees
  • Random Forest
  • XGBoost
  • Support Vector Machines
  • K Nearest Neighbors
  • Neural Networks
  • Model Evaluation
  • Conclusion
  • Bibliography

Unsupervised and Reinforcement Learning:

  • K-Means Clustering
  • Gaussian Mixture Model
  • Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
  • Principal Component Analysis
  • Testing an Unsupervised Model
  • Reinforcement Learning
  • Conclusion
  • Bibliography

Working with Data:

  • JSON
  • XML
  • Scraping
  • SQL
  • NoSQL
  • Data on s3
  • Data Transformations
  • Working with Matrices
  • Conclusion

Natural Language Processing:

  • Sentence Classification
  • Named Entity Recognition
  • Chatbots and Natural Language Understanding (NLU)
  • Building an Inference Engine
  • Conclusion

Computer Vision:

  • Image Classification
  • Convolutional Neural Networks (CNN)
  • Rust and Torch
  • Torch Dataset
  • CNN Model
  • Model Building and Debugging
  • Pretrained Models
  • Transfer Learning
  • Training
  • Neural Style Transfer
  • Tensorflow and Face Detection
  • Conclusion
  • Bibliography

Machine Learning Domains:

  • Statistical Analysis
  • Writing High Performance Code
  • Recommender Systems
  • Command Line
  • Downloading Data
  • Data
  • Model Building
  • Model Prediction
  • Conclusion
  • Bibliography

Using Rust Applications:

  • Rust Plug-n-Play
  • Python
  • Java
  • Rust in the Cloud
  • Conclusion
  • Bibliography





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