AIPrimer.AI
  • 🚦AI Primer In Transportation
  • CHAPTER 1 - INTRODUCTION TO MACHINE LEARNING
    • Machine Learning in Transportation
    • What is Machine Learning?
    • Types of Machine Learning
      • Supervised Learning
      • Unsupervised Learning
      • Semi-supervised Learning
      • Reinforced Learning
    • Fundamental concepts of machine learning
      • Model Training and Testing
      • Evaluating the Model’s Prediction Accuracy
      • The Underfitting and Overfitting Problems
      • Bias-Variance Tradeoff in Overfitting
      • Model Validation Techniques
      • Hyperparameter Tuning
      • Model Regularization
      • The Curse of Ddimensionality
    • Machine Learning versus Statistics
  • CHAPTER 2 - SUPERVISED METHODS
    • Supervised Learning_Complete Draft
    • K-Nearest Neighbor (KNN) Algorithm
    • Tree-Based Methods
    • Boosting
    • Support Vector Machines (SVMs)
  • CHAPTER 3 - UNSUPERVISED LEARNING
    • Principal Component Analysis
      • How Does It Work?
      • Interpretation of PCA result
      • Applications in Transportation
    • CLUSTERING
      • K-MEANS
      • SPECTRAL CLUSTERING
      • Hierarchical Clustering
    • REFERENCE
  • CHAPTER 4 - NEURAL NETWORK
    • The Basic Paradigm: Multilayer Perceptron
    • Regression and Classification Problems with Neural Networks
    • Advanced Topologies
      • Modular Network
      • Coactive Neuro–Fuzzy Inference System
      • Recurrent Neural Networks
      • Jordan-Elman Network
      • Time-Lagged Feed-Forward Network
      • Deep Neural Networks
  • CHAPTER 5 - DEEP LEARNING
    • Convolutional Neural Networks
      • Introduction
      • Convolution Operation
      • Typical Layer Structure
      • Parameters and Hyperparameters
      • Summary of Key Features
      • Training of CNN
      • Transfer Learning
    • Recurrent Neural Networks
      • Introduction
      • Long Short-Term Memory Neural Network
      • Application in transportation
    • Recent Development
      • AlexNet, ZFNet, VggNet, and GoogLeNet
      • ResNet
      • U-Net: Full Convolutional Network
      • R-CNN, Fast R-CNN, and Faster R-CNN
      • Mask R-CNN
      • SSD and YOLO
      • RetinaNet
      • MobileNets
      • Deformable Convolution Networks
      • CenterNet
      • Exemplar Applications in Transportation
    • Reference
  • CHAPTER 6 - REINFORCEMENT LEARNING
    • Introduction
    • Reinforcement Learning Algorithms
    • Model-free v.s. Model-based Reinforcement Learning
    • Applications of Reinforcement Learning to Transportation and Traffic Engineering
    • REFERENCE
  • CHAPTER 7 - IMPLEMENTING ML AND COMPUTATIONAL REQUIREMENTS
    • Data Pipeline for Machine Learning
      • Introduction
      • Problem Definition
      • Data Ingestion
      • Data Preparation
      • Data Segregation
      • Model Training
      • Model Deployment
      • Performance Monitoring
    • Implementation Tools: The Machine Learning Ecosystem
      • Machine Learning Framework
      • Data Ingestion tools
      • Databases
      • Programming Languages
      • Visualization Tools
    • Cloud Computing
      • Types and Services
    • High-Performance Computing
      • Deployment on-premise vs on-cloud
      • Case Study: Data-driven approach for the implementation of Variable Speed Limit
      • Conclusion
  • CHAPTER 8 - RESOURCES
    • Mathematics and Statistics
    • Programming, languages, and software
    • Machine learning environments
    • Tools of the Trade
    • Online Learning Sites
    • Key Math Concepts
  • REFERENCES
  • IMPROVEMENT BACKLOG
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  1. CHAPTER 7 - IMPLEMENTING ML AND COMPUTATIONAL REQUIREMENTS
  2. Implementation Tools: The Machine Learning Ecosystem

Programming Languages

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Last updated 1 year ago

Programming languages are the platforms which provide suitable functions and operation to write codes and execute for machine learning. Machine learning algorithms can be written in any language. However, some languages offer developed packages consisting of easy functionalities to handle data and execute machine learning models. Some of the popular programming languages and their usability are discussed below.

  1. Python: It is relatively easy to use, code and debugs. It offers good readability and structure. There are several packages and frameworks available for handling different formats of data and perform a wide range of operations. There are widespread community and corporate support available. It is also portable and extensible.

  2. R: It can be used to perform statistics, visualizations and data analysis. Libraries written in it provide numerous graphical and statistical techniques like classical statistical tests, linear and nonlinear modeling , classification, clustering, and machine learning packages. The language allows for creating high-quality plots, including formulae and mathematical symbols.

  3. Java: This is one of the most widely used languages for programming in both public and private sector organizations. Many of the open-source big data integration tools such as Apache Hadoop or Apache Spark are written in Java. It offers easy maintenance and good readability. It provides several Deep learning libraries, Machine learning model servers and a wide range of machine learning algorithms which has increased its popularity in the machine learning community.

  4. C++: This is one of the oldest programming languages with syntax easy to read for computers and hard for humans. It provides better performance by creating compact codes with faster running time. Most of the machine learning platforms support C++.

  5. There are several other programming languages such as Julia, Scala, JavaScript, Matlab, GO, etc available which can be used writing and execution of machine learning models.

Table 5-2 below lists useful packages and libraries across different programming languages for machine learning.

Language

Package or Library

Python

Scikit, TensorFlow, Keras, PyTorch, Theano, NumPy, Pandas

R

rPart, randomForest, Nnet, kernLab, e1071

C++

MLPack

Java

Weka, MOA, DeepLearning.4j, MALLET

Scala

SMILE, Apache Spark MLLib, NLP

Julia

Flux, Knet, MLBase.jl, TensorFlow.jl, ScikitLearn.jl

Matlab

Statistics and Machine Learning Toolbox

time-series analysis