Introduction to the Machine Learning Stack

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Last updated on
17th May, 2021
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05th May, 2021
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Introduction to the Machine Learning Stack

What is Machine Learning

Arthur Samuel coined the term Machine Learning or ML in 1959. Machine learning is the branch of Artificial Intelligence that allows computers to think and make decisions without explicit instructions.  At a high level, ML is the process of teaching a system to learn, think, and take actions like humans.  

Machine Learning helps develop a system that analyses the data with minimal intervention of humans or external sources.  ML uses algorithms to analyse and filter search inputs and correspondingly displays the desirable outputs. 

Machine Learning implementation can be classified into three parts: 

  • Supervised Learning 
  • Unsupervised Learning 
  • Reinforcement Learning 

What is Stacking in Machine Learning? 

Stacking in generalised form can be represented as an aggregation of the Machine Learning Algorithm. Stacking Machine Learning provides you with the advantage of combining the meta-learning algorithm with training your dataset, combining them to predict multiple Machine Learning algorithms and machine learning models. 

Stacking helps you harness the capabilities of a number of well-established models that perform regression and classification tasking.

When it comes to stacking, it is classified into 4 different parts: 

  • Generalisation 
  • Scikit- Learn API 
  • Classification of Stacking 
  • Regression of Stacking 

A generalisation of Stacking: Generalisation is a composition of numerous Machine Learning models performed on a similar dataset, somewhat similar to Bagging and Boosting. 

  • Bagging: Used mainly to provide stability and accuracy, it reduces variance and avoids overfitting. 
  • Boosting: Used mainly to convert a weak learning algorithm to a strong learning algorithm and reduce bias and variance. 
  • Scikit-Learn API: This is among the most popular libraries and contains tools for machine learning and statistical modeling.

Introduction to the Machine Learning Stack

The basic technique of Stacking in Machine Learning; 

  • Divide the training data into 2 disjoint sets. 
  • The level to which you train data depends on the base learner. 
  • Test base learner and make a prediction. 
  • Collect correct responses from the output. 

Machine Learning Stack

Dive deeper into the Machine Learning engineering stack to have a proper understanding of how it is used and where it is used. Find out the below list of resources: 

  1. CometML: Comet.ML is the machine learning platform dedicated to data scientists and researchers to help them seamlessly track the performance, modify code, and manage history, models, and databases.   
  2. It is somewhat similar to GitHub, which allows training models, tracks code changes, and graphs the dataset. Comet.ml can be easily integrated with other machine learning libraries to maintain the workflow and develop insights for your data. Comet.ml can work with GitHub and other git services, and a developer can merge the pull request easily with your GitHub repository. You can get help from the comet.ml official website regarding the documentation, download, installing, and cheat sheet. 
  3. GitHub: GitHub is an internet hosting and version control system for software developers. Using Git business and open-source communities, both can host and manage their project, review their code and deploy their software. There are more than 31 million who actively deploy their software and projects on GitHub. The GitHub platform was created in 2007, and in 2020 GitHub made all the core features free to use for everyone. You can add your private repository and perform unlimited collaborations. You can get help from the GitHub official website, or you can learn the basics of GitHub from many websites like FreeCodeCamp or the GitHub documentation. 
  4. Hadoop: Hadoop provides you with a facility to store data and run an application on a commodity hardware cluster. Hadoop is powered by Apache that can be described as a software library or a framework that enables you to process data or large datasets. Hadoop environment can be scaled from one to a thousand commodities providing computing power and local storage capacity. 

The benefit of the Hadoop System

  • High computing power. 
  • High fault tolerance. 
  • More flexibility 
  • Low delivery cost 
  • Easily grown system (More scalability). 
  • More storage. 

Challenges faced in using Hadoop System

  • Most of the problems require a unique solution. 
  • Processing speed is very slow. 
  • Need for high data security and safety. 
  • High data management and governance requirements.  

Where Hadoop is used

  • Data lake. 
  • Data Warehouse 
  • Low-cost storage and management 
  • Building the IoT system 

Hadoop framework can be classified into

  • Hadoop yarn 
  • Hadoop Distributed File System 
  • Hadoop MapReduce 
  • Hadoop common 
  1. Keras: Keras is an open-source library, which provides you with the open interface for Artificial Intelligence and Artificial Neural Network using Python. It helps in designing API for human convenience and follows best practices to reduce cost and move toward cognitive load maintenance. 

It acts as an interface between the TensorFlow library and dataset. Keras was released in 2015. It has a vast ecosystem which you can deploy anywhere. There are many facilities provided by Keras which you can easily access with your requirements. 

CERN uses Keras, NASA, NIH, LHC, and other scientific organisations to implement their research ideas, offer the best services to their client, and develop a high-quality environment with maximum speed and convenience. 

Keras has always focused on user experience offering a simple APIs environment. Keras has abundant documentation and developer guides which are also open-source, which anyone in need can refer to. 

  1. Luigi: This is a Python module that supports building batch jobs with the background of complex pipelining. Luigi is internally used by Spotify, and helps to run thousands of tasks daily, that are organised in the form of the complex dependency graph. Luigi uses the Hadoop task as a prelim job for the system. Luigi being open-source has no restrictions on its usage by users. 

The concept of Luigi is based on a unique contribution where there are thousands of open-source contributions or enterprises. 

Companies using Luigi

  • Spotify. 
  • Weebly 
  • Deloitte 
  • Okko 
  • Movio 
  • Hopper 
  • Mekar 
  • M3 
  • Assist Digital 

Luigi supports cascading Hive and Pig tools to manage the low level of data processing and bind them together in the big chain together. It takes care of workflow management and task dependency.

  1. Pandas: If you want to become a Data Scientist, then you must be aware of Pandas--a favourite tool with Data Scientists, and the backbone of many high-profile big data projects. Pandas are needed to clean, analyse, and transform the data according to the project's need. 

Pandas is a fast and open-source environment for data analysis and managing tools. Pandas is created at the top of the Python language. The latest version of Pandas is Pandas 1.2.3. 

When you are working with Pandas in your project, you must be aware of these scenarios

  • Want to open the local file? It uses CSV, Excel, or delimited file. 
  • Want to open a remote store databaseConvert list, dictionary, or NumPy using Pandas. 

Pandas provide an open-source environment and documentation where you can raise your concern, and they will identify the solution to your problem. 

  1. PyTorch: PyTorch is developed in Python, which is the successor of the python torch library. PyTorch is also an open-source Machine learning Library; the main use of PyTorch is found in computer vision, NLP, and ML-related fields. It is released under the BSD license. 

Facebook and Convolutional Architecture operate PyTorch for Fast Feature Embedding (CAFFE2). Other major players are working with it like Twitter, Salesforce, and oxford. 

PyTorch has emerged as a replacement for NumPy, as it is faster than NumPy in performing the mathematical operations, array operations and provides the most suitable platform. 

PyTorch provides a more pythonic framework in comparison to TensorFlow. PyTorch follows a straightforward procedure and provides a pre-prepared model to perform a user-defined function. There is a lot of documentation you can refer to at their official site. 

Modules of PyTorch

  • Autograd Module 
  • Optim module 
  • In module 

Key Features

  • Make your project production-ready. 
  • Optimised performance. 
  • Robust Ecosystem. 
  • Cloud support. 
  1. Spark: Spark or Apache Spark is a project from Apache. It is an open-source, distributed, and general-purpose processing engine. It provides large-scale data processing for big data or large datasets. Spark provides you support for many backgrounds like Java, Python, R, or SQL, and many other technologies. 

The benefits of Spark include

  • High Speed. 
  • High performance. 
  • Easy to use UI. 
  • Large and complex libraries. 

Leverage data to a variety of sources

  • Amazon S3. 
  • Cassandra. 
  • Hadoop Distributed File System. 
  • OpenStack. 

APIs Spark contains

  • Java 
  • Python 
  • Scala 
  • Spark SQL 
  • R 
  1. Scikit- learn: Scikit-Learn also known as sklearn, is a free and open-source software Machine Learning Library for Python. Scikit-Learn is the result of a Google summer Code project by David Cournapeau. Scikit-Learn makes use of NumPy for an operation like array operation, algebra, and high performance. 

The latest version of Scikit-Learn was deployed in Jan 2021, Version of Scikit-Learn 0.24. 

The benefits of Scikit-Learn include

  • It provides simple and efficient tools. 
  • Easily assignable and reusable tool. 
  • Built on the top of NumPy, scipy, and matplotlib. 

Scikit-Learn is used in

  • Dimensionality reduction. 
  • Clustering 
  • Regression 
  • Classification 
  • Pre-processing 
  • Model selection and extraction. 
  1. TensorFlow: TensorFlow is an open-source end-to-end software library used for numerical computation. It does graph-based computations quickly and efficiently leveraging the GPU (Graphics Processing Unit), making it seamless to distribute the work across multiple GPUs and computers. TensorFlow can be used across a range of projects with a particular concentration on the training dataset and Neural network. 

The benefits of TensorFlow

  • Robust ML model. 
  • Easy model building. 
  • Provide powerful experiments for research and development. 
  • Provide an easy mathematical model. 

Why Stacking

Stacking provides many benefits over other technologies. 

  • It is simple. 
  • More scalable. 
  • More flexible. 
  • More Space 
  • Less cost 
  • Most machine learning stacks are open source. 
  • Provides virtual chassis capability. 
  • Aggregation switching. 

How does stacking work? 

If you are working in Python, you must be aware of the K-folds clustering or k-mean clustering, and we perform stacking using the k fold method. 

  • Divide the dataset into k-folds very similar to the k-cross-validation method. 
  • If the model fits in k-1 parts, then the prediction is made for the kth part. 
  • Perform the same function for each part of the training data. 
  • The base model is fitted into the dataset, and then complete performance is calculated. 
  • Prediction from the training set used for the second level prediction. 
  • The next level makes predictions for the test dataset. 

Blending is a subtype of stacking. 

Installation of libraries on the system

Installing libraries in Python is an easy task; you just require some pre-requisites. 

  • Ensure you can run your Python command using the Command-line interface. 
    • Use - python –version on your command line to check if Python is installed in your system. 
  • Try to run the pip command in your command-line interface. 
    • Python -m pip - - version 
  • Check for your pip, setup tools, and wheels recent update. 
    • Python -m pip install - - upgrade pip setuptools wheel 
  • Create a virtual environment. 

Use pip for installing libraries and packages into your system. 

Conclusion

To understand the basics of data science, machine learning, data analytics, and artificial intelligence, you must be aware of machine learning stacking, which helps store and manage the data and large datasets. 

There is a list of open-source models and platforms where you can find the complete documentation about the machine learning stacking and required tools. This machine learning toolbox is robust and reliable. Stacking uses the meta-learning model to develop the data and store them in the required model. 

Stacking has the capabilities to harness and perform classification, regression, and prediction on the provided dataset. It helps to constitute regression and classification predictive modelling. The model has been classified into two models, level 0, known as the base model, and the other model-level 1, known as a meta-model.

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Abhresh Sugandhi

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Abhresh is specialized as a corporate trainer, He has a decade of experience in technical training blended with virtual webinars and instructor-led session created courses, tutorials, and articles for organizations. He is also the founder of Nikasio.com, which offers multiple services in technical training, project consulting, content development, etc.