Open Source Machine Learning

What is Machine Learning?

Machine Learning (ML) is the analytical processing of algorithms that use big data sets to learn, extrapolate and predict. One of the exciting prospects of machine learning is the predictability algorithm ameliorates as the dataset size increases. Every day we are using tools that are using ML algorithm to predict likewise smartphone keyboards uses keylogger and prediction algorithm for understanding the next word. Self-driving cars, Chatbots, Speech Recognition, eCommerce products and many more. Machine learning focusses on developing predictions via computers based on computational statistics and analytics.

Machine Learning (ML) tasks are categorised as:

  1. Supervised Learning
  2. Unsupervised Learning

Difference between Supervised Learning and Unsupervised Learning

  1. Supervised learning task works on Y = f(X) algorithm. Here the motive of this function to predict an accurate output. The process of this task focusses on how a teacher will supervise their students in learning process. Iterative predictions on training data make predictability more accurate for future. Example: Writing a word “G” in the URL of a browser predicts a list of keywords like – Google, Gmail, Grammarly etc. as your first prediction and clicking on Google provides a predictive score. Again, when you type G analyze the keyword with the highest predictive score and provides the results based on it.
  2. Unsupervised learning task requires only input data (X) with no corresponding output variable. The aim of unsupervised learning is to design a prediction framework based on the different No teacher involvement in correcting the predictions so the algorithms formulate and demonstrate the interesting analytical data based on predictions done by machine. Example: Big-data analytic tools, Google analytics etc.

R-programming Language – R is an Open Source free language package used for computational statistics and graphics language for all operating systems. This language is widely used among Big-data analyst and Data miners for evaluation and analysis of statistic data. Statistical functionalities like linear and non-linear stats, test-series analysis, clustering, classification, classical statistics and advanced graphical techniques are performed. R language is scalable for many statistical functions, dynamic in nature, offers advanced statistical analytical features for calculation, array functions, graphics display, data manipulations etc. More than 11000 packages are available serving different purposes like Comprehensive R Archive Network (CRAN) containing archive for a network of FTP, web-servers, codes, and documentation of R, Bioconductor– a tool for analysis of high-throughput genomic data, Omegahat- is a repository from R etc.

Open-Source Machine Learning Software

  1. Apache Singa: Design proposed a programming model on deep learning. Three components of a software
    a) Core – Memory management
    b) IO – writes data on disk and networks
    c) Model – data structures and algorithms
  2. Google Cloud Machine Learning Engine: This Open Source technology enables to work on any big data set based on ML framework. Integration with Cloud enables the user to access the Google drive. TensorFlow Model works on large-scale unsupervised training task and batch productivity of prediction models for scale-up the prediction efficiency.
  3. Amazon ML: AML is an Open-Source technology delivering ML services for developers. Amazon Machine learning enhance the functionality of Amazon Echo Dot powered by Alexa, AWS Cloud platform, Amazon Go etc.
  4. Unity ML: Unity Machine Learning focusses on transforming games and simulation. Developers and Researchers develop Artificial Intelligent bots for training by deep learning enforcement.
  5. Shogun: An Open-Source free toolbox scripted in C++, providing data structures and analytical algorithms for machine learning. The project can run on Linux, Windows, and Mathematical operations like regression, classification, clustering, statistical functions etc. are performed
  6. Oryx2: Lambda architecture built in the Oryx2 realization This is a real-time machine-learning tool that is designed for tracking the present analytics.
  7. Microsoft Distributed ML: Microsoft provides a model for training machines based on big-data and user analytics. This toolkit works on algorithm and system innovations for analyzing big data and scale-up the machine learning.

IT ecosystem is increasing exponentially with more velocity, variety, veracity and so it is becoming a primary responsibility to manage these data efficiently. Machine learning works with Big-Data for analysis and visualization of data in an effective manner.

Open-Source Big Data Analytics Tools And Platforms Are:

  1. Apache Hadoop
  2. Cassandra
  3. KNIME
  4. Solr
  5. Rapidminer
  6. Terracotta
  7. Oozie
  8. AVRO
  9. Grid Gain
  10. ZookeeperMachine learning is so pervasive today that you presumably utilize it many times each day without knowing it. A number of researchers think it is the ideal approach to gain ground towards human-level AI.

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