![]() Not all transactional data are relevant though!īiG data are not just big but very often problematic too - containing missing data, information pretending to be numbers and outliers.ĭata management is art of getting useful information from raw data generated within the business process or collected from external sources. Big data can be and are overwhelming consisting of data table with millions of rows and hundreds if not thousands of columns. Online stores as well as traditional bricks-and-mortar retail stores generate wide streams of data. For business, it usually refers to the information that is capture or collected by the computer systems installed to facilitate and monitor various transactions. The technical and business newspapers/journals are full of references to "BiG Data". Turing test is used to determine whether or not computer (or machines) can think (intelligently) like humans. This is to check the validity of Turing test developed by Alan Turing in 1950. There are other topics of discussion such as Chinese Room Argument to question whether a program can give a computer a 'mind, 'understanding' and / or 'consciousness'. His approach to machine learning was explained in a paper published in the IBM Journal of Research and Development in 1959". In fact, he coined the term machine learning. Arthur Lee Samuels, an IBM researcher, developed one of the earliest machine learning programs - a self-learning program for playing checkers. In other words: machine learning is the science of getting computers to act without being explicitly programmed every time a new information is received.Īn excerpt from Machine Learning For Dummies, IBM Limited Edition: "AI and machine learning algorithms aren't new. In contrast to explicit (and somewhat static) programming, machine learning uses many algorithms that iteratively learn from data to improve, interpret the data and finally predict outcomes. are emerging and dominant conversations today all based on one fundamental truth - follow the data. ✍ Machine learning, artificial intelligence, cognitive computing, deep learning. the linear algebra behind each algorithm or optimization operations! The best way is to find a data, a working example script and fiddle with them. Video editing and streaming using OBS Studio - Animations using BlenderĮach statement is commented so that you easily connect with the code and the function of each module - remember one does not need to understand everything at the foundational level - e.g.Linux Commands and Utilities and Utilities related to Files and Folders.GhostView to convert PDF into other formats such as PNG -Compress, merge, scale, rotate and delete pages from PDF files using PyPDF2.Video Editing - Image Filtering, Masking and Denoising - Image Deskewing - Video Editing using FFmpeg - Animations like PowerPoint - Morphological Operations - Connected Component Labeling: CCL - Find and Draw Contours in an Image.Digit Recognition and ANN MLP classifications - Computer Vision.Decision Tree / Random Forest Classification with Python + sciKit-Learn. ![]() SVM using Python + sciKit-Learn - Naive Bayes classification - Anomaly Detection - Recommender Systems and Collaborative Filtering.K-Nearest Neighbours (KNN) using Python + sciKit-Learn - clustering by K-means (GNU OCTAVE) - Probability in Machine Learning.Linear Algebra - Principal Component Analysis - PCA (GNU OCTAVE).Python - Python Argument Parsing - Vectorization - Slicing of Arrays - OOP in Python - Web Scraping in Python Regression and Logistic Regression (GNU OCTAVE). ![]() On this page, you will find working examples of most of the machine learning methods in use now-a-days! ![]() Human Brain: Have you tried to search online the keywords "number of neurons in the brain"? The answer is invariably 100 billions! There is another data that we use only 1% of brain and hence do the number 1 billion neurons not astonish you? Can we build a machine and the learning algorithm to deal with similar number of neurons?
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