AI News, Machine Learning & Data Analysts: Seizing the Opportunity in 2018
Machine Learning & Data Analysts: Seizing the Opportunity in 2018
Undoubtedly, 2017 has been yet another hype year for machine learning (ML) and artificial intelligence (AI).
Becoming successful with data requires collaboration across teams, placing new concepts – such as reusability and reproducibility of data models – at the heart of the business.
According to a recent study by Bitkom Research and KPMG, approximately 60 percent of German companies have already managed to either reduce risk, reduce costs or increase revenue through the use of data science (including ML and AI).
Businesses that were ill-equipped in the past to manage large amounts of data are in the process of “gearing up.” This trend coincides with a rise in the adoption of data science platforms.
On top of scalability, however, they provide the tools necessary for a data team to easily manage ever-increasing volumes of data, innovate in a competitive market, move away from error-prone ad-hoc methodology, easily reproduce processes and data projects, and – perhaps most importantly with the coming of the EU General Data Protection Regulation (GDPR) – have proper data governance and permanence in place.
Businesses are learning firsthand the famous adage whereby 80 percent of a typical data science project is sourcing, cleaning and preparing the data, while the remaining 20 percent is actual data analysis.
In order to help data analysts hop into the realm of machine learning, Dataiku, which provides a collaborative data science platform software, has put together a free, illustrated guide.
19 Data Science and Machine Learning Tools for people who Don’t Know Programming
This article was originally published on 5 May, 2016 and updated with the latest tools on May 16, 2018.
Among other things, it is acknowledged that a person who understands programming logic, loops and functions has a higher chance of becoming a successful data scientist.
There are tools that typically obviate the programming aspect and provide user-friendly GUI (Graphical User Interface) so that anyone with minimal knowledge of algorithms can simply use them to build high quality machine learning models.
The tool is open-source for old version (below v6) but the latest versions come in a 14-day trial period and licensed after that.
RM covers the entire life-cycle of prediction modeling, starting from data preparation to model building and finally validation and deployment.
You just have to connect them in the right manner and a large variety of algorithms can be run without a single line of code.
There current product offerings include the following: RM is currently being used in various industries including automotive, banking, insurance, life Sciences, manufacturing, oil and gas, retail, telecommunication and utilities.
BigML provides a good GUI which takes the user through 6 steps as following: These processes will obviously iterate in different orders. The BigML platform provides nice visualizations of results and has algorithms for solving classification, regression, clustering, anomaly detection and association discovery problems.
Cloud AutoML is part of Google’s Machine Learning suite offerings that enables people with limited ML expertise to build high quality models. The first product, as part of the Cloud AutoML portfolio, is Cloud AutoML Vision.
This service makes it simpler to train image recognition models. It has a drag-and-drop interface that let’s the user upload images, train the model, and then deploy those models directly on Google Cloud.
It also provides visual guidance making it easy to bring together data, find and fix dirty or missing data, and share and re-use data projects across teams.
Also, for each column it automatically recommends some transformations which can be selected using a single click. Various transformations can be performed on the data using some pre-defined functions which can be called easily in the interface.
Trifacta platform uses the following steps of data preparation: Trifacta is primarily used in the financial, life sciences and telecommunication industries.
The core idea behind this is to provide an easy solution for applying machine learning to large scale problems.
All you have to do is using simple dropdowns select the files for train, test and mention the metric using which you want to track model performance.
Sit back and watch as the platform with an intuitive interface trains on your dataset to give excellent results at par with a good solution an experienced data scientist can come up with.
It also comes with built-in integration with the Amazon Web Services (AWS) platform. Amazon Lex is a fully managed service so as your user engagement increases, you don’t need to worry about provisioning hardware and managing infrastructure to improve your bot experience.
You can interactively discover, clean and transform your data, use familiar open source tools with Jupyter notebooks and RStudio, access the most popular libraries, train deep neural networks, among a a vast array of other things.
It can take in various kinds of data and uses natural language processing at it’s core to generate a detailed report.
But these are excellent tools to assist organizations that are looking to start out with machine learning or are looking for alternate options to add to their existing catalogue.
Top Tools for Data Scientists: Analytics Tools, Data Visualization Tools, Database Tools, and More
Data scientists are inquisitive and often seek out new tools that help them find answers.
Overall, data scientists should have a working knowledge of statistical programming languages for constructing data processing systems, databases, and visualization tools.
however, not all data scientist students study programming, so it is helpful to be aware of tools that circumvent programming and include a user-friendly graphical interface so that data scientists’
This tool turns raw data into real-time insights and actionable events so that companies are in a better position to deploy machine learning for streaming data.
An iterative graph processing system designed for high scalability, Apache Giraph began as an open source counterpart to Pregel but adds multiple features beyond the basic Pregel model.
A framework allowing for the distributed processing of large datasets across clusters of computers, the software library uses simple programming models.
This tool is a data warehouse software that assists in reading, writing, and managing large datasets that reside in distributed storage using SQL.
Data scientists use this tool to build real-time data pipelines and streaming apps because it empowers you to publish and subscribe to streams of records, store streams of records in a fault-tolerant way, and process streams of records as they occur.
An open source Apache Foundation project for machine learning, Apache Mahout aims to enable scalable machine learning and data mining.
Mesos abstracts CPU, memory, storage, and other resources away from physical or virtual machines to enable fault-tolerant, elastic distributed systems to be built easily and run effectively.
platform designed for analyzing large datasets, Apache Pig consists of a high-level language for expressing data analysis programs that is coupled with infrastructure for evaluating such programs.
A wide range of organizations use Spark to process large datasets, and this data scientist tool can access diverse data sources such as HDFS, Cassandra, HBase, and S3.
BigML makes it simple to solve and automate classification, regression, cluster analysis, anomaly detection, association discovery, and topic modeling tasks.
Python interactive visualization library, Bokeh targets modern web browsers for presentation and helps users create interactive plots, dashboards, and data apps easily.
Users can solve simple and complex data problems with Cascading because it boasts computation engine, systems integration framework, data processing, and scheduling capabilities.
robust and fast programming language, Clojure is a practical tool that marries the interactive development of a scripting language with an efficient infrastructure for multithreaded programming.
An advanced machine learning automation platform, DataRobot helps data scientists build better predictive models faster.
The company strives to make deep learning relevant for finance and economics by enabling investment managers, quantitative analysts, and data scientists to use their own data to generate robust forecasts and optimize complex future objectives.
An experimental app, Fusion Tables is a data visualization web application tool for data scientists that empowers you to gather, visualize, and share data tables.
With ggplot2, data scientists can avoid many of the hassles of plotting while maintaining the attractive parts of base and lattice graphics and producing complex multi-layered graphics easily.
Java is a language with a broad user base that serves as a tool for data scientists creating products and frameworks involving distributed systems, data analysis, and machine learning. Java now is recognized as being just as important to data science as R and Python because it is robust, convenient, and scalable for data science applications.
Its Notebook, an open source web application, allows data scientists to create and share documents containing live code, equations, visualizations, and explanatory text.
The KNIME Analytics Platform is a leading open solution for data-driven innovation to help data scientists uncover data’s hidden potential, mine for insights, and predict futures.
high-level language and interactive environment for numerical computation, visualization, and programming, MATLAB is a powerful tool for data scientists.
Matplotlib is a Python 2D plotting library that produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms.
GNU Octave is a scientific programming language that is a useful tool for data scientists looking to solve systems of equations or visualize data with high-level plot commands.
pandas is an open source library that delivers high-performance, easy-to-use data structures and data analysis tools for the Python programming language.
A tool for making data science fast and simple, RapidMiner is a leader in the 2017 Gartner Magic Quadrant for Data Science Platforms, a leader in 2017 Forrester Wave for predictive analytics and machine learning, and a high performer in the G2 Crowd predictive analytics grid.
The Scala programming language is a tool for data scientists looking to construct elegant class hierarchies to maximize code reuse and extensibility.
Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to 'learn' (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed.
These analytical models allow researchers, data scientists, engineers, and analysts to 'produce reliable, repeatable decisions and results' and uncover 'hidden insights' through learning from historical relationships and trends in the data.
Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: 'A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.'
Developmental learning, elaborated for robot learning, generates its own sequences (also called curriculum) of learning situations to cumulatively acquire repertoires of novel skills through autonomous self-exploration and social interaction with human teachers and using guidance mechanisms such as active learning, maturation, motor synergies, and imitation.
Work on symbolic/knowledge-based learning did continue within AI, leading to inductive logic programming, but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval.:708–710;
Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases).
Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously unknown knowledge.
Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.
Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples).
The difference between the two fields arises from the goal of generalization: while optimization algorithms can minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples.
The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases.
An artificial neural network (ANN) learning algorithm, usually called 'neural network' (NN), is a learning algorithm that is vaguely inspired by biological neural networks.
They are usually used to model complex relationships between inputs and outputs, to find patterns in data, or to capture the statistical structure in an unknown joint probability distribution between observed variables.
Falling hardware prices and the development of GPUs for personal use in the last few years have contributed to the development of the concept of deep learning which consists of multiple hidden layers in an artificial neural network.
Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples.
Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.
Cluster analysis is the assignment of a set of observations into subsets (called clusters) so that observations within the same cluster are similar according to some predesignated criterion or criteria, while observations drawn from different clusters are dissimilar.
Different clustering techniques make different assumptions on the structure of the data, often defined by some similarity metric and evaluated for example by internal compactness (similarity between members of the same cluster) and separation between different clusters.
Bayesian network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independencies via a directed acyclic graph (DAG).
Representation learning algorithms often attempt to preserve the information in their input but transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions, allowing reconstruction of the inputs coming from the unknown data generating distribution, while not being necessarily faithful for configurations that are implausible under that distribution.
Deep learning algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of (or generating) lower-level features.
genetic algorithm (GA) is a search heuristic that mimics the process of natural selection, and uses methods such as mutation and crossover to generate new genotype in the hope of finding good solutions to a given problem.
In 2006, the online movie company Netflix held the first 'Netflix Prize' competition to find a program to better predict user preferences and improve the accuracy on its existing Cinematch movie recommendation algorithm by at least 10%.
Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ('everything is a recommendation') and they changed their recommendation engine accordingly.
Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.
Classification machine learning models can be validated by accuracy estimation techniques like the Holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set.
In comparison, the N-fold-cross-validation method randomly splits the data in k subsets where the k-1 instances of the data are used to train the model while the kth instance is used to test the predictive ability of the training model.
For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.
There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these 'greed' biases are addressed.
Different bias and fairness criteria need to be used for different types of interventions. Aequitas allows audits to be done across multiple metrics
Machine Learning, AI and Data Science based predictive tools are being increasingly used in problems that can have a drastic impact on people’s lives in policy areas such as criminal justice, education, public health, workforce development and social services.
Aequitas, an open source bias audit toolkit developed by the Center for Data Science and Public Policy at University of Chicago, can be used to audit the predictions of machine learning based risk assessment tools to understand different types of biases, and make informed decisions about developing and deploying such systems.
- On Thursday, June 20, 2019
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