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“Really useful and transferable skills. Much better content and materials than Training the Street.” Ramiro Del Valle, Barclays Investment Bank

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Gartner Top 10 Trends in Data and Analytics for 2020

In response to the COVID-19 emergency, over 500 clinical trials of potential COVID-19 treatments and interventions began worldwide. The trials use a living database that compiles and curates data from trial registries and other sources. This helps medical and public health experts predict disease spread, find new treatments and plan for clinical management of the pandemic.

Explore the latest: Gartner Top 10 Data and Analytics Trends for 2021

Data and analytics combined with artificial intelligence (AI) technologies will be paramount in the effort to predict, prepare and respond in a proactive and accelerated manner to a global crisis and its aftermath.

Download roadmap: IT Roadmap for Data and Analytics

“In the face of unprecedented market shifts, data and analytics leaders require an ever-increasing velocity and scale of analysis in terms of processing and access to accelerate innovation and forge new paths  to a post-COVID-19 world,” said Rita Sallam, Distinguished VP Analyst, during her presentation at virtual Gartner IT Symposium/Xpo™ 2020 .

Here are the top 10 technology trends that data and analytics leaders should focus on as they look to make essential investments to prepare for a reset.

Trend 1: Smarter, faster, more responsible AI

By the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving a 5X increase in streaming data and analytics infrastructures.

Within the current pandemic context, AI techniques such as machine learning (ML), optimization and natural language processing (NLP) are providing vital insights and predictions about the spread of the virus and the effectiveness and impact of countermeasures. AI and machine learning are critical realigning supply and the supply chain to new demand patterns.

“ Pre-COVID models based on historical data may no longer be valid ”

AI techniques such as reinforcement learning and distributed learning are creating more adaptable and flexible systems to handle complex business situations; for example, agent-based systems can model and stimulate complex systems - particularly now when pre-COVID models based on historical data may no longer be valid. 

Significant investments made in new chip architectures such as neuromorphic hardware that can be deployed on edge devices are accelerating AI and ML computations and workloads and reducing reliance on centralized systems that require high bandwidths. Eventually, this could lead to more scalable AI solutions that have higher business impact.

Responsible AI that enables model transparency is essential to protect against poor decisions. It results in better human-machine collaboration and trust for greater adoption and alignment of decisions throughout the organization.

Trend 2: Decline of the dashboard

Dynamic data stories with more automated and consumerized experiences will replace visual, point-and-click authoring and exploration. As a result, the amount of time users spend using predefined dashboards will decline. The shift to in-context data stories means that the most relevant insights will stream to each user based on their context, role or use. These dynamic insights leverage technologies such as augmented analytics, NLP, streaming anomaly detection and collaboration.

Data and analytics leaders need to regularly evaluate their existing analytics and business intelligence (BI) tools and innovative startups offering new augmented and NLP-driven user experiences beyond the predefined dashboard.

Trend 3: Decision intelligence

By 2023, more than 33% of large organizations will have analysts practicing decision intelligence, including decision modeling.

Decision intelligence brings together a number of disciplines, including decision management and decision support. It encompasses applications in the field of complex adaptive systems that bring together multiple traditional and advanced disciplines.

It provides a framework to help data and analytics leaders design, compose, model, align, execute, monitor and tune decision models and processes in the context of business outcomes and behavior.

Explore using decision management and modeling technology when decisions need multiple logical and mathematical techniques, must be automated or semi-automated, or must be documented and audited.

Trend 4: X analytics

Gartner coined the term “X analytics” to be an umbrella term, where X is the data variable for a range of different structured and unstructured content such as text analytics, video analytics, audio analytics, etc.

Data and analytics leaders use X analytics to solve society’s toughest challenges, including climate change, disease prevention and wildlife protection.

During the pandemic, AI has been critical in combing through thousands of research papers, news sources, social media posts and clinical trials data to help medical and public health experts predict disease spread, capacity-plan, find new treatments and identify vulnerable populations. X analytics combined with AI and other techniques such as graph analytics (another top trend) will play a key role in identifying, predicting and planning for natural disasters and other business crises and opportunities in the future.

Data and analytics leaders should explore X analytics capabilities available from their existing vendors, such as cloud vendors for image, video and voice analytics, but recognize that innovation will likely come from small disruptive startups and cloud providers.

Источник: https://www.gartner.com/smarterwithgartner/gartner-top-10-trends-in-data-and-analytics-for-2020

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Machine learning

Study of algorithms that improve automatically through experience

For the journal, see Machine Learning (journal).

"Statistical learning" redirects here. For statistical learning in linguistics, see statistical learning in language acquisition.

Machine learning (ML) is the study of computer algorithms that can improve automatically through experience and by the use of data.[1] It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so.[2] Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.[3]

A subset of machine learning is closely related to computational statistics, which focuses on making predictions using computers; but not all machine learning is statistical learning. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning.[5][6] Some implementations of machine learning use data and neural networks in a way that mimics the working of a biological brain.[7][8] In its application across business problems, machine learning is also referred to as predictive analytics.

Overview[edit]

Learning algorithms work on the basis that strategies, algorithms, and inferences that worked well in the past are likely to continue working well in the future. These inferences can be obvious, such as "since the sun rose every morning for the last 10,000 days, it will probably rise tomorrow morning as well". They can be nuanced, such as "X% of families have geographically separate species with color variants, so there is a Y% chance that undiscovered black swans exist".

Machine learning programs can perform tasks without being explicitly programmed to do so. It involves computers learning from data provided so that they carry out certain tasks. For simple tasks assigned to computers, it is possible to program algorithms telling PDF Shaper Download - Crack Key For U machine how to execute all steps required to solve the problem at hand; on the computer's part, no learning is needed. For more advanced tasks, it can be challenging for a human to manually create the needed algorithms. In practice, it can turn out to be more effective to help the machine develop its own algorithm, rather than having human programmers specify every needed step.[10]

The discipline of machine learning employs various approaches to teach computers to accomplish tasks where no fully satisfactory algorithm is available. In cases where vast numbers of potential answers exist, one approach is to label some of the correct answers as valid. This can then be used as training data for the computer to improve the algorithm(s) it uses to determine correct answers. For example, to train a system for the task of digital character recognition, the MNIST dataset of handwritten digits has often been used.[10]

History and relationships to other fields[edit]

See also: Timeline of machine learning

The term machine learning was coined in 1959 by Arthur Samuel, an American IBMer and pioneer in the field of computer gaming and artificial intelligence.[11][12] Also the synonym self-teaching computers was used in this time period.[13][14] A representative book of the machine learning research during the 1960s was the Nilsson's book on Learning Machines, dealing mostly with machine learning for pattern classification.[15] Interest related to pattern recognition continued into the 1970s, as described by Duda and Hart in 1973.[16] In 1981 a report was given on using teaching strategies so that a neural network learns to recognize 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal.[17]

Tom M. 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."[18] This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Training - 10 (Ten) Crack Software Collection proposal in his paper "Computing Machinery and Intelligence", in which the question "Can machines think?" is replaced with the question "Can machines do what we (as thinking entities) can do?".[19]

Modern day machine learning has two objectives, one is to classify data based on models which have been developed, the other Training - 10 (Ten) Crack Software Collection is to make predictions for future outcomes based on these models. A hypothetical algorithm specific to classifying data may use computer vision of moles coupled with supervised learning in order to train it to classify the cancerous moles. Where as, a machine learning algorithm for stock trading may inform the trader of future potential predictions.[20]

Artificial intelligence[edit]

Part of machine learning as subfield of AI or part of AI as subfield of machine learning[22]

As a scientific endeavor, machine learning grew out of the quest for artificial intelligence. In the early days of AI as an academic discipline, some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what was then termed "neural networks"; these were mostly perceptrons and other models that were later found to be reinventions of the generalized linear models of statistics.[23]Probabilistic reasoning was also employed, especially in automated medical diagnosis.[24]: 488 

However, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation.[24]: 488  By 1980, expert systems had come to dominate AI, and statistics was out of favor.[25] 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.[24]: 708–710, 755  Neural networks research had been abandoned by AI and computer science around the same time. This line, too, was continued outside the AI/CS field, as "connectionism", by researchers from other disciplines including Hopfield, Rumelhart and Hinton. Their main success came in the mid-1980s with the reinvention of backpropagation.[24]: 25 

Machine learning (ML), reorganized as a separate field, started to flourish Training - 10 (Ten) Crack Software Collection the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics and probability theory.[25]

The difference between ML and AI is frequently misunderstood. ML learns and predicts based on passive observations, whereas AI implies an agent interacting with the environment to learn and take actions that maximize its chance of successfully achieving its goals.[26]

As of 2020, many sources continue to assert that ML remains a subfield of AI.[27][28][25] Others have the view that not all ML is part of AI, but only an 'intelligent subset' of ML should be considered AI.[4][29][30]

Data mining[edit]

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). Data driver booster 7.1.0.534 key - Activators Patch uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. 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.

Optimization[edit]

Machine learning also has intimate ties to optimization: many learning problems are formulated as minimization of some loss function on a training set of examples. 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).[31]

Generalization[edit]

The difference between optimization and machine learning 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. Characterizing the generalization of various learning algorithms is an active topic of current research, especially for deep learning algorithms.

Statistics[edit]

Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population inferences from a sample, while machine learning finds generalizable predictive patterns.[32] According to Michael I. Jordan, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics.[33] He also suggested the term data science as a placeholder to call the overall field.[33]

Leo Breiman distinguished two statistical modeling paradigms: data model and algorithmic model,[27] wherein "algorithmic model" means more or less the machine learning algorithms like Random forest.

Some statisticians have adopted methods from machine learning, leading to a combined field that they call statistical learning.[28]

Theory[edit]

Main articles: Computational learning theory and Statistical learning theory

A core objective of a learner is to generalize from its experience.[4][29] Generalization in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set. 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.

The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory. Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms. Instead, probabilistic bounds on the performance are quite common. The bias–variance decomposition is one way to quantify generalization error.

For the best performance in the context of generalization, the complexity of the hypothesis should match the complexity of the function underlying the data. If the hypothesis is less complex than the function, then the model has under fitted the data. If the complexity of the model is increased in response, then the training error decreases. But if the hypothesis is too complex, then the model is subject to overfitting and generalization will be poorer.[30]

In addition to performance bounds, learning theorists study the time complexity and feasibility of learning. In computational learning theory, a computation is considered feasible if it can be done in polynomial time. There are two kinds of time complexity results. Positive results show that a certain class of functions can be learned in polynomial time. Negative results show that certain classes cannot be learned in polynomial time.

Approaches[edit]

Machine learning approaches are traditionally divided into three broad categories, depending on the nature of the "signal" or "feedback" available to the learning system:

  • Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs.
  • Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).
  • Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). As it navigates its problem space, the program is provided feedback that's analogous to rewards, which it tries to maximize.[4]

Supervised learning[edit]

Main article: Supervised learning

A support-vector machineis a supervised learning model that divides the data into regions separated by a linear boundary. Here, the linear boundary divides the black circles from the white.

Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs.[34] The data is known as training data, and consists of a set of training examples. Each training example has one or more inputs and the desired output, also known as a supervisory signal. In the mathematical model, each training example is represented by an array or vector, sometimes called a feature vector, and the training data is represented by a matrix. Through iterative optimization of an objective function, supervised learning algorithms learn a function that can be used to predict the output associated with new inputs.[35] An optimal function will allow the algorithm to correctly determine the output for inputs that were not a part of the training data. An algorithm that improves the accuracy of its outputs or predictions over time is said to have learned to perform that task.[18]

Types of supervised learning algorithms include active learning, classification and regression.[26] Classification algorithms are used when the outputs are restricted to a limited set of values, and regression algorithms are used when the outputs may have any numerical value within a range. As an example, for a classification algorithm that filters emails, the input would be an incoming email, and the output would be the name of the folder in which to file the email.

Similarity learning is an area of supervised machine learning closely related to regression and classification, but the goal is to learn from examples using a similarity function that measures how similar or related two objects are. It has applications in ranking, recommendation systems, visual identity tracking, face verification, and speaker verification.

Unsupervised learning[edit]

Main article: Unsupervised learning

See also: Cluster analysis

Unsupervised learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. The algorithms, therefore, learn from test data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. A central application of unsupervised learning is in the field of density estimation in statistics, such as finding the probability density function.[36] Though unsupervised learning encompasses other domains involving summarizing and explaining data features.

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 one or more predesignated 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, or the similarity between members of the same cluster, and separation, the difference between clusters. Other methods are based on estimated density and graph connectivity.

Semi-supervised learning[edit]

Main article: Semi-supervised learning

Semi-supervised learning falls between unsupervised learning (without any labeled training data) and supervised learning (with completely labeled training data). Some of the training examples are missing training labels, yet many machine-learning researchers have found that unlabeled data, when used in conjunction with a small amount of labeled data, can produce a considerable improvement in learning accuracy.

In weakly supervised learning, the training labels are noisy, limited, or imprecise; however, these labels are often cheaper to obtain, resulting in larger effective training sets.[37]

Reinforcement learning[edit]

Main article: Reinforcement learning

Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Due to its generality, the field is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In machine learning, the environment is typically represented as a Markov decision process (MDP). Many reinforcement learning algorithms use dynamic programming techniques.[38] Reinforcement learning algorithms do not assume knowledge of an exact mathematical model of the MDP, and are used when exact models are infeasible. Reinforcement learning algorithms are used in autonomous vehicles or in learning to play a game against a human opponent.

Dimensionality reduction[edit]

Dimensionality reduction is a process of reducing the number of random variables under consideration by obtaining a set of principal variables.[39] In other words, it is a process of reducing the dimension of the feature set, also called "number of features". Most of the dimensionality reduction techniques can be considered as either feature elimination or extraction. One of the popular methods of dimensionality reduction is principal component analysis (PCA). PCA involves changing higher-dimensional data (e.g., 3D) to a smaller space (e.g., 2D). This results in a smaller dimension of data (2D instead of 3D), while keeping all original variables in the model without changing the data.[40] The manifold hypothesis proposes that high-dimensional data sets lie along low-dimensional Training - 10 (Ten) Crack Software Collection, and many dimensionality reduction techniques make this assumption, leading to the area of manifold learning and manifold regularization.

Other types[edit]

Other approaches have been developed which don't fit neatly into this three-fold categorisation, and sometimes more than one is used by the same machine learning system. For example topic modeling, meta learning.[41]

As of 2020, deep learning has become the dominant approach for much ongoing work in the field of machine learning.[10]

Self learning[edit]

Self-learning as a machine learning paradigm was introduced in 1982 along with a neural network capable of self-learning named crossbar adaptive array (CAA).[42] It is a learning with no external rewards and no external teacher advice. The CAA self-learning algorithm computes, in a crossbar fashion, both decisions about actions and emotions (feelings) about consequence situations. The system is driven by the interaction between cognition and emotion.[43] The self-learning algorithm updates a memory matrix W = Wharton Business School

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Источник: https://www.educative.io/courses/grokking-the-system-design-interview
Training  - 10 (Ten) Crack Software Collection

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Comments

  1. В @sen carВ  they really let anyone become developers these days huh?

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