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Machine Learning Chips Market

The global Machine Learning Chips market is expected to grow at a significant pace, reports QY Research. Its latest research report, titled [name of the report], offers a unique point of view about the global market. The publication offers an insightful take on the historical data of the market and the milestones it has achieved. The report also includes an assessment of current market trends and dynamics, which helps in mapping the trajectory of the global Machine Learning Chips market. Analysts have used Porters five forces analysis and SWOT analysis to explain the various elements of the market in absolute detail. They have also provided accurate data on Machine Learning Chips production, capacity, price, cost, margin, and revenue to help the players gain a clear understanding into the overall existing and future market situation.

Key companies operating in the global Machine Learning Chips market include: :, Wave Computing, Graphcore, Google Inc, Intel Corporation, IBM Corporation, Nvidia Corporation, Qualcomm, Taiwan Semiconductor Manufacturing

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The research report is committed to giving its readers an unbiased point of view of the global Machine Learning Chips market. Thus, along with statistics, it includes opinions and recommendation of market experts. This allows the readers to acquire a holistic view of the global market and the segments therein. The research report includes the study of the market segments on the basis of type, application, and region. This helps in identifying segment-specific drivers, restraints, threats, and opportunities.

Segmental Analysis

The report has classified the global Machine Learning Chips industry into segments including product type and application. Every segment is evaluated based on growth rate and share. Besides, the analysts have studied the potential regions that may prove rewarding for the Machine Learning Chips manufcaturers in the coming years. The regional analysis includes reliable predictions on value and volume, thereby helping market players to gain deep insights into the overall Machine Learning Chips industry.

Global Machine Learning Chips Market Segment By Type:

Neuromorphic Chip, Graphics Processing Unit (GPU) Chip, Flash Based Chip, Field Programmable Gate Array (FPGA) Chip, Other

Global Machine Learning Chips Market Segment By Application:

, Robotics Industry, Consumer Electronics, Automotive, Healthcare, Other

Competitive Landscape:

It is important for every market participant to be familiar with the competitive scenario in the global Machine Learning Chips industry. In order to fulfil the requirements, the industry analysts have evaluated the strategic activities of the competitors to help the key players strengthen their foothold in the market and increase their competitiveness.

Key companies operating in the global Machine Learning Chips market include::, Wave Computing, Graphcore, Google Inc, Intel Corporation, IBM Corporation, Nvidia Corporation, Qualcomm, Taiwan Semiconductor Manufacturing

Key questions answered in the report:

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TOC

Table of Contents 1 Machine Learning Chips Market Overview1.1 Machine Learning Chips Product Overview1.2 Machine Learning Chips Market Segment by Type1.2.1 Neuromorphic Chip1.2.2 Graphics Processing Unit (GPU) Chip1.2.3 Flash Based Chip1.2.4 Field Programmable Gate Array (FPGA) Chip1.2.5 Other1.3 Global Machine Learning Chips Market Size by Type (2015-2026)1.3.1 Global Machine Learning Chips Market Size Overview by Type (2015-2026)1.3.2 Global Machine Learning Chips Historic Market Size Review by Type (2015-2020)1.3.2.1 Global Machine Learning Chips Sales Market Share Breakdown by Type (2015-2026)1.3.2.2 Global Machine Learning Chips Revenue Market Share Breakdown by Type (2015-2026)1.3.2.3 Global Machine Learning Chips Average Selling Price (ASP) by Type (2015-2026)1.3.3 Global Machine Learning Chips Market Size Forecast by Type (2021-2026)1.3.3.1 Global Machine Learning Chips Sales Market Share Breakdown by Application (2021-2026)1.3.3.2 Global Machine Learning Chips Revenue Market Share Breakdown by Application (2021-2026)1.3.3.3 Global Machine Learning Chips Average Selling Price (ASP) by Application (2021-2026)1.4 Key Regions Market Size Segment by Type (2015-2020)1.4.1 North America Machine Learning Chips Sales Breakdown by Type (2015-2026)1.4.2 Europe Machine Learning Chips Sales Breakdown by Type (2015-2026)1.4.3 Asia-Pacific Machine Learning Chips Sales Breakdown by Type (2015-2026)1.4.4 Latin America Machine Learning Chips Sales Breakdown by Type (2015-2026)1.4.5 Middle East and Africa Machine Learning Chips Sales Breakdown by Type (2015-2026) 2 Global Machine Learning Chips Market Competition by Company2.1 Global Top Players by Machine Learning Chips Sales (2015-2020)2.2 Global Top Players by Machine Learning Chips Revenue (2015-2020)2.3 Global Top Players Machine Learning Chips Average Selling Price (ASP) (2015-2020)2.4 Global Top Manufacturers Machine Learning Chips Manufacturing Base Distribution, Sales Area, Product Type2.5 Machine Learning Chips Market Competitive Situation and Trends2.5.1 Machine Learning Chips Market Concentration Rate (2015-2020)2.5.2 Global 5 and 10 Largest Manufacturers by Machine Learning Chips Sales and Revenue in 20192.6 Global Top Manufacturers by Company Type (Tier 1, Tier 2 and Tier 3) (based on the Revenue in Machine Learning Chips as of 2019)2.7 Date of Key Manufacturers Enter into Machine Learning Chips Market2.8 Key Manufacturers Machine Learning Chips Product Offered2.9 Mergers & Acquisitions, Expansion 3 Global Machine Learning Chips Status and Outlook by Region (2015-2026)3.1 Global Machine Learning Chips Market Size and CAGR by Region: 2015 VS 2020 VS 20263.2 Global Machine Learning Chips Market Size Market Share by Region (2015-2020)3.2.1 Global Machine Learning Chips Sales Market Share by Region (2015-2020)3.2.2 Global Machine Learning Chips Revenue Market Share by Region (2015-2020)3.2.3 Global Machine Learning Chips Sales, Revenue, Price and Gross Margin (2015-2020)3.3 Global Machine Learning Chips Market Size Market Share by Region (2021-2026)3.3.1 Global Machine Learning Chips Sales Market Share by Region (2021-2026)3.3.2 Global Machine Learning Chips Revenue Market Share by Region (2021-2026)3.3.3 Global Machine Learning Chips Sales, Revenue, Price and Gross Margin (2021-2026)3.4 North America Machine Learning Chips Market Size YoY Growth (2015-2026)3.4.1 North America Machine Learning Chips Revenue YoY Growth (2015-2026)3.4.2 North America Machine Learning Chips Sales YoY Growth (2015-2026)3.5 Asia-Pacific Machine Learning Chips Market Size YoY Growth (2015-2026)3.5.1 Asia-Pacific Machine Learning Chips Revenue YoY Growth (2015-2026)3.5.2 Asia-Pacific Machine Learning Chips Sales YoY Growth (2015-2026)3.6 Europe Machine Learning Chips Market Size YoY Growth (2015-2026)3.6.1 Europe Machine Learning Chips Revenue YoY Growth (2015-2026)3.6.2 Europe Machine Learning Chips Sales YoY Growth (2015-2026)3.7 Latin America Machine Learning Chips Market Size YoY Growth (2015-2026)3.7.1 Latin America Machine Learning Chips Revenue YoY Growth (2015-2026)3.7.2 Latin America Machine Learning Chips Sales YoY Growth (2015-2026)3.8 Middle East and Africa Machine Learning Chips Market Size YoY Growth (2015-2026)3.8.1 Middle East and Africa Machine Learning Chips Revenue YoY Growth (2015-2026)3.8.2 Middle East and Africa Machine Learning Chips Sales YoY Growth (2015-2026) 4 Global Machine Learning Chips by Application4.1 Machine Learning Chips Segment by Application4.1.1 Robotics Industry4.1.2 Consumer Electronics4.1.3 Automotive4.1.4 Healthcare4.1.5 Other4.2 Global Machine Learning Chips Sales by Application: 2015 VS 2020 VS 20264.3 Global Machine Learning Chips Historic Sales by Application (2015-2020)4.4 Global Machine Learning Chips Forecasted Sales by Application (2021-2026)4.5 Key Regions Machine Learning Chips Market Size by Application4.5.1 North America Machine Learning Chips by Application4.5.2 Europe Machine Learning Chips by Application4.5.3 Asia-Pacific Machine Learning Chips by Application4.5.4 Latin America Machine Learning Chips by Application4.5.5 Middle East and Africa Machine Learning Chips by Application 5 North America Machine Learning Chips Market Size by Country (2015-2026)5.1 North America Market Size Market Share by Country (2015-2020)5.1.1 North America Machine Learning Chips Sales Market Share by Country (2015-2020)5.1.2 North America Machine Learning Chips Revenue Market Share by Country (2015-2020)5.2 North America Market Size Market Share by Country (2021-2026)5.2.1 North America Machine Learning Chips Sales Market Share by Country (2021-2026)5.2.2 North America Machine Learning Chips Revenue Market Share by Country (2021-2026)5.3 North America Market Size YoY Growth by Country5.3.1 U.S. Machine Learning Chips Market Size YoY Growth (2015-2026)5.3.2 Canada Machine Learning Chips Market Size YoY Growth (2015-2026) 6 Europe Machine Learning Chips Market Size by Country (2015-2026)6.1 Europe Market Size Market Share by Country (2015-2020)6.1.1 Europe Machine Learning Chips Sales Market Share by Country (2015-2020)6.1.2 Europe Machine Learning Chips Revenue Market Share by Country (2015-2020)6.2 Europe Market Size Market Share by Country (2021-2026)6.2.1 Europe Machine Learning Chips Sales Market Share by Country (2021-2026)6.2.2 Europe Machine Learning Chips Revenue Market Share by Country (2021-2026)6.3 Europe Market Size YoY Growth by Country6.3.1 Germany Machine Learning Chips Market Size YoY Growth (2015-2026)6.3.2 France Machine Learning Chips Market Size YoY Growth (2015-2026)6.3.3 U.K. Machine Learning Chips Market Size YoY Growth (2015-2026)6.3.4 Italy Machine Learning Chips Market Size YoY Growth (2015-2026)6.3.5 Russia Machine Learning Chips Market Size YoY Growth (2015-2026) 7 Asia-Pacific Machine Learning Chips Market Size by Country (2015-2026)7.1 Asia-Pacific Market Size Market Share by Country (2015-2020)7.1.1 Asia-Pacific Machine Learning Chips Sales Market Share by Country (2015-2020)7.1.2 Asia-Pacific Machine Learning Chips Revenue Market Share by Country (2015-2020)7.2 Asia-Pacific Market Size Market Share by Country (2021-2026)7.2.1 Asia-Pacific Machine Learning Chips Sales Market Share by Country (2021-2026)7.2.2 Asia-Pacific Machine Learning Chips Revenue Market Share by Country (2021-2026)7.3 Asia-Pacific Market Size YoY Growth by Country7.3.1 China Machine Learning Chips Market Size YoY Growth (2015-2026)7.3.2 Japan Machine Learning Chips Market Size YoY Growth (2015-2026)7.3.3 South Korea Machine Learning Chips Market Size YoY Growth (2015-2026)7.3.4 India Machine Learning Chips Market Size YoY Growth (2015-2026)7.3.5 Australia Machine Learning Chips Market Size YoY Growth (2015-2026)7.3.6 Taiwan Machine Learning Chips Market Size YoY Growth (2015-2026)7.3.7 Indonesia Machine Learning Chips Market Size YoY Growth (2015-2026)7.3.8 Thailand Machine Learning Chips Market Size YoY Growth (2015-2026)7.3.9 Malaysia Machine Learning Chips Market Size YoY Growth (2015-2026)7.3.10 Philippines Machine Learning Chips Market Size YoY Growth (2015-2026)7.3.11 Vietnam Machine Learning Chips Market Size YoY Growth (2015-2026) 8 Latin America Machine Learning Chips Market Size by Country (2015-2026)8.1 Latin America Market Size Market Share by Country (2015-2020)8.1.1 Latin America Machine Learning Chips Sales Market Share by Country (2015-2020)8.1.2 Latin America Machine Learning Chips Revenue Market Share by Country (2015-2020)8.2 Latin America Market Size Market Share by Country (2021-2026)8.2.1 Latin America Machine Learning Chips Sales Market Share by Country (2021-2026)8.2.2 Latin America Machine Learning Chips Revenue Market Share by Country (2021-2026)8.3 Latin America Market Size YoY Growth by Country8.3.1 Mexico Machine Learning Chips Market Size YoY Growth (2015-2026)8.3.2 Brazil Machine Learning Chips Market Size YoY Growth (2015-2026)8.3.3 Argentina Machine Learning Chips Market Size YoY Growth (2015-2026) 9 Middle East and Africa Machine Learning Chips Market Size by Country (2015-2026)9.1 Middle East and Africa Market Size Market Share by Country (2015-2020)9.1.1 Middle East and Africa Machine Learning Chips Sales Market Share by Country (2015-2020)9.1.2 Middle East and Africa Machine Learning Chips Revenue Market Share by Country (2015-2020)9.2 Middle East and Africa Market Size Market Share by Country (2021-2026)9.2.1 Middle East and Africa Machine Learning Chips Sales Market Share by Country (2021-2026)9.2.2 Middle East and Africa Machine Learning Chips Revenue Market Share by Country (2021-2026)9.3 Middle East and Africa Market Size YoY Growth by Country9.3.1 Turkey Machine Learning Chips Market Size YoY Growth (2015-2026)9.3.2 Saudi Arabia Machine Learning Chips Market Size YoY Growth (2015-2026)9.3.3 U.A.E Machine Learning Chips Market Size YoY Growth (2015-2026) 10 Company Profiles and Key Figures in Machine Learning Chips Business10.1 Wave Computing10.1.1 Wave Computing Corporation Information10.1.2 Wave Computing Description, Business Overview and Total Revenue10.1.3 Wave Computing Machine Learning Chips Sales, Revenue and Gross Margin (2015-2020)10.1.4 Wave Computing Machine Learning Chips Products Offered10.1.5 Wave Computing Recent Development10.2 Graphcore10.2.1 Graphcore Corporation Information10.2.2 Graphcore Description, Business Overview and Total Revenue10.2.3 Graphcore Machine Learning Chips Sales, Revenue and Gross Margin (2015-2020)10.2.5 Graphcore Recent Development10.3 Google Inc10.3.1 Google Inc Corporation Information10.3.2 Google Inc Description, Business Overview and Total Revenue10.3.3 Google Inc Machine Learning Chips Sales, Revenue and Gross Margin (2015-2020)10.3.4 Google Inc Machine Learning Chips Products Offered10.3.5 Google Inc Recent Development10.4 Intel Corporation10.4.1 Intel Corporation Corporation Information10.4.2 Intel Corporation Description, Business Overview and Total Revenue10.4.3 Intel Corporation Machine Learning Chips Sales, Revenue and Gross Margin (2015-2020)10.4.4 Intel Corporation Machine Learning Chips Products Offered10.4.5 Intel Corporation Recent Development10.5 IBM Corporation10.5.1 IBM Corporation Corporation Information10.5.2 IBM Corporation Description, Business Overview and Total Revenue10.5.3 IBM Corporation Machine Learning Chips Sales, Revenue and Gross Margin (2015-2020)10.5.4 IBM Corporation Machine Learning Chips Products Offered10.5.5 IBM Corporation Recent Development10.6 Nvidia Corporation10.6.1 Nvidia Corporation Corporation Information10.6.2 Nvidia Corporation Description, Business Overview and Total Revenue10.6.3 Nvidia Corporation Machine Learning Chips Sales, Revenue and Gross Margin (2015-2020)10.6.4 Nvidia Corporation Machine Learning Chips Products Offered10.6.5 Nvidia Corporation Recent Development10.7 Qualcomm10.7.1 Qualcomm Corporation Information10.7.2 Qualcomm Description, Business Overview and Total Revenue10.7.3 Qualcomm Machine Learning Chips Sales, Revenue and Gross Margin (2015-2020)10.7.4 Qualcomm Machine Learning Chips Products Offered10.7.5 Qualcomm Recent Development10.8 Taiwan Semiconductor Manufacturing10.8.1 Taiwan Semiconductor Manufacturing Corporation Information10.8.2 Taiwan Semiconductor Manufacturing Description, Business Overview and Total Revenue10.8.3 Taiwan Semiconductor Manufacturing Machine Learning Chips Sales, Revenue and Gross Margin (2015-2020)10.8.4 Taiwan Semiconductor Manufacturing Machine Learning Chips Products Offered10.8.5 Taiwan Semiconductor Manufacturing Recent Development 11 Machine Learning Chips Upstream, Opportunities, Challenges, Risks and Influences Factors Analysis11.1 Machine Learning Chips Key Raw Materials11.1.1 Key Raw Materials11.1.2 Key Raw Materials Price11.1.3 Raw Materials Key Suppliers11.2 Manufacturing Cost Structure11.2.1 Raw Materials11.2.2 Labor Cost11.2.3 Manufacturing Expenses11.3 Machine Learning Chips Industrial Chain Analysis11.4 Market Opportunities, Challenges, Risks and Influences Factors Analysis11.4.1 Market Opportunities and Drivers11.4.2 Market Challenges11.4.3 Market Risks11.4.4 Porters Five Forces Analysis 12 Market Strategy Analysis, Distributors12.1 Sales Channel12.2 Distributors12.3 Downstream Customers 13 Research Findings and Conclusion 14 Appendix14.1 Methodology/Research Approach14.1.1 Research Programs/Design14.1.2 Market Size Estimation14.1.3 Market Breakdown and Data Triangulation14.2 Data Source14.2.1 Secondary Sources14.2.2 Primary Sources14.3 Author Details14.4 Disclaimer*

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Machine Learning Chips Market shares and volumes by 2026 - Cole of Duty

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Software ToolsCloud and Web-based Application Programming Interface (APIs)Other

Machine Learning as a Service Market By Application:

ManufacturingRetailHealthcare & Life SciencesTelecomBFSIOther (Energy & Utilities, Education, Government)

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Machine Learning as a Service

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Global Coronavirus Impact And Implications On Global Machine Learning as a Service Market Analysis, Growth, Trends, Share and Forecast to 2026 -...

Adoption of artificial intelligence (AI) is growing worldwide, according to an IDC survey of more than 2,000 IT and line of business (LoB) decision makers.

Over a quarter of all AI initiatives are already in production and more than one third are in advanced development stages. And organizations are reporting an increase in their AI spending this year.

Delivering a better customer experience was identified as the leading driver for AI adoption by more than half the large companies surveyed. At the same time, a similar number of respondents indicated that AIs greatest impact is in helping employees to get better at their jobs.

Whether it is an improved customer experience or better employee experience, there is a direct correlation between AI adoption and superior business outcomes.

Early adopters report an improvement of almost 25 percent in customer experience, accelerated rates of innovation, higher competitiveness, higher margins, and better employee experience with the roll out of AI solutions.

Organizations worldwide are adopting AI in their business transformation journey, not just because they can but because they must to be agile, resilient, innovative, and able to scale, said Ritu Jyoti, program vice president, Artificial Intelligence Strategies.

While there is considerable agreement on the benefits of AI, there is some divergence in how companies deploy AI solutions. IT automation, intelligent task/process automation, automated threat analysis and investigation, supply and logistics, automated customer service agents, and automated human resources are the top use cases where AI is being currently employed.

While automated customer services agents and automated human resources are a priority for larger companies (5000+ employees), IT automation is the priority for smaller and medium sized companies (less than 1000 employees).

Despite the benefits, deploying AI continues to present challenges, particularly with regard to data. Lack of adequate volumes and quality of training data remains a significant development challenge. Data security, governance, performance, and latency (transfer rate) are the top data integration challenges.

Solution price, performance and scale are the top data management issues. And enterprises report cost of the solution to be the number one challenge for implementing AI. As enterprises scale up their efforts, fragmented pricing across different services and pay-as-you-go pricing may present barriers to AI adoption.

An AI-ready data architecture, MLOps, and trustworthy AI are critical for realizing AI and Machine Learning at scale, added Jyoti.

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There is a direct correlation between AI adoption and superior business outcomes - Help Net Security

What Is Machine Learning?

Machine learning is theconcept that a computer program can learn and adapt to new data without human interference. Machine learning is a field of artificial intelligence (AI) that keeps a computers built-in algorithms current regardless of changes in the worldwide economy.

Various sectors of the economy are dealing with huge amounts of data available in different formats from disparate sources. The enormous amount of data, known as big data, is becoming easily available and accessible due to the progressive use of technology. Companies and governments realize the huge insights that can be gained from tapping into big data but lack the resources and time required to comb through its wealth of information. As such, artificial intelligence measures are being employed by different industries to gather, process, communicate, and share useful information from data sets. One method of AI that is increasingly utilized for big data processing is machine learning.

The various data applications of machine learning are formed through a complex algorithm or source code built into the machine or computer. This programming code creates a model that identifies the data and builds predictions around the data it identifies. The model uses parameters built in the algorithm to form patterns for its decision-making process. When new or additional data becomes available, the algorithm automatically adjusts the parameters to check for a pattern change, if any. However, the model shouldnt change.

Machine learning is used in different sectors for various reasons. Trading systems can be calibrated to identify new investment opportunities. Marketing and e-commerce platforms can be tuned to provide accurate and personalized recommendations to their users based on the users internet search history or previous transactions. Lending institutions can incorporate machine learning to predict bad loans and build a credit risk model. Information hubs can use machine learning to cover huge amounts of news stories from all corners of the world. Banks can create fraud detection tools from machine learning techniques. The incorporation of machine learning in the digital-savvy era is endless as businesses and governments become more aware of the opportunities that big data presents.

How machine learning works can be better explained by an illustration in the financial world. Traditionally, investment players in the securities market like financial researchers, analysts, asset managers, individual investors scour through a lot of information from different companies around the world to make profitable investment decisions. However, some pertinent information may not be widely publicized by the media and may be privy to only a select few who have the advantage of being employees of the company or residents of the country where the information stems from. In addition, theres only so much information humans can collect and process within a given time frame. This is where machine learning comes in.

An asset management firm may employ machine learning in its investment analysis and research area. Say the asset manager only invests in mining stocks. The model built into the system scans the web and collects all types of news events from businesses, industries, cities, and countries, and this information gathered makes up the data set. The asset managers and researchers of the firm would not have been able to get the information in the data set using their human powers and intellects. The parameters built alongside the model extracts only data about mining companies, regulatory policies on the exploration sector, and political events in select countries from the data set. Saya mining company XYZ just discovered a diamond mine in a small town in South Africa, the machine learning app would highlight this as relevant data. The model could then use an analytics tool called predictive analytics to make predictions on whether the mining industry will be profitable for a time period, or which mining stocks are likely to increase in value at a certain time. This information is relayed to the asset manager to analyze and make a decision for his portfolio. The asset manager may make a decision to invest millions of dollars into XYZ stock.

In the wake of an unfavorable event, such as South African miners going on strike, the computer algorithm adjusts its parameters automatically to create a new pattern. This way, the computational model built into the machine stays current even with changes in world events and without needing a human to tweak its code to reflect the changes. Because the asset manager received this new data on time, they are able to limit his losses by exiting the stock.

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Machine Learning Definition - Investopedia

What is Machine Learning?

Machine Learning is a system that can learn from example through self-improvement and without being explicitly coded by programmer. The breakthrough comes with the idea that a machine can singularly learn from the data (i.e., example) to produce accurate results.

Machine learning combines data with statistical tools to predict an output. This output is then used by corporate to makes actionable insights. Machine learning is closely related to data mining and Bayesian predictive modeling. The machine receives data as input, use an algorithm to formulate answers.

A typical machine learning tasks are to provide a recommendation. For those who have a Netflix account, all recommendations of movies or series are based on the user's historical data. Tech companies are using unsupervised learning to improve the user experience with personalizing recommendation.

Machine learning is also used for a variety of task like fraud detection, predictive maintenance, portfolio optimization, automatize task and so on.

In this basic tutorial, you will learn-

Traditional programming differs significantly from machine learning. In traditional programming, a programmer code all the rules in consultation with an expert in the industry for which software is being developed. Each rule is based on a logical foundation; the machine will execute an output following the logical statement. When the system grows complex, more rules need to be written. It can quickly become unsustainable to maintain.

Machine learning is supposed to overcome this issue. The machine learns how the input and output data are correlated and it writes a rule. The programmers do not need to write new rules each time there is new data. The algorithms adapt in response to new data and experiences to improve efficacy over time.

Machine learning is the brain where all the learning takes place. The way the machine learns is similar to the human being. Humans learn from experience. The more we know, the more easily we can predict. By analogy, when we face an unknown situation, the likelihood of success is lower than the known situation. Machines are trained the same. To make an accurate prediction, the machine sees an example. When we give the machine a similar example, it can figure out the outcome. However, like a human, if its feed a previously unseen example, the machine has difficulties to predict.

The core objective of machine learning is the learning and inference. First of all, the machine learns through the discovery of patterns. This discovery is made thanks to the data. One crucial part of the data scientist is to choose carefully which data to provide to the machine. The list of attributes used to solve a problem is called a feature vector. You can think of a feature vector as a subset of data that is used to tackle a problem.

The machine uses some fancy algorithms to simplify the reality and transform this discovery into a model. Therefore, the learning stage is used to describe the data and summarize it into a model.

For instance, the machine is trying to understand the relationship between the wage of an individual and the likelihood to go to a fancy restaurant. It turns out the machine finds a positive relationship between wage and going to a high-end restaurant: This is the model

When the model is built, it is possible to test how powerful it is on never-seen-before data. The new data are transformed into a features vector, go through the model and give a prediction. This is all the beautiful part of machine learning. There is no need to update the rules or train again the model. You can use the model previously trained to make inference on new data.

The life of Machine Learning programs is straightforward and can be summarized in the following points:

Once the algorithm gets good at drawing the right conclusions, it applies that knowledge to new sets of data.

Machine learning can be grouped into two broad learning tasks: Supervised and Unsupervised. There are many other algorithms

An algorithm uses training data and feedback from humans to learn the relationship of given inputs to a given output. For instance, a practitioner can use marketing expense and weather forecast as input data to predict the sales of cans.

You can use supervised learning when the output data is known. The algorithm will predict new data.

There are two categories of supervised learning:

Imagine you want to predict the gender of a customer for a commercial. You will start gathering data on the height, weight, job, salary, purchasing basket, etc. from your customer database. You know the gender of each of your customer, it can only be male or female. The objective of the classifier will be to assign a probability of being a male or a female (i.e., the label) based on the information (i.e., features you have collected). When the model learned how to recognize male or female, you can use new data to make a prediction. For instance, you just got new information from an unknown customer, and you want to know if it is a male or female. If the classifier predicts male = 70%, it means the algorithm is sure at 70% that this customer is a male, and 30% it is a female.

The label can be of two or more classes. The above example has only two classes, but if a classifier needs to predict object, it has dozens of classes (e.g., glass, table, shoes, etc. each object represents a class)

When the output is a continuous value, the task is a regression. For instance, a financial analyst may need to forecast the value of a stock based on a range of feature like equity, previous stock performances, macroeconomics index. The system will be trained to estimate the price of the stocks with the lowest possible error.

In unsupervised learning, an algorithm explores input data without being given an explicit output variable (e.g., explores customer demographic data to identify patterns)

You can use it when you do not know how to classify the data, and you want the algorithm to find patterns and classify the data for you

Type

K-means clustering

Puts data into some groups (k) that each contains data with similar characteristics (as determined by the model, not in advance by humans)

Clustering

Gaussian mixture model

A generalization of k-means clustering that provides more flexibility in the size and shape of groups (clusters

Clustering

Hierarchical clustering

Splits clusters along a hierarchical tree to form a classification system.

Can be used for Cluster loyalty-card customer

Clustering

Recommender system

Help to define the relevant data for making a recommendation.

Clustering

PCA/T-SNE

Mostly used to decrease the dimensionality of the data. The algorithms reduce the number of features to 3 or 4 vectors with the highest variances.

Dimension Reduction

There are plenty of machine learning algorithms. The choice of the algorithm is based on the objective.

In the example below, the task is to predict the type of flower among the three varieties. The predictions are based on the length and the width of the petal. The picture depicts the results of ten different algorithms. The picture on the top left is the dataset. The data is classified into three categories: red, light blue and dark blue. There are some groupings. For instance, from the second image, everything in the upper left belongs to the red category, in the middle part, there is a mixture of uncertainty and light blue while the bottom corresponds to the dark category. The other images show different algorithms and how they try to classified the data.

The primary challenge of machine learning is the lack of data or the diversity in the dataset. A machine cannot learn if there is no data available. Besides, a dataset with a lack of diversity gives the machine a hard time. A machine needs to have heterogeneity to learn meaningful insight. It is rare that an algorithm can extract information when there are no or few variations. It is recommended to have at least 20 observations per group to help the machine learn. This constraint leads to poor evaluation and prediction.

Augmentation:

Automation:

Finance Industry

Government organization

Healthcare industry

Marketing

Example of application of Machine Learning in Supply Chain

Machine learning gives terrific results for visual pattern recognition, opening up many potential applications in physical inspection and maintenance across the entire supply chain network.

Unsupervised learning can quickly search for comparable patterns in the diverse dataset. In turn, the machine can perform quality inspection throughout the logistics hub, shipment with damage and wear.

For instance, IBM's Watson platform can determine shipping container damage. Watson combines visual and systems-based data to track, report and make recommendations in real-time.

In past year stock manager relies extensively on the primary method to evaluate and forecast the inventory. When combining big data and machine learning, better forecasting techniques have been implemented (an improvement of 20 to 30 % over traditional forecasting tools). In term of sales, it means an increase of 2 to 3 % due to the potential reduction in inventory costs.

Example of Machine Learning Google Car

For example, everybody knows the Google car. The car is full of lasers on the roof which are telling it where it is regarding the surrounding area. It has radar in the front, which is informing the car of the speed and motion of all the cars around it. It uses all of that data to figure out not only how to drive the car but also to figure out and predict what potential drivers around the car are going to do. What's impressive is that the car is processing almost a gigabyte a second of data.

Machine learning is the best tool so far to analyze, understand and identify a pattern in the data. One of the main ideas behind machine learning is that the computer can be trained to automate tasks that would be exhaustive or impossible for a human being. The clear breach from the traditional analysis is that machine learning can take decisions with minimal human intervention.

Take the following example; a retail agent can estimate the price of a house based on his own experience and his knowledge of the market.

A machine can be trained to translate the knowledge of an expert into features. The features are all the characteristics of a house, neighborhood, economic environment, etc. that make the price difference. For the expert, it took him probably some years to master the art of estimate the price of a house. His expertise is getting better and better after each sale.

For the machine, it takes millions of data, (i.e., example) to master this art. At the very beginning of its learning, the machine makes a mistake, somehow like the junior salesman. Once the machine sees all the example, it got enough knowledge to make its estimation. At the same time, with incredible accuracy. The machine is also able to adjust its mistake accordingly.

Most of the big company have understood the value of machine learning and holding data. McKinsey have estimated that the value of analytics ranges from $9.5 trillion to $15.4 trillion while $5 to 7 trillion can be attributed to the most advanced AI techniques.

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Machine Learning Tutorial for Beginners - Guru99