John David

John David

1560932841

Machine Learning vs Traditional Programming

Difference between Machine Learning and Traditional Programming

While some call AI and ML overhyped themes that are nothing more than if statements, or just programming stuff, I offer you to come face to face with all pieces of evidence to check this out. In this post, I will contrast these terms and also showcase the difference between the specialists who are involved in these two spheres: who are they? Software engineer, software developer, machine learning expert, data scientist…some people even use a programmer or coder, and some even go as far as a ninja, guru, or rock star! But, are they really the same? And if so, is there a line between Machine Learning and Traditional Programming?

ML vs Programming: First, What’s Machine Learning?

It’s easy to say that AI and ML are nothing more than if statements. Or what is more, it is just simple statistics. What else do we hear about it? Is ML just a new word to describe math + algorithms? Sometimes such simplifications seem to be funny, but obviously, ML is more complicated.

But let’s have a look at a more appropriate explanation.

So, in simple words, Artificial Intelligence is an umbrella that contains other realms like image processing, cognitive science, neural networks and much more. Machine Learning is also a component located under this umbrella. Its core idea is that the computer does not just use a pre-written algorithm, but learns how to solve the problem itself. Or, to explain it in other words, there is an excellent definition by Arthur Samuel (who actually had coined the term of ML):

Machine Learning is a field of study that gives computers the ability to learn without being explicitly programmed.
So, yes, ML teach a machine to solve various complex tasks that are difficult to be solved algorithmically. What are those tasks? Well, you probably have already stumble at them in practice. For example, it can be face recognition on your phone or voice understanding, driving a car (Google Self-Driving Car), diagnose diseases by symptoms (Watson), advise products, books (Amazon), movies (Netflix), music (Spotify), perform the functions of a personal assistant (Siri, Cortana)…this list can go on and on.

Okay, I hope it was clear enough and now it’s time to move on onto another important thing about ML.

Any working ML-technology can be conditionally attributed to one of three levels of accessibility. What does this mean? Well, the first level is when it is available exceptionally to major tech-giants like Google or IBM. The second level is when, let’s say, a student with a certain amount of knowledge can use it. And the last one, the third level of ML accessibility is when even a granny is able to cope with it.

What we have on the current stage of development is Machine learning at the junction of the second and third levels. Due to this, the rate of change of the world with the help of this technology grows with cosmic speed.

Last but the least thing about ML: most of the ML-tasks can be divided into learning with a teacher (supervised learning) and learning without a teacher (unsupervised learning). And if you imagine a programmer with a whip in one hand and a piece of sugar in the other, you are a little bit mistaken.

The name “teacher” means the idea of human intervention in data processing. When training with a teacher, which is supervised learning, we have the data, and we need to predict something on its basis. On the other hand, when teaching without a teacher, which is unsupervised learning, we again have the data, but here we need to find its properties.

ML vs Programming: Okay, Then How it differs from Programming?

In traditional programming you hard code the behavior of the program. In machine learning, you leave a lot of that to the machine to learn from data.
Consequently, these terms are no way interchangeable: data engineer can’t replace the work of traditional programming and vice versa. Although every data engineer is obliged to use at least one coding language, traditional programming is only a small part of what he (or she?) does. On the other hand, we can’t say software developer is using ML-algorithms to launch a website.

ML just like AI is not a substitution, but supplementation for traditional programming approaches. For instance, ML can be used to build predictive algorithms for an online trading platform, while the platform’s UI, data visualization and other elements will be performed in a mainstream programming language such as Ruby, or Java.

So, here is the main thing: ML is used in the case when traditional programming strategy falls behind and it is not enough to fully implement a certain task.

What does this mean in practice? Here is a great explanation on the basis of a classical ML-problem of exchange rate forecasting and two different ways to do it:

Traditional programming approach

For any solution, the first task is the creation of the most suitable algorithm and writing the code. Thereafter, it is mandatory to set the input parameters and, in fact, if an implemented algorithm is ok it will produce the expected result.

How a software developer creates a solution

However, when we need to predict something, we need to use an algorithm with a variety of input parameters. In case of prediction of the exchange rate, it’s mandatory to add such details like yesterday’s rate; external and internal economic changes in the country that issues the currency and more.

Consequently, we handcraft a solution that is able to accept a set of parameters and, based on the input data, predict a new exchange rate.

But it’s extremely important to add one more thing or to be more clear one problem of such an approach. So what is it?

Well, it’s simple, we need to add a thousand and hundreds of parameters, whereas their limited set allows building a very basic and unscalable model. So yes, for any person is troublesome to work with such massive data arrays.

Then we have a slightly different Machine learning approach for this task, so what is it?

To solve the same problem using ML-methods, data engineers use a totally different procedure. Instead of developing an algorithm on its own, they need to collect an array of historical data that will be used for semi-automatic model building.

Following managing a satisfactory set of data, the data engineer loads it into already tailored ML-algorithms. The result is a model that can predict a new result, receiving new data as input.

How a data engineer develops a solution using machine learning

A distinctive feature of ML is there is no need to build a model. This complicated yet meaningful responsibility is executed by ML-algorithms. And ML expert will only add just a minor edit to this.

Another significant difference between ML and Programming is determined by the number of input parameters that the model is capable of processing. For an accurate prediction, you have to add thousands of parameters and do it with high accuracy, as every bit will affect the final result. A human being a priori cannot build an algorithm that will use all of those details in a reasonable way.

However, for ML, there are no such restrictions. As long as you have enough processor power and memory, you can use as many input parameters as you see fit. Undoubtedly, this fact makes ML be so powerful and widespread nowadays.

Summing it up: ML expert, Data scientist, programmer, and software engineer…who is who?

According to Wiki,** Data Science*** is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.*
So far it does not sound very cool.

But then there is something interesting:

use the most powerful hardware, the most powerful programming systems, and the most efficient algorithms to solve problems.
And then even more interesting part:
In 2012, Harvard Business Review called it The Sexiest Job of the 21st Century.
So, Data Science is another extensive umbrella, just like Computer Science, only Data Science aimed at processing data and extracting useful information from them.

What about programming? Data scientists nowadays do it exceptionally in the interest of research. They are not only programmers, but they are also usually supposed to have an applied statistics or research background. Some also do software engineering, especially at companies serving data science/ML in their products. The most interesting thing is that Data Scientist is not obliged to be able to program well, but can be limited to tools like Matlab, SPSS, SAS, etc.

What then is the position of Machine Learning Engineer?

The position of the Machine Learning Engineer is more “technical”. In other words, ML Engineer has more in common with classic Software Engineering than Data Scientist.

The standard tasks of ML Engineer are generally similar to Data Scientist. You also need to be able to work with data, experiment with different machine learning algorithms that will solve the problem, create prototypes and ready-made solutions.

Of the key differences, I would highlight:

  • Strong programming skills in one or more languages (usually Python);
  • Less emphasis on the ability to work in data analysis environments, but more emphasis on Machine Learning algorithms;
  • The ability to use in the application ready-made libraries for different stacks, for example, NumPy / SciPy for Python;
  • The ability to create distributed applications using Hadoop and more.

And now let’s move back to the programming and have a closer look at what tasks are assigned to the programmer.

A programmer is actually someone like a data analyst or business systems developer. They don’t have to build systems themselves, they just write a loosely structured code against existing systems. So, yes, we can call data science a new wave of programming, but coding is only a small part of it. So, don’t be mistaken.

But if digging deeper, we will found out there are other terms like **Software Engineer **and Software Developer, and both of them are also not similar. For example, software engineers have to engineer things. They deal with production applications, distributed systems, concurrency, build systems, microservices. And just for the record, a software developer needs to understand all the cycles of software development, not just implementation (which sometimes won’t even need any programming or coding).

So, programming and machine learning…do you feel the difference now? I hope this post helped you to avoid confusion around those terms. Undoubtedly, all of them have something in common which is technology, but the number of their differences is much bigger. Thus, ml-engineer, software engineer and software developer are completely not interchangeable.

#machine-learning #data-science #artificial-intelligence

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Machine Learning vs Traditional Programming

Noah Sykes

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sophia tondon

sophia tondon

1620898103

5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

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Visit Blog- https://www.xplace.com/article/8743

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Nora Joy

1604154094

Hire Machine Learning Developers in India

Hire machine learning developers in India ,DxMinds Technologies is the best product engineering company in India making innovative solutions using Machine learning and deep learning. We are among the best to hire machine learning experts in India work in different industry domains like Healthcare retail, banking and finance ,oil and gas, ecommerce, telecommunication ,FMCG, fashion etc.
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Maintenance / Support / Sustenance
Integration / Data Management
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Hire machine learning developers in India ,DxMinds Technologies is the best product engineering company in India making innovative solutions using Machine learning and deep learning. We are among the best to hire machine learning experts in India work in different industry domains like Healthcare retail, banking and finance ,oil and gas, ecommerce, telecommunication ,FMCG, fashion etc.

Services

Product Engineering & Development

Re-engineering

Maintenance / Support / Sustenance

Integration / Data Management

QA & Automation

Reach us 917483546629

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Nora Joy

1607006620

Applications of machine learning in different industry domains

Machine learning applications are a staple of modern business in this digital age as they allow them to perform tasks on a scale and scope previously impossible to accomplish.Businesses from different domains realize the importance of incorporating machine learning in business processes.Today this trending technology transforming almost every single industry ,business from different industry domains hire dedicated machine learning developers for skyrocket the business growth.Following are the applications of machine learning in different industry domains.

Transportation industry

Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

  • ML and AI can offer high security in the transportation industry.
  • It offers high reliability of their services or vehicles.
  • The adoption of this technology in the transportation industry can increase the efficiency of the service.
  • In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

  • Identifying Diseases and Diagnosis
  • Drug Discovery and Manufacturing
  • Medical Imaging Diagnosis
  • Personalized Medicine
  • Machine Learning-based Behavioral Modification
  • Smart Health Records
  • Clinical Trial and Research
  • Better Radiotherapy
  • Crowdsourced Data Collection
  • Outbreak Prediction

**
Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

  • Fraud prevention
  • Risk management
  • Investment predictions
  • Customer service
  • Digital assistants
  • Marketing
  • Network security
  • Loan underwriting
  • Algorithmic trading
  • Process automation
  • Document interpretation
  • Content creation
  • Trade settlements
  • Money-laundering prevention
  • Custom machine learning solutions

Education industry

Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.

Outsource your machine learning solution to India,India is the best outsourcing destination offering best in class high performing tasks at an affordable price.Business** hire dedicated machine learning developers in India for making your machine learning app idea into reality.
**
Future of machine learning

Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

  • Improved Unsupervised Algorithms
  • Increased Adoption of Quantum Computing
  • Enhanced Personalization
  • Improved Cognitive Services
  • Rise of Robots

**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

#hire machine learning developers in india #hire dedicated machine learning developers in india #hire machine learning programmers in india #hire machine learning programmers #hire dedicated machine learning developers #hire machine learning developers

Nora Joy

1607006620

Hire Machine Learning Developer | Hire ML Experts in India

Machine learning applications are a staple of modern business in this digital age as they allow them to perform tasks on a scale and scope previously impossible to accomplish.Businesses from different domains realize the importance of incorporating machine learning in business processes.Today this trending technology transforming almost every single industry ,business from different industry domains hire dedicated machine learning developers for skyrocket the business growth.Following are the applications of machine learning in different industry domains.

Transportation industry

Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

  • ML and AI can offer high security in the transportation industry.
  • It offers high reliability of their services or vehicles.
  • The adoption of this technology in the transportation industry can increase the efficiency of the service.
  • In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

  • Identifying Diseases and Diagnosis
  • Drug Discovery and Manufacturing
  • Medical Imaging Diagnosis
  • Personalized Medicine
  • Machine Learning-based Behavioral Modification
  • Smart Health Records
  • Clinical Trial and Research
  • Better Radiotherapy
  • Crowdsourced Data Collection
  • Outbreak Prediction

**
Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

  • Fraud prevention
  • Risk management
  • Investment predictions
  • Customer service
  • Digital assistants
  • Marketing
  • Network security
  • Loan underwriting
  • Algorithmic trading
  • Process automation
  • Document interpretation
  • Content creation
  • Trade settlements
  • Money-laundering prevention
  • Custom machine learning solutions

Education industry

Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.

Outsource your machine learning solution to India,India is the best outsourcing destination offering best in class high performing tasks at an affordable price.Business** hire dedicated machine learning developers in India for making your machine learning app idea into reality.
**
Future of machine learning

Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

  • Improved Unsupervised Algorithms
  • Increased Adoption of Quantum Computing
  • Enhanced Personalization
  • Improved Cognitive Services
  • Rise of Robots

**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

#hire machine learning developers in india #hire dedicated machine learning developers in india #hire machine learning programmers in india #hire machine learning programmers #hire dedicated machine learning developers #hire machine learning developers

Ananya Gupta

1595485129

Pros and Cons of Machine Learning Language

Amid all the promotion around Big Data, we continue hearing the expression “AI”. In addition to the fact that it offers a profitable vocation, it vows to tackle issues and advantage organizations by making expectations and helping them settle on better choices. In this blog, we will gain proficiency with the Advantages and Disadvantages of Machine Learning. As we will attempt to comprehend where to utilize it and where not to utilize Machine learning.

In this article, we discuss the Pros and Cons of Machine Learning.
Each coin has two faces, each face has its property and highlights. It’s an ideal opportunity to reveal the essence of ML. An extremely integral asset that holds the possibility to reform how things work.

Pros of Machine learning

  1. **Effectively recognizes patterns and examples **

AI can survey enormous volumes of information and find explicit patterns and examples that would not be evident to people. For example, for an online business site like Amazon, it serves to comprehend the perusing practices and buy chronicles of its clients to help oblige the correct items, arrangements, and updates pertinent to them. It utilizes the outcomes to uncover important promotions to them.

**Do you know the Applications of Machine Learning? **

  1. No human mediation required (mechanization)

With ML, you don’t have to keep an eye on the venture at all times. Since it implies enabling machines to learn, it lets them make forecasts and improve the calculations all alone. A typical case of this is hostile to infection programming projects; they figure out how to channel new dangers as they are perceived. ML is additionally acceptable at perceiving spam.

  1. **Constant Improvement **

As ML calculations gain understanding, they continue improving in precision and productivity. This lets them settle on better choices. Let’s assume you have to make a climate figure model. As the measure of information you have continues developing, your calculations figure out how to make increasingly exact expectations quicker.

  1. **Taking care of multi-dimensional and multi-assortment information **

AI calculations are acceptable at taking care of information that is multi-dimensional and multi-assortment, and they can do this in unique or unsure conditions. Key Difference Between Machine Learning and Artificial Intelligence

  1. **Wide Applications **

You could be an e-posterior or a social insurance supplier and make ML work for you. Where it applies, it holds the ability to help convey a considerably more close to home understanding to clients while additionally focusing on the correct clients.

**Cons of Machine Learning **

With every one of those points of interest to its effectiveness and ubiquity, Machine Learning isn’t great. The accompanying components serve to confine it:

1.** Information Acquisition**

AI requires monstrous informational indexes to prepare on, and these ought to be comprehensive/fair-minded, and of good quality. There can likewise be times where they should trust that new information will be created.

  1. **Time and Resources **

ML needs sufficient opportunity to allow the calculations to learn and grow enough to satisfy their motivation with a lot of precision and pertinence. It additionally needs monstrous assets to work. This can mean extra necessities of PC power for you.
**
Likewise, see the eventual fate of Machine Learning **

  1. **Understanding of Results **

Another significant test is the capacity to precisely decipher results produced by the calculations. You should likewise cautiously pick the calculations for your motivation.

  1. High mistake weakness

AI is self-governing yet exceptionally powerless to mistakes. Assume you train a calculation with informational indexes sufficiently little to not be comprehensive. You end up with one-sided expectations originating from a one-sided preparing set. This prompts unessential promotions being shown to clients. On account of ML, such botches can set off a chain of mistakes that can go undetected for extensive periods. What’s more, when they do get saw, it takes very some effort to perceive the wellspring of the issue, and significantly longer to address it.

**Conclusion: **

Subsequently, we have considered the Pros and Cons of Machine Learning. Likewise, this blog causes a person to comprehend why one needs to pick AI. While Machine Learning can be unimaginably ground-breaking when utilized in the correct manners and in the correct spots (where gigantic preparing informational indexes are accessible), it unquestionably isn’t for everybody. You may likewise prefer to peruse Deep Learning Vs Machine Learning.

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