What is the Future of Machine Learning?

By James Tredwell on February 2, 2022

Just a little while ago, machine learning itself was in the future. Now, it can be found in anything from baby food quality control to translating legal documents.   

So even thinking about where it’s going to go next is exciting. But, we’re getting ahead of ourselves.

Machine learning is a branch of artificial intelligence focused on developing computer algorithms that allow a machine to learn from data without being programmed to do so. 

It is a subset in the sphere of computer science and engineering, and it has been gaining traction in recent years. The machine uses various inputs, including images, audio, and text, to train itself to learn how to make decisions by analyzing patterns.

In this article, we will try and figure out the next steps in its evolution.

Why is Machine Learning Important? 

Machine learning has its own purely practical benefits without going into the actual significance of humanity getting closer and closer to creating an authentic, true-blue artificial intelligence that can think for itself. 

One of the main reasons machine learning is meaningful in that it provides businesses an opportunity to serve their customers better. Machine learning algorithms can improve the success rates for PPC ads and SEO strategies, as can entire manufacturing processes in practically every industry.

Machine learning also provides an opportunity for businesses to save money by leveraging pre-existing data sets and algorithms rather than building new ones from scratch. This reduces the cost and time associated with developing new programs and systems and preventing errors in the process.

Many companies hope to make relevant and timely predictions about potential company outcomes (i.e., sales) and customer behaviors (i.e., purchase behavior) through machine learning. 

For example, companies may use machine learning techniques to analyze customer transaction histories to predict future purchases or identify fraudulent transactions before they’re completed.

Further Improvements in Automation

Using this technology, machines can perform tasks or make decisions with minimal input from humans. For example, an autonomous vehicle could use machine learning when it encounters an obstacle in its path to identify what type of object it is facing and how best to navigate around it. This technology has endless possibilities in terms of automation across various industries.

These automated machines will help keep things moving when there are too many tasks to keep up with at once, freeing up human resources for other tasks where their expertise would be more valuable. This automation will significantly impact the workforce as these machines take over jobs traditionally held by people.

Furthermore, this democratization of AI and ML apps will stand out. The introduction of commercial applications, like Wizata manufacturing software, speeds up almost every aspect of a factory process. They make applying machine learning and AI work in a factory easier. 

Essentially, machine learning will continue to improve and advance automation. 

The Rise of Quantum Computing

Now let’s go into something a bit more abstract. Quantum computing algorithms are becoming more prevalent, and machine learning utilizing these can have its potential significantly increased.

Quantum computing algorithms lead to greater computing capacity without going into too much detail that we barely understand. When adequately integrated, it helps computers process data much faster and analyzes it more quickly, leading to more complex and more insightful results and predictions.

The best part is that we still don’t know the potential of these algorithms. The second best part – machine learning boosted by quantum computing can help us advance more.

Unsupervised Algorithms

Continuing from the previous point, let’s talk about unsupervised algorithms for a moment.

What makes machine learning so good is that you leave the machine alone to “think”. More precisely, it analyzes data and finds patterns that a team of MIT scientists would never notice. As a result, we can expect greater “thinking”, stronger advancements, and better algorithms along with quantum computing.

Stronger Cognitive Services

Now, we have cognitive resources. So, what are they? These are sets of machine learning algorithms that can be used publicly by different developers and coders. Their benefits go far and wide, one of them allowing applications and devices to become more responsive. 

These services, being released to the public by giants like Microsoft, help include specific, more advanced features into regular apps. For example, developers can put things like speech detection, or visual recognition, into their apps, all using machine learning.

Greater Personalization

On the marketing level, personalization is key. More direct, more personalized, and custom-made recommendations to potential customers can yield great results for marketers. They can expect greater conversion rates, greater profits, and more significant website traffic with a good, targeted recommendation.

You guessed it right; machine learning can also improve this aspect of marketing. Machine learning algorithms can help gather more information, read patterns, analyze them, figure out what people want, and how to give it to them.

For example, a company can use ML to get some information on the browsing activities of its target audience. It can gather a tone of data in this manner and then learn how to use it to its advantage.

Healthcare and Big Pharma

The data gathered and analyzed by machine learning algorithms can be used to predict and prevent the occurrence of diseases. This can happen on an individual or a communal level.

Usually, treating or predicting diseases takes in a couple of factors, such as weight, age, height, geographic location. In any case, with machine learning, there is no limit to the number of variables we can introduce. Sure, most of them won’t be that useful, but we might just hit gold with some of them.


With the rate of innovation, it’s hard to predict what’s coming next. But there are some things we know for sure.

Machine learning is here to stay. It has already proven itself to be an invaluable tool for many different fields, and the potential for machine learning to improve our lives is limitless.

Machine learning will change the way we work, live, and interact with each other. The world is changing fast, and it’s exciting to think about the possibilities of tomorrow!

Author’s Bio

Christopher is a digital marketing specialist, Project Manager and Head Editor at Find Digital Agency and a passionate blogger. He is focused on new web tech trends and digital voice distribution across different channels. In his free time Christopher plays drums and Magic: the Gathering. 

How is Machine Learning impacting the Industrial Growth

By James Tredwell on June 16, 2020

Ever since the first industrial revolution, automation has played an important role in industrial growth. From assembly lines to robotic arms, industries have seen automation in various forms and types. Although leaving everything to the machine in a manufacturing process is still not a realized dream, machine learning does hold the promise of seeing that someday. Probably soon.

Industrial Automation means using intelligent machines to carry out individual tasks without any or minimal human intervention. This decreases the time it takes for completing a task while reducing the risks from human error and reducing manual labor that significantly reduces the cost of production while increasing the production itself.

This transformation results in a significant increase in the revenue directly impacting the growth of a company and the entire industry. The mechanical automation of the late 20th century bore results that were neither expected nor imagined.

However, mechanical automation came to a halt after a peak in its development and although some improvements were being made, nothing of great value seemed to emerge. This was during the beginning of the Internet Age and there was a boom in computing. No one would have expected the kind of impact that computing would have on industrial growth shortly.

The Rise of Machine Learning

Machine learning is a field of computer science that engages statistical techniques to enable computer systems with the ability to learn by themselves. Today’s Machine Learning algorithms are so advanced that they can overcome static program instructions to make data-based predictions that empower companies with decisions without human intervention.

In a survey conducted by O’Reilly Media for MemSQL, 61% of the respondents said that Machine Learning is their companies’ most important data initiative for the year and 74% of them said that Machine Learning would have a significant impact on the industry.

The global spending on AI systems is expected to maintain a strong trajectory as businesses continue to invest in AI-led projects through various software and platforms. According to the IDC Worldwide Artificial Intelligence Systems Spending Guide, the spending of AI systems will reach $97.9 Billion in 2013 which is significantly higher than the spending in 2019 ($37.5 Billion). The forecast for the compound annual growth rate for the 2018-2023 period is going to be 28.4%.

How Machine Learning is impacting Industrial Production

With Machine Learning, industrial production can be made faster and more reliable at reduced costs. This is done by feeding into the system enormous amounts of historical and statistical data that is then collated, analyzed, and used to develop knowledge about the improvement in the production process. It is not a plug-and-play system but rather a detailed and tedious process that takes time but proves worth every second.

The increased speed to market and reduced costs helps companies remain competitive in the market and keep delivering what customers expect and more. Machine Learning can also be used to study and understand customer behavior to understand the needs and wants of the customers and make changes in the production accordingly.

Process-Based Machine Learning

In a process-based machine learning system, the data on how a production line works are sent to the system to be analyzed. This helps the system understand what to expect from the production process. The system then uses this data to teach its algorithm on what to expect from the machines they are monitoring to obtain training data, relying on pattern recognition and inference for developing the ability to make decisions and prediction without programming the machine to do the task.

Some of the ways Machine Learning is transforming the industrial production are:

  • Predictive Maintenance

The ability to predict disruption in production in time to take necessary measures helps in reducing risks and improving on the production for maximum results. It allows scheduling of downtime to eliminate the loss of profit, customer base, and data.

  • Network and Security Convergence

If the network is not reliable and comes to a halt due to some disturbance, the production stops. With Machine Learning, network and security are handled simultaneously by monitoring both departments at close quarters.

  • Smart Manufacturing Digital Design

Artificial intelligence and Machine Learning enables the development of a digital twin to the production floor that helps design and monitor the production more efficiently. Using the systems algorithm and data collected from various sources scenarios are created to train crisis handling.

Six Ways Machine Learning is impacting the Industrial Growth

  1. Data Analytics

Through the technologies based on Artificial Intelligence, Cognitive Computing, and Machine Learning, Data Analytics have given new life to actionable insights. The recent push to data-driven tasks and automation has helped move towards an effective product but the major issue is still BigData storage and silos. The computing abilities and data management in the manufacturing and production sectors have taken prime importance.

  1. Cloud Computing

The ability to access and store data on the cloud rather than a hard drive helps companies save enormous amounts of storage space and equipment costs. Cloud Computing helps data exchange over the internet rather than physical servers or storage systems that brings ease and convenience to storage for an effective industrial process.

  1. Smart Transport Systems

Smart transport systems have disrupted the automobile industry across the world and their impact on Industrial Growth is quite significant. The industry is forecasted to have a greater market share in the coming years as predictions have placed its value at $149.21 billion by 2023 at a CAGR of 14.7% for 2018-2023.

  1. Smart Processes Application

Applications that use computer intelligence to draw important information about business processes are known as Smart Process Applications. Across the industry, such apps are either up and functioning or are in an experimental stage. In both these cases, the results are extremely promising. These smart process applications can integrate machine learning techniques with process workflows to make decisions for improving production. These apps help speed up customer responses and quicker end-to-end processes.

  1. Smart Manufacturing

Monitoring systems that enhance the controlling and managing capabilities of a production system are part of cloud-based machine learning techniques for smart manufacturing. This system also employs the Internet of Things to collect data through sensors embedded across the production process to check operational status and performance for raising red flags on time and taking necessary actions promptly.

  1. Smart Supply Chain

A smart supply chain management system considers several aspects of end-to-end logistics management through automation and other emerging technologies like cyber-physical systems, IoT, Data Analytics, and Cloud. This automation across the supply chain helps is identifying key differentiators and making data-driven decisions to improve the efficiency and effectiveness of the supply chain.

As industries in all sectors move towards digital transformation and there’s an earnestness in companies to digitize their production and manufacturing, there’s a huge demand for effective and time-friendly digital transformation strategies that focus on the key factors of digitization to bring about a faster, more efficient digital transformation.

In addition to the core technologies, strong leadership, engaging work culture, and value chain contribute equally to for the most beneficial digitization, leading the industries towards a higher than expected curve on the growth graph.

Author Bio :- Lily James is a technological enthusiast from Cincinnati who writes for Narwal Inc. She is passionate about Digital Transformation for businesses and keeps a watchful eye on industry trends.

How AI, Big Data and Machine Learning Are Shaping the Energy Industry

By James Tredwell on August 1, 2019

Machine learning has been impacting a vast variety of business sectors in the last couple of years, ranging from finance (with the Fintech boom) to eCommerce and many more.

Energy has also been another big part of what’s known today as the “machine learning revolution”, both from an architectural setup to the execution of risky tasks in the nuclear field, for example.

Machine learning and data science have been a considerably big focus in an industry which still amounts for billions of dollars every year, worldwide. Let’s analyse, therefore, what’s the impact of these technologies on this very sector.

Automation and The AoT

The so-called “Automation of Things” has become quite a big obsession in the warehousing business, after Alibaba made public that, by simply fully automating their whole supply chain in 2018, they were able to increase their productivity by over 70%.

Warehousing principles are applied to the energy sector as well, with dozens of Python-based tools being installed in control rooms to automatically keep control of the entire facility, its power and “metabolism”.

Automation in complex energy facilities such as the nuclear ones is a matter of security as well, given the fact that it decreases the chances of human errors which, in case, would cause major devastation.

The Evolution Of The Matter In The Next Couple Of Years

Machine learning is stated at over $2 billion in value and it’s definitely very likely to increase even more. Just by covering its applications in regards to what is going on for the architectural setup of machine learning and deep learning,

we can safely say that this niche part of the industry will grow both from a technological perspective and, most importantly, from a business one. Energy-related startups, following a Forbes study, have grown by at least 24% in 2018, leading to:

a). More job-related positions on the matter: the figure of the “automation engineer” has, in fact, grown visibly and tangibly in the past couple of years, peaking at over 15% of the entire engineer market, in terms of job positions and availability in terms of resources from companies.

b). More investment influxes in the next couple of years. With examples like Currant and Raptor, who went through millions of dollars in investments (already repaid), it’s safe to confirm how this subject will attract more tech moguls and investors in the nearest future.

How Does Big Data Work With Energy?

If there’s one thing which combines the energy sector and big data, that would definitely be marketing. All the biggest and most successful companies in the sector have been using data in order to properly target their potential customers.

From a marketing perspective, the energy sector basically wants to provide plans which are targeting every potential customer differently. With examples like the recent Cambridge Analytica scandal, although, this very matter has been highly questioned in the recent past.

Luckily, GDPR in Europe has stated some cardinal rules for what concerns data acquisition, especially in regards to big data and data points.

An Example Of An “Automated Power Plant”

It may sound like science fiction, but automated power plants which are working with heavy machine learning tools have become reality in the recent past. Dissecting their workflow, we were able to understand how and which algorithms are doing specific bits:

a). The main architecture is coded in either C++ or Java and represents the “muscles” of the automation process. By telling which specific parts are moving in a power plant, this saves a lot of time in communications and execution.

b). The “brain” of these tools is usually made by a combination of Python and R. Guidelines are given to the Python tool which then processes all the variables via generally R coded algorithms.

This is a very simplified version of an automated control centre in power plants, which escalates to tens of these algorithms in complex architectures such as nuclear power plants, for example.

Will This Be a “Problem” For Us Humans?

Technically, every single time a machine learning-related matter approaches the “mainstream” market, someone comes out saying that “this will be the bane of our jobs”.

Realistically speaking, though, the application of these tools to every single energy-related facility will most likely happen in 2060, therefore there is no reason to panic. Plus, surveillance will still be required to check if everything is properly into place and, most importantly, these tools must be guided and programmed, in the first place.

With this being said, there is absolutely no reason to panic. Power plants and every single energy-related architecture will still require some sort of human guidelines.

The Mobile World: Remote Control For Workers

In a recent study, it has been pointed out that some app developers in the UK have been working on mobile apps which were capable of controlling warehousing and management tasks straight from their smartphones.

In warehousing management, in fact, the usage of remote controls has become quite normal since 2015, therefore there are no reasons why this shouldn’t apply as well to power plant controls or anything else in between, especially if machine learning related, where variables and guidelines are interchangeable anytime.

Connection and Blockchain, An “Almost Tangible” Hypothesis

A big problem between power stations has always been the fact that communications could have been quite slow. Imagine if all the above-mentioned tools could be able to communicate between them instantly by relying on a simplified blockchain-based structure.

This could be quite game-changing and will, if properly set up, save a lot of errors, dangers whilst, at the same time, improving the station’s productivity. The evolution of this very matter will actively set the foundations in regards to the entire technological evolution of blockchain being applied to both warehousing communications and management.

An Overall Picture Of Energy, Machine Learning And AI In 2019

With everything that has been said above, it’s important to understand how the application of machine learning to the energy sector will set the future for other business and supplying sectors in the nearest future.

It’s no secret that robots will dominate the warehousing world in the nearest future and, therefore, it’s important to monitor everything from both a technological and business perspective.

At the minute, this very technology is being put into place and has reached the stability needed for it to become something which could be considered as “an industry-standard”.

To Conclude

Energy as a whole has always been a sector which has embraced new technologies since its very beginning. The “nuclear revolution” has been the first big implementation of new pieces of technology in this sector and machine learning/ML and everything in between will definitely be the second big industrial revolution in this very sector.

With all the examples mentioned above and after a proper analysis of how these tools operate, it’s definitely safe to say that automated features and big data will definitely become extremely big in this sector.

This is definitely an exciting time for every robotics student, data science and architectural technology engineer who’s looking for a career change.

This article is contributed by Alice Porter has experience as an Alexa App Developer and is now a freelance tech writer.

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Trending application of machine learning 2019

By James Tredwell on May 21, 2019

Did you know? All coming activities will be done, with the help of AI either other applications. Machine learning technology doesn’t need to be introduced here; it has made easy the things in daily life. Machine learning is using Artificial Intelligence, making the system to predict the automatically results without the intervention of human beings.

Machine Learning Algorithms

Supervised Machine Learning algorithm, they are using both input and output and then the algorithm is used in order to predict the output result. In unsupervised machine learning, only input variable is available instead of an output variable. In Unsupervised Machine learning, data is divided into classes so, get more significant results.

Whatever the actions we’re opt, by using smart technology that’s Machine Learning, we don’t even know what activities are under the shadow of Machine learning. Thus, Machine learning is used in a lot of sectors whether it is in private life or other industries. Thus, let’s focus once various sectors of the software of Machine learning.

Machine Learning technology can protect the companies which are handling finance, from fiscal fraud which might happen in the future. Apart from that, machine learning can help to predict the upcoming opportunities that could be executed for additional investments.

Cyber surveillance will help to protect those institutions that are more under the shadow of financial risk and is able to take action so that particular fraud could be stopped. Thus, it is needed to measure inside the doors of machine learning companies as soon as possible in order to protect the fund related problems.

On hearing the name of Assistants, the very first thing strikes our thoughts are that, candidates are those who help to guide and assist for a particular direction and here we are speaking about the Machine learning established personal assistants who on the basis of the preceding setting pick our upcoming actions through best devices.

Siri, Alexa, Google now are a few of the examples of Virtual Private Assistants that help in assisting information, you merely need to inquire through voice and you will find the result instantly in line with this search. Machine learning based virtual assistants such as Amazon’s Alexa, Google Assistant, and Apple’s Siri who are operating on our smart speakers and speakers are making our day to day life easier and entertaining.

Role of Alexa assistants individually

In Alexa, you have to decide on a routine up and whenever you state “Alexa, good morning,” the lights of your room will turn on automatically along with your favorite playlist would start playing itself because this Alexa, a digital assistant run on smart speakers.

Let’s understand how Siri, which is a smart device of this iPhone, may entertain your day to day life. Well, if you say “Siri, I am going home”. This will open the navigation management in addition to sends the text message into your family at the exact same time. Yes! We cannot deny that Machine Learning is the foundation of those private assistants as they get input and supplies the output in the type of the result according to your requirements.

Machine Learning Program in Marketing and Sales

Marketing and earnings on the basis of machine learning technologies are such a wonderful approach to keep the clients always in touch in order to buy your merchandise. Well, how can this be possible? Simple! With the support of Machine learning technology, you’d have the ability to examine the purchase background of the clients and would suggest those goods in the recommendations in order to generate the customers buy it for the next time.

So, it could be said that, it is told before that Machine learning technology forecasts future events on the basis of earlier participation, similarly in the case of advertising and sales, it is must say that, on the grounds of previous captured client’s likings, it boosts future sales and promotion.

Machine Learning Program in Predictions while traveling

Well, you know that, everybody travels with the help of GPS navigation. Machine learning technology here predicts the coming visitors on the way for that time being linked together with the GPS, your current place and velocities are being connected with central host of managing traffic.

How Machine learning impact on traveling?

You may have booked cab on the internet, and you have noticed that it automatically shows you the estimated cost of the ride. So this is all because of Machine learning. At times it also happens when you select the option of “sharing ride”, it automatically reduces the price of the ride. This is happening with the wisdom of machine learning technology.

Machine learning is playing a significant part in the medical industry too. Sensors that are fixed in the wearable of the individual so as to provide advice regarding the individual’s condition, heartbeat, blood pressure, etc.

The information that’s gathered through the detectors could assist the doctors in assessing the health and condition of an individual. Doctors can forecast the approaching health issues that may fret about the sufferers and if, if you’re running a healthcare department, do consult a good computer software development company in India which may assist you in various manners in order to maintain decent relation with your physician.

Machine Learning Application in Social Media Services

How entertaining and colorful your social media has become? No matter the item is wandering in mind, social media begins flashing the advertisements of that specific interest.

So, this is about that Social Media has connected with Machine learning so as to make your social presence beneficial and knowledgeable.

Let us see the impact of Machine learning on Facebook

Here is a really simple concept where Machine learning is dominating the popular program Facebook. How? Well, actually by indicating the various friend suggestions. On the basis of experience, Facebook keeps noticing the buddies you may connect and the profiles that you have ever visited.

Another manner where Machine learning is working on Facebook and that is when you upload an image with some friend of yours- Facebook immediately admits the exceptional feature of that individual after going through your friend list.

Final thoughts

This is how Machine learning is always making your lives simpler and entertaining. In the above-given examples, you may have understood how Machine learning is helping to forecast your output in the shape of future activities.

As its well-known that machine learning is amazingly revolutionizing the world, there are various mobile app development firms in India which are providing the supply of building ML-based applications.

This article is contributed by Ashish Goyal, Digital Marketing Specialist at Xtreem Solution, a leading mobile app development company; you can hire app developer from our robust team.

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Is Python a Good Language for Machine Learning?

By James Tredwell on January 26, 2019

Nowadays, Python which is a general-purpose high-level programming language has been increasingly used in machine learning algorithms. It is usually favored for applications varying from web development to scripting and process automation.

Willing to start a small business or already working in an MNC, Python provides a huge list of benefits for all. The usage of Python cannot be limited to only one activity as its growing popularity has allowed it to enter some of the most popular and complex processes like Artificial Intelligence, Machine Learning, Data Science, Natural Language Processing and much more.

Machine learning is the technology to parse data by using algorithms and also make decisions based on it. It is a field of Artificial intelligence that uses statistical techniques for the ability to learn from data given by computer systems without being explicitly programmed.

What is Python?

Python is a high level, interpreted and an OOPs based interpreted programming language. It is considered as robust and highly useful being focused on rapid application development(RAD). It works well by connecting existing components together and because of easy leaning, it has become one of the fastest growing languages in terms of scalability and adaptability. Also, the broad support and constant evolving libraries make Python a better choice for any project be it WebApp, Mobile App, IoT, ML, Data Science or AI.

Is Python a good for Machine Learning?

The answer is definitely yes. What makes Python stand out is its exceptional qualities which undoubtedly allows developers to opt it in machine learning.

#1. Expansive selection of libraries and frameworks :- Python accords for a variety of specific libraries used for scientific computation and data analysis. These libraries have been upgraded continuously to reduce the constraints that developers faced before. Here are a few important ones from many libraries to perform data analysis.

#2. Matplotlib :- A 2D plotting library which offers data visualizations in the form of histograms, power spectra, bar charts, and scatter plots.

#3. NumPy :- To perform scientific computing by assorting high-level mathematical functions to operate on multi-dimensional arrays and matrices.

#4. Pandas :- Delivers data structures and operations to change numerical tables and time series which is developed on top of NumPy.

#5. SciPy :- Offers effective routines for numerical integration and up-gradation in association with NumPy arrays.

#6. Easy to Learn :- Python is considered as the popular data analysis tool which is ahead of SQL and SAS. The developers are rushing towards the Python for easy to learn and code by promoting an easy-to-understand syntax when compared other data science languages like R and leads to a shorter learning curve. To help data scientists and engineers work in a collaborative manner, a tool names Jupyter is used for writing code and text within a web page’s context.

#7. Scalable :- Python is best known for its simple, concise and readable coding properties. As machine learning relies on complex algorithms developers have to worry less about convolution of the coding. Python is faster to use than Matlab and Stata by emerging as a scalable language. It is also utilized by YouTube for flexible problem-solving situations.

#8. Less Code :- Machine Learning involves a lot of algorithms which provides ease of testing with the help of Python. It also helps in easy writing and execution of codes to implement the same logic with the same logic as other OOPs languages. Python’s interpretation approach enables to check code methodology.

#9. Platform-Oriented :- Python comes up with the flexibility to provide an API from its existing language which itself provides extreme flexibility and becomes platform independent. You can get your application running in a new OS or other platforms just by making a few changes in codes. This approach helps to save the developers time in testing on different platforms and gets rid of code migration.

#10. Boatload of support :- As python is an open source programming language, it has a wide number of developers who are ready out there to support and guide through it. Python comes with a huge community of developers with a host of resources which are available to speed up their tasks. There is a huge community of active coders who are willing to help programmers from their early stage of developing cycle.

Wrap Up

We can conclude Python as an easy, simple, powerful, and innovative language because of its broader usage in a variety of contexts which are not associated with data science. Understanding Python and using it as per its respective strengths can refine you as a machine learning programmer which should be versatile and stay at the top of every problem. In the end, it is concluded from the features that nothing suits exceptionally well to handle the complexity of machine learning as the Python does. The decision-making ability derived from machine learning and reduced complexity of Python makes a perfect match in heaven.

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