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Intel keeps on eating up new businesses to work out its machine learning and AI tasks.

In the most recent move, TechCrunch has discovered that the chip goliath has obtained chúng tôi an Israeli organization that has manufactured and works a platform for information scientists to assemble and run machine learning models, which can be utilized to prepare and follow various models and run examinations on them, construct proposals and the sky is the limit from there.

Intel affirmed the securing to us with a short note. “We can affirm that we have procured Cnvrg,” a representative said.

Also read: Top 10 Trending Technologies You should know about it for Future Days

Intel isn’t uncovering any monetary terms of the arrangement, nor who from the startup will join Intel.

Cnvrg, helped to establish by Yochay Ettun (CEO) and Leah Forkosh Kolben, had raised $8 million from financial specialists that incorporate Hanaco Venture Capital and Jerusalem Venture Partners, and PitchBook gauges that it was esteemed at around $17 million in its last round.

It was just seven days back that Intel made another procurement to help its AI business, additionally in the region of machine getting the hang of demonstrating: it got SigOpt, which had built up a streamlining platform to run machine picking up displaying and reenactments.

Also read: Top 10 Programming Languages for Kids to learn

Cnvrg.io’s platform works across on-reason, cloud and half and half conditions and it comes in paid and complementary plans (we covered the dispatch of the free help, marked Core, a year ago).

It contends with any semblance of Databricks, Sagemaker and Dataiku, just as more modest activities like chúng tôi that are based on open-source structures.

Cnvrg’s reason is that it gives an easy to use platform to information scientists so they can focus on formulating calculations and estimating how they work, not fabricating or keeping up the platform they run on.

While Intel isn’t saying much regarding the arrangement, it appears to be that a portion of a similar rationale behind a week ago’s SigOpt procurement applies here also: Intel has been pulling together its business around cutting edge chips to more readily contend with any semblance of Nvidia and more modest players like GraphCore.

So it bodes well to likewise give/put resources into AI apparatuses for clients, explicitly administrations to help with the process stacks that they will be running on those chips.

It’s prominent that in our article about the Core complementary plan a year ago, Frederic noticed that those utilizing the platform in the cloud can do as such with Nvidia-improved holders that sudden spike in demand for a Kubernetes bunch.

Also read: Best 12 Vocabulary Building Apps for Adults 2023?

Intel’s attention on the up and coming age of figuring expects to balance decreases in its inheritance activities. In the last quarter, Intel announced a 3% decrease in its incomes, driven by a drop in its server farm business.

It said that it’s anticipating the AI silicon market to be greater than $25 billion by 2024, with AI silicon in the server farm to be more prominent than $10 billion in that period.

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Understand Machine Learning And Its End

This article was published as a part of the Data Science Blogathon.

What is Machine Learning?

Machine Learning: Machine Learning (ML) is a highly iterative process and ML models are learned from past experiences and also to analyze the historical data. On top, ML models are able to identify the patterns in order to make predictions about the future of the given dataset.

Why is Machine Learning Important?

Since 5V’s are dominating the current digital world (Volume, Variety, Variation Visibility, and Value), so most of the industries are developing various models for analyzing their presence and opportunities in the market, based on this outcome they are delivering the best products, services to their customers on vast scales.

What are the major Machine Learning applications?

Machine learning (ML) is widely applicable in many industries and its processes implementation and improvements. Currently, ML has been used in multiple fields and industries with no boundaries. The figure below represents the area where ML is playing a vital role.

Where is Machine Learning in the AI space?

Just have a look at the Venn Diagram, we could understand where the ML in the AI space and how it is related to other AI components.

As we know the Jargons flying around us, let’s quickly look at what exactly each component talks about.

How Data Science and ML are related?

Machine Learning Process, is the first step in ML process to take the data from multiple sources and followed by a fine-tuned process of data, this data would be the feed for ML algorithms based on the problem statement, like predictive, classification and other models which are available in the space of ML world. Let us discuss each process one by one here.

Machine Learning – Stages: We can split ML process stages into 5 as below mentioned in the flow diagram.

Collection of Data

Data Wrangling

Model Building

Model Evaluation

Model Deployment

Identifying the Business Problems, before we go to the above stages. So, we must be clear about the objective of the purpose of ML implementation. To find the solution for the given/identified problem. we must collect the data and follow up the below stages appropriately.

Data collection from different sources could be internal and/or external to satisfy the business requirements/problems. Data could be in any format. CSV, XML.JSON, etc., here Big Data is playing a vital role to make sure the right data is in the expected format and structure.

Data Wrangling and Data Processing: The main objective of this stage and focus are as below.

Data Processing (EDA):

Understanding the given dataset and helping clean up the given dataset.

It gives you a better understanding of the features and the relationships between them

Extracting essential variables and leaving behind/removing non-essential variables.

Handling Missing values or human error.

Identifying outliers.

The EDA process would be maximizing insights of a dataset.

Handling missing values in the variables

Convert categorical into numerical since most algorithms need numerical features.

Need to correct not Gaussian(normal). linear models assume the variables have Gaussian distribution.

Finding Outliers are present in the data, so we either truncate the data above a threshold or transform the data using log transformation.

Scale the features. This is required to give equal importance to all the features, and not more to the one whose value is larger.

Feature engineering is an expensive and time-consuming process.

Feature engineering can be a manual process, it can be automated

Training and Testing:

the efficiency of the algorithm which is used to train the machine.

Test data is used to see how well the machine can predict new answers based on its training.

used to train the model.

Training

Training data is the data set on which you train the model.

Train data from which the model has learned the experiences.

Training sets are used to fit and tune your models.

Testing

learnt good enough from the experiences it got in the train data set.

are “unseen” data to evaluate your models.

Test data: After the training the model, test data is used to test its efficiency and performance of the model

The purpose of the random state in train test split: Random state ensures that the splits that you generate are reproducible. The random state that you provide is used as a seed to the random number generator. This ensures that the random numbers are generated in the same order.

Data Split into Training/Testing Set

We used to split a dataset into training data and test data in the machine learning space.

The split range is usually 20%-80% between testing and training stages from the given data set.

A major amount of data would be spent on to train your model

The rest of the amount can be spent to evaluate your test model.

But you cannot mix/reuse the same data for both Train and Test purposes

If you evaluate your model on the same data you used to train it, your model could be very overfitted. Then there is a question of whether models can predict new data.

Therefore, you should have separate training and test subsets of your dataset.

MODEL EVALUATION: Each model has its own model evaluation mythology, some of the best evaluations are here.  

Evaluating the Regression Model.

Sum of Squared Error (SSE)

Mean Squared Error (MSE)

Root Mean Squared Error (RMSE)

Mean Absolute Error (MAE)

Coefficient of Determination (R2)

Adjusted R2

Evaluating Classification Model.

Confusion Matrix.

Accuracy Score.

Deployment of an ML-model simply means the integration of the finalized model into a production environment and getting results to make business decisions.

So, Hope you are able to understand the Machine Learning end-to-end process flow and I believe it would be useful for you, Thanks for your time.

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A Quick Guide To Data Science And Machine Learning

This article was published as a part of the Data Science Blogathon.

Introduction

Do you know that data is the ultimate goal for every organization, and hence actually I believe that it is the ruler? Without data, nothing can be achieved. From a business perspective to solving problems for end-to-end applications we require data.

This data needs to be in order to derive some purpose from it. Because forms of data can be texts, images, videos, infographics, gifs, etc. Some data are structured while most of them are unstructured. Collection, analysis, and prediction are the necessary steps that are to take into consideration with this data.

Image Source

Now, what exactly are Data Science and Machine learning?

I’ll just define it for you in a simple way. All the context related to this can be similar if you search somewhere else. So data science is the science of deriving insights from data for the purpose of gaining the most important and relevant source of information. And with a reliable source of information making predictions by the use of machine learning. So I guess you might have very well understood this definition. Now my point here is that with data science you can bring meaningful insights.

Why there is a need for data science and machine learning?

Data has been there for a very long time. During earlier times analysis of data was done by statisticians and analysts. Analysis of data was done primarily to get the summary and what were the causes. Mathematics was also the core subject of interest when used for this work.

How data science and machine learning solutions?

Data science uses statistical methods, maths, and programming techniques to solve these problems. The programming techniques are extensively used for analysis, visualizing, and making predictions. So you see it does all the work of a statistician, programmer, and maths. The study of all these major areas makes the best way of dealing with such big data. Machine learning is integrated by making models from various algorithms. 

This is done for model building in data science which helps for future predictions. These predictions depend upon the new data which is given to the model without explicitly telling it what to do. The model understands it and then gives us the output or solution. For example, banks use machine learning algorithms to detect if there is a fraud transaction or not. Or if this customer will default in paying his credit card dues.

Cancer detection in the health care industry uses data science and machine learning to detect if patients are prone to cancer or not. So there are a lot of examples around us where companies are widely using this. Online food delivery companies like zomato or swiggy use for recommending us food to order based on what have we ordered in the past. This type of machine learning algorithm is a recommendation system. They are also used by YouTube, Spotify, Amazon, etc.

The Data science life cycle.

There are various steps involved in solving business problems with data science.

1. Data acquisition – this process involves the collection of data. Depends on are objectives or what is the problem that needs to be solved. By this means, we tend to gather the required data.

2. Data pre-processing – this stage involves processing data in a structured format for ease of use. Unstructured data cannot be used for any analysis because it will give wrong business solutions and can have a bad impact on consumers.

3.Exploratory data analysis (EDA) – it is one of the most important stages where all the summarizations of data by statistics and math’s. Identifying the target(output) variable and predictor(independent) variables. Visualization of data and then sorting all the necessary data that will be used for predictions. Programming plays a vital role in this. A data scientist spends almost 75% of their time on this to understand their data very well. Further in this stage data is divided into training and test data.

4. Model building – After EDA we select the most appropriate methods to build our model. This is done with the use of machine learning algorithms. Selection of algorithms like regression, classification, or clustering. As machine learning algorithms are of 3 types. Supervised learning, unsupervised learning, and reinforcement learning. There are different sets of algorithms for all these types. Selecting them depends mainly on what is a problem are we trying to solve.

5. Evaluation of model – model evaluation is done to see how efficient our model is doing on the test data. Minimizing errors and also tuning of the model.

6. Deployment of model – model deployment is done as now it is fit to cater to all the future data for making predictions.

Note: There are re-evaluation techniques involved even after deployment to keep our model up-to-date.

How all this is done?

Data science tools and frameworks are specifically used for this process. Some popular tools like jupyter, tableau, tensor flow. Programming languages such as Python and R are important to do these tasks. To know and learn any one language is sufficient. Python and R are widely used for data science because there are additional libraries that make it easy for any data science project. I prefer Python as it is open-source, easy to learn, and has huge community support across the world. Statistics, math, and linear algebra are some core subjects you need to understand before getting involved in any data science or machine learning project.

In the future, these sources of data will keep on expanding and there will be a need to harvest all of these. An important part or information to get from this data will only derive the need for data scientists and machine learning engineers.

Mohammed Nabeel Qureshi

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Machine Learning Is Revolutionizing Stock Predictions

Stock predictions made by machine learning are being deployed by a select group of hedge funds that are betting that the technology used to make facial recognition systems can also beat human investors in the market.

Computers have been used in the stock market for decades to outrun human traders because of their ability to make thousands of trades a second. More recently, algorithmic trading has programmed computers to buy or sell stocks the instant certain criteria is met, such as when a stock suddenly becomes cheaper in one market than in another — a trade known as arbitrage.

Software That Learns to Improve Itself

Machine learning, an offshoot of studies into artificial intelligence, takes the stock trading process a giant step forward. Pouring over millions of data points from newspapers to TV shows, these AI programs actually learn and improve their stock predictions without human interaction.

According to Live Science, one recent academic study said it was now possible for computers to accurately predict whether stock prices will rise or fall based solely on whether there’s an increase in Google searches for financial terms such as “debt.” The idea is that investors get nervous before selling stocks and increase their Google searches of financial topics as a result.

These complex software packages, which were developed to help translate foreign languages and recognize faces in photographs, now are capable of searching for weather reports, car traffic in cities and tweets about pop music to help decide whether to buy or sell certain stocks.

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Mimicking Evolution and the Brain’s Neural Networks

A number of hedge funds have been set up that use only technology to make their trades. They include Sentient Technologies, a Silicon Valley-based fund headed by AI scientisk Babak Hodjat; Aidiya, a Hong Kong-based hedge fund headed by machine learning pioneer Ben Goertzel; and a fund still in “stealth mode” headed by Shaunak Khire, whose Emma computer system demonstrated that it could write financial news almost as well as seasoned journalists.

Although these funds closely guard their proprietary methods of trading, they involve two well-established facets of artificial intelligence: genetic programs and deep learning. Genetic software tries to mimic human evolution, but on a vastly faster scale, simulating millions of strategies using historic stock price data to test the theory, constantly refining the winner in a Darwinian competition for the best. While human evolution took two million years, these software giants accomplish the same evolutionary “mutations” in a matter of seconds.

Deep learning, on the other hand, is based on recent research into how the human brain works, employing many layers of neural networks to make connections with each other. A recent research study from the University of Freiburg, for example, found that deep learning could predict stock prices after a company issues a press release on financial information with about 5 percent more accuracy than the market.

Hurdles the Prediction Software Faces

None of the hedge funds using the new technology have released their results to the public, so it’s impossible to know whether these strategies work yet. One problem they face is that stock trading is not what economists call frictionless: There is a cost every time a stock is traded, and stocks don’t have one fixed price to buyers and sellers, but rather a spread between bid and offer, which can make multiple buy-and-sell orders expensive. Additionally, once it’s known that a particular program is successful, others would rush to duplicate it, rendering such trades unprofitable.

Another potential problem is the possible effects of so-called “black swan” events, or rare financial events that are completely unforeseen, such as the 2008 financial crisis. In the past, these types of events have derailed some leading hedge funds that relied heavily on algorithmic trading. Traders recall that the immensely profitable Long-Term Capital Management, which had two Nobel Prize-winning economists on its board, lost $4 billion in a matter of weeks in 1998 when Russia unexpectedly defaulted on its debt.

Some of the hedge funds say they have a human trader overseeing the computers who has the ability to halt trading if the programs go haywire, but others don’t.

The technology is still being refined and slowly integrated into the investing process at a number of firms. While the software can think for itself, humans still need to set the proper parameters to guide the machines toward a profitable outcome.

Technology and industry trends are shaping the next era of finance. Check out our complete line of finance industry solutions to stay ahead of the competition.

Can Java Be Used For Machine Learning And Data Science?

The world is drooling over

Top Expertise to Develop For Machine Learning & Data Science

If you want to excel in any field, you first need to develop the skills. Here’s a list of all the skills required if you’re going to learn ML & data science. Math: It is all about permutations and combination complemented with your calculation ability to be able to link yourself with machines. Data Architecture: To be able to reach the core of any technology, you must have a broad idea of the data formats. Software Structures: There is no ML without software, and a data engineer should be clear with concepts related to software and their working. Programming & Languages: If you do not know anything about this, there is no ML for you. Programming languages are the essential requirement for one to be able to build a career in ML. Differencing and Data Mining: If you have no clue about data, you are a zero. To be able to learn ML, data mining, and the ability to infer the information is crucial.  

Java: Machine Learning & Data Science’s Future

Java is a technology that proves beneficial in varied arrays of development and ML. One of the critical things in ML & Data Science is algorithms. With Java’s available resources, one can efficiently work in various algorithms and even develop them. It is a scalable language with many frameworks and libraries. In the current scenario, Java is amongst the most prominent languages in AI and ML. Some of the reasons why Java is an excellent alternative for a future in Data Science, Machine Learning, and finally, Artificial Intelligence are:  

Pace of Execution

If you are arguing about the speed of coding and execution, Java takes the lead in it, which means faster ML & DS technologies. Its features of statically typing and compilation are what makes it super in execution. With a lesser run time than any other language, knowing Java means you are good to go in the ML industry.  

Coding

Indentation in Java is not a must which makes it easier than Python or R. Also, coding in Java may require more lines, but it is easier than in other languages. If you are well-versed with coding, Java will be beneficial in ML and DS.  

Learning Curve

Java has a lot of areas where one must work hard. The learning curve for Java and allied language is quicker and more comfortable than other languages in totality. Suppose you know a language better and efficiently. In that case, it means that you can enter the domain at a more accelerated pace than through any other language whose learning curve is typical of Java.  

Salary Packages

Java has been in use for 30+ years. The future salaries of people who know Java are perceived to be higher than through any other language. We are not saying that you might not have a handsome amount in your hand if one knows Python. Instead, we are just focusing that with Java’s legacy in place, the salaries you get in your growth years are expected to be more for people who know Java.  

Community

Java will complete three decades of existence and is still one of the most prevalent and popularized languages. It means that numerous people in the enterprise know the language and will provide you with support in requirements. Several people in DS and ML are working through Java. It is an additional benefit that you can avail of if you learn ML and DS with Java.  

Varied Libraries

With Java, you have access to various libraries in Java for learning ML. To name a few, there are ADAMS, Mahaut, JavaML, WEKA, Deeplearning4j, etc.

The world is drooling over Artificial Intelligence . From research institutions to corporate houses, every organization aims to create AI-driven systems to build their enterprise. Machine Learning, or more commonly known as ML, is a sub-array of AI. With ML, you can teach the machines to behave like humans, i.e. develop brains in a machine. The result is automated machines that know-how and what is to be done. One commonly used place for AI & ML is Maps. Have you noticed that it shows you the route with the least traffic and the best route? That happens through ML along with other technologies. Another hot thing in the technological sphere is Big Data and its management. Big data is a terminology utilized for data of all types. It incorporates structured, semi-structured, and unstructured data. Be it any type of organization, you will always have a lot of data related to operations, finance, marketing, manufacturing, sales, etc. How you utilize and manage this data is the work of data scientists. Machines absorb the information that is further utilized and adopted in AI is all related to Big Data. Hence, to dive into AI, you will have to be accustomed to ML and Big data . Data science, ML, big data, and AI are all interlinked and synchronized. If you are talking about turning a machine like a human, it requires you to feed it in the language that it understands. Yes, we are talking, i.e. programming languages. Some of the commonly practiced languages for ML and Decision science are Python, Java, etc. But Java is a language that one must never forget. If you know Java Outsourcing Company , you can hop on the bandwagon of ML with great ease. How will it happen? Read along to learn chúng tôi you want to excel in any field, you first need to develop the skills. Here’s a list of all the skills required if you’re going to learn ML & data chúng tôi is all about permutations and combination complemented with your calculation ability to be able to link yourself with chúng tôi be able to reach the core of any technology, you must have a broad idea of the data formats.There is no ML without software, and a data engineer should be clear with concepts related to software and their chúng tôi you do not know anything about this, there is no ML for you. Programming languages are the essential requirement for one to be able to build a career in chúng tôi you have no clue about data, you are a zero. To be able to learn ML, data mining, and the ability to infer the information is chúng tôi is a technology that proves beneficial in varied arrays of development and ML. One of the critical things in ML & Data Science is algorithms. With Java’s available resources, one can efficiently work in various algorithms and even develop them. It is a scalable language with many frameworks and libraries. In the current scenario, Java is amongst the most prominent languages in AI and ML. Some of the reasons why Java is an excellent alternative for a future in Data Science, Machine Learning, and finally, Artificial Intelligence are:If you are arguing about the speed of coding and execution, Java takes the lead in it, which means faster ML & DS technologies. Its features of statically typing and compilation are what makes it super in execution. With a lesser run time than any other language, knowing Java means you are good to go in the ML industry.Indentation in Java is not a must which makes it easier than Python or R. Also, coding in Java may require more lines, but it is easier than in other languages. If you are well-versed with coding, Java will be beneficial in ML and chúng tôi has a lot of areas where one must work hard. The learning curve for Java and allied language is quicker and more comfortable than other languages in totality. Suppose you know a language better and efficiently. In that case, it means that you can enter the domain at a more accelerated pace than through any other language whose learning curve is typical of chúng tôi has been in use for 30+ years. The future salaries of people who know Java are perceived to be higher than through any other language. We are not saying that you might not have a handsome amount in your hand if one knows Python. Instead, we are just focusing that with Java’s legacy in place, the salaries you get in your growth years are expected to be more for people who know chúng tôi will complete three decades of existence and is still one of the most prevalent and popularized languages. It means that numerous people in the enterprise know the language and will provide you with support in requirements. Several people in DS and ML are working through Java. It is an additional benefit that you can avail of if you learn ML and DS with chúng tôi Java, you have access to various libraries in Java for learning ML. To name a few, there are ADAMS, Mahaut, JavaML, WEKA, Deeplearning4j, etc. We hope that now you know why one must learn Machine Learning and Data Science in Java. With its scalability, versatility, and balanced demand, you will always have to work with Java.

Knowledge Enhanced Machine Learning: Techniques & Types

This article was published as a part of the Data Science Blogathon.

Introduction

In machine learning, the data is an esse of the training of machine learning algorithms. The amount of data a the data quality highly affect the results from the machine learning algorithms. Almost all machin rning algorithms are data dependent, and their performance can be enhanced until some thresh mount of the data. However, the traditional machine learning algorithm’s behavior tends to be constant after some data is fed to the model.

This article will discuss the knowledge-enhanced machine learning techniques that introduce hierarchical and symbolic methods with limited data. Here we will discuss these methods, their relevance, and working mechanisms, followed by other vital discussions related to them. These methods are proper when there is little data and a need to train an accurate machine-learning model. the article will help one to understand the concepts related to knowledge and enhance machine learning better, and will able to make efficient choices and decisions in case of limited data scenarios.

Knowledge Enhanced Machine Learning

As the name suggests, knowledge enhanced machine learning is a type of technique where the knowledge of machine learning algorithms is enhanced by human capabilities or human understanding. In this technique, the machine learning algorithms apply their knowledge, and human or domain knowledge is integrated.

We humans can be trained on limited data, meaning that humans can learn several things by seeing or practicing stuff quickly and with limited data. For example, If we see a particular device, let’s say a Laptop, we can easily classify it and say it’s a type of electronic device. Also, we can classify it as an HP, Dell, or another model.

Source – Google

Machine learning models can classify several objects and perform specific tasks very quickly and efficiently, but the only problem is the amount of data. Yes, it requires a lot more amount of data to train an accurate model. But the knowledge-enhanced machine learning approach comes into the picture; it combines majorly two fields, the first is the model’s knowledge, and the other is human knowledge or human capabilities.

Hierarchical Learning and Symbolic Methods are knowledge enhancement machine learning approaches where human knowledge can be used to train a machine learning model with limited data, and the model’s performance can be en d.

Hierarchical Learning

As discussed above, when we humans see particular objects, our human mind automatically tries to classify the object into several classes. Let’s try to understand the same thing by taking appropriate examples.

Source – Google

As discussed above, the human mind can be taken as a trained machine learning model on limited data that classifies the object as a spot into several categories. Let’s take an example to understand the same thing.

Let’s suppose you saw the dog. Looking at the dog, we can easily classify its parent category as a “pet” and classify the dog as a labrador, dalmatian, french bulldog, or poodle. Here we can see that there are several levels of hierarchy where every single layer has several categories, and based on the knowledge of hierarchy, we humans can classify objects.

To implement this approach, the machine learning model can be trained on every layer of the hierarchy, and the model can be hyper-tuned to obtain the hierarchical learning model.

Symbolic Methods

The symbolic methods are also a knowledge base machine learning approach that tries to integrate human knowledge to classify several objects and build an accurate machine learning model.

Some machine learning models are trained so that whenever they are given an unseen image or object, they can efficiently and accurately classify the particular thing. These models are trained on a large amount of data.

We implement the same thing in symbolic methods but with limited data. Here we create the description or tags for the various objects and feed the data to the model. As there is little data available, there will be few images to train the model on, but the description of many objects will still be available.

Source – Google

Once the model is trained on such data, it can efficiently classify the unseen objects without training in such an image dataset, as it will use the description or tags provided based on human knowledge. So here, human knowledge is used to create the descriptions or tags of several objects, and machine learning models are used to train on such data.

Hierarchical vs. Symbolic Methods

As both approaches use human knowledge, a gentle question might come to mind: What is the main difference between such techniques?

The hierarchical learning approach is more towards a hierarchy of an object and its classification. Here human knowledge is used to classify and create the hierarchy of an object. Then machine learning models are used to train the algorithm on every level of the hierarchy for limited data scenarios.

In Symbolic methods, human knowledge is used to create the descriptions or the tags for particular objects, where the machine learning models are trained on limited image data. This machine learning model can now perform classification tasks on unseen images using human-generated descriptions or tags.

In general, we can not say that one of the approaches is always better, it all depends on the specific scenario of the data models and problem statement. Both approaches are nowadays being used for better performance on limited data, but one could use a specific approach per the requirement and conditions associated.

Conclusion

This article discussed knowledge enhanced machine learning techniques and their types. The hierarchical and symbolic approaches are discussed in detail with their core intuition and the difference between such methods. These articles will help geeks to understand the limited data scenario better and in an efficient way. They will help in several interviews and examinations as it is more of an academic topic.

Some Key Takeaways from this article are:

1. Knowledge-enhanced machine learning is a technique where human knowledge is used to train a machine learning model.

2. In the hierarchical technique, the machine learning model is strained on every hierarchy level generated by human knowledge or domain experts.

3. Symbolic methods also use human knowledge to generate descriptions or tags related to several objects so that the machine learning model can also classify unseen images and objects.

4. Both of the approaches are useful for specific cases and can be implemented as per the requirement and problem statement related to machine learning.

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