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Difference Between Data Mining and Machine Learning

Data mining, introduced in 1930, involves finding potentially useful, hidden, and valid patterns from large amounts of data. While machine learning introduced in near 1950 involves new algorithms from the data as well as previous experience to train and make predictions from the models, both of them intersect at the point of having useful datasets but other than that, they have various differences based upon the responsibilities, origin, Implementation, Nature, Application, Abstractions, Techniques and scope.

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Head to Head comparison Between Data mining and Machine learning (Infographics) Key Differences Between Data Mining and Machine Learning

Let us discuss some of the major differences between Data Mining and Machine Learning:

To implement data mining techniques, it used two-component first one are the database and the second one is machine learning. The Database offers data management techniques, while machine learning offers data analysis techniques. But to implement machine learning techniques, it used algorithms.

Data Mining uses more data to extract useful information, and that particular data will help to predict some future outcomes; for example, a sales company uses last year’s data to predict this sale, but machine learning will not rely much on data. It uses algorithms, for example, OLA and UBER machine learning techniques, to calculate the ETA for rides.

Self-learning capacity is not present in data mining; it follows the rules and is predefined. It will provide the solution for a particular problem, but machine learning algorithms are self-defined and can change their rules as per the scenario; it will find out the solution for a particular problem and resolve it in its own way.

The main and foremost difference between data mining and machine learning is without the involvement of humans, data mining can’t work, but in machine learning, human effort is involved only at the time when the algorithm is defined. After that, it will conclude everything by itself once implemented forever to use, but this is not the case with data mining.

The result produced by machine learning will be more accurate than data mining since machine learning is an automated process.

Data mining uses the database or data warehouse server, data mining engine, and pattern evaluation techniques to extract useful information. In contrast, machine learning uses neural networks, predictive models, and automated algorithms to make decisions.

Data Mining and Machine Learning Comparison Table

Basis for Comparison Data Mining Machine Learning

Meaning Extracting knowledge from a large amount of data Introduce a new algorithm from data as well as past experience

History Introduced in 1930, initially referred as knowledge discovery in databases Introduced in near 1950, the first program was Samuel’s checker-playing program

Responsibility Data mining is used to get the rules from the existing data. Machine learning teaches the computer to learn and understand the given rules.

Origin Traditional databases with unstructured data Existing data as well as algorithms.

Implementation We can develop our own models where we can use data mining techniques for We can use machine learning algorithms in decision trees, neural networks, and some other areas of artificial intelligence.

Nature Involves human interference more manually. Automated, once design is self-implemented, no human effort

Application used in cluster analysis used in web search, spam filter, credit scoring, fraud detection, computer design

Abstraction Data mining abstract from the data warehouse

Techniques Involved Data mining is more of research using methods like machine learning Self-learned and trained system to do the intelligent task.

Scope Applied in the limited area It can be used in a vast area.

 Conclusion

In most cases now, data mining is used to predict the result from historical data or find a new solution from the existing data. Most of organization uses this technique to drive business outcomes where machine learning techniques are growing in a much faster way since it overcomes the problems with what data mining techniques have. But to drive the business still, we need to have data mining process because it will define the problem of a particular business, and to resolve such problem, we can use machine learning techniques. In one word, we can say that to drive a business, both Data mining and Machine learning techniques have to work hand in hand, one technique will define the problem, and the other will give you the solution in a much more accurate way.

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Artificial Intelligence Vs. Machine Learning

During the past few years, the terms artificial intelligence and machine learning have begun showing up frequently in technology news and websites. Often the two are used as synonyms, but many experts argue that they have subtle but real differences.

And of course, the experts sometimes disagree among themselves about what those differences are.

In general, however, two things seem clear: first, the term artificial intelligence (AI) is older than the term machine learning (ML), and second, most people consider machine learning to be a subset of artificial intelligence.

One of the best graphic representations of this relationship comes from Nvidia’s blog. It offers a good starting point for understanding the differences between artificial intelligence and machine learning.

Artificial Intelligence vs. Machine Learning – First, What’s AI?

Computer scientists have defined artificial intelligence in many different ways, but at its core, AI involves machines that think the way humans think. Of course, it’s very difficult to determine whether or not a machine is “thinking,” so on a practical level, creating artificial intelligence involves creating a computer system that is good at doing the kinds of things humans are good at.

The idea of creating machines that are as smart as humans goes all the way back to the ancient Greeks, who had myths about automatons created by the gods. In practical terms, however, the idea didn’t really take off until 1950.

In that year, Alan Turing published a groundbreaking paper called “Computing Machinery and Intelligence” that posed the question of whether machines can think. He proposed the famous Turing test, which says, essentially, that a computer can be said to be intelligent if a human judge can’t tell whether he is interacting with a human or a machine.

The phrase artificial intelligence was coined in 1956 by John McCarthy, who organized an academic conference at Dartmouth dedicated to the topic. At the end of the conference, the attendees recommended further study of “the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.”

This proposal foreshadowed many of the topics that are of primary concern in artificial intelligence today, including natural language processing, image recognition and classification, and machine learning.

In the years immediately after that first conference, artificial intelligence research flourished. However, within a few decades it became apparent that the technology to create machines that could truly be said to be thinking for themselves was many years off.

But in the last decade, artificial intelligence has moved from the realms of science fiction to the realm of scientific fact. Stories about IBM’s Watson AI winning the game show Jeopardy and Google’s AI beating human champions at the game of Go have returned artificial intelligence to the forefront of public consciousness.

Today, all of the largest technology companies are investing in AI projects, and most of us interact with AI software every day whenever we use smartphones, social media, Web search engines or ecommerce sites. And one of the types of AI that we interact with most often is machine learning.

Artificial Intelligence vs. Machine Learning – Okay, Then What’s Machine Learning?

The phrase “machine learning” also dates back to the middle of the last century. In 1959, Arthur Samuel defined machine learning as “the ability to learn without being explicitly programmed.” And he went on to create a computer checkers application that was one of the first programs that could learn from its own mistakes and improve its performance over time.

Like AI research, machine learning fell out of vogue for a long time, but it became popular again when the concept of data mining began to take off around the 1990s. Data mining uses algorithms to look for patterns in a given set of information. Machine learning does the same thing, but then goes one step further – it changes its program’s behavior based on what it learns.

One application of machine learning that has become very popular recently is image recognition. These applications first must be trained – in other words, humans have to look at a bunch of pictures and tell the system what is in the picture. After thousands and thousands of repetitions, the software learns which patterns of pixels are generally associated with horses, dogs, cats, flowers, trees, houses, etc., and it can make a pretty good guess about the content of images.

Many web-based companies also use machine learning to power their recommendation engines. For example, when Facebook decides what to show in your newsfeed, when Amazon highlights products you might want to purchase and when Netflix suggests movies you might want to watch, all of those recommendations are on based predictions that arise from patterns in their existing data.

Currently, many enterprises are beginning to use machine learning capabilities for predictive analytics. As big data analysis has become more popular, machine learning technology has become more commonplace, and it’s a standard feature in many analytics tools.

In fact, machine learning has become so associated with statistics, data mining and predictive analytics that some people argue it should be classified as a separate field from artificial intelligence. After all, systems can exhibit AI features like natural language processing or automated reasoning without having any machine learning capabilities, and machine learning systems don’t necessarily need to have any other features of artificial intelligence.

However, machine learning has been part of the discussion around artificial intelligence from the very beginning, and the two remain closely entwined in many applications coming to market today. For example, personal assistants and bots often have many different AI features, including ML.

Artificial Intelligence and Machine Learning Frontiers: Deep Learning, Neural Nets, and Cognitive Computing

Of course, “machine learning” and “artificial intelligence” aren’t the only terms associated with this field of computer science. IBM frequently uses the term “cognitive computing,” which is more or less synonymous with AI.

However, some of the other terms do have very unique meanings. For example, an artificial neural network or neural net is a system that has been designed to process information in ways that are similar to the ways biological brains work. Things can get confusing because neural nets tend to be particularly good at machine learning, so those two terms are sometimes conflated.

In addition, neural nets provide the foundation for deep learning, which is a particular kind of machine learning. Deep learning uses a certain set of machine learning algorithms that run in multiple layers. It is made possible, in part, by systems that use GPUs to process a whole lot of data at once.

If you’re confused by all these different terms, you’re not alone. Computer scientists continue to debate their exact definitions and probably will for some time to come. And as companies continue to pour money into artificial intelligence and machine learning research, it’s likely that a few more terms will arise to add even more complexity to the issues.

Simplifying Data Preparation And Machine Learning Tasks Using Rapidminer

Introduction

It’s a well-known fact that we spend too much time on data preparation and not as much time as we want on building cool machine learning models. In fact, a Harvard Business Review publication confirmed what we always knew: analytics teams spend 80% of their time preparing data. And they are typically slowed down by clunky data preparation tools coupled with a scarcity of data science experts.

But not for much longer, folks! RapidMiner recently released a really nice functionality for data preparation, RapidMiner Turbo Prep. You will soon know why we picked this name 🙂, but the basic idea is that Turbo Prep provides a new data preparation experience that is fast and fun to use with a drag and drop interface.

Let’s walk through some of the possibilities of this feature, as well as demonstrate how it integrates with RapidMiner Auto Model, our automated machine learning product. These two features truly make data prep and machine learning fast, fun, and simple. If you would like to follow along, make sure you have RapidMiner Studio 9.0 downloaded. All free users have access to Auto Model and Turbo Prep for 30 days.

Table of Contents

Loading and Inspecting the Data

Transforming Data

Viewing the Process

Predicting Delays using Automated Machine Learning

Data Preparation and Machine Learning Simplified

Loading and Inspecting the Data

First, we’re going to start by loading some data. Data can be added from all repository-based sources or be imported from your local machine.

RapidMiner Turbo Prep start screen

Loading sample data sets

Once you load the data it can be seen immediately in a data-centric view, along with some data quality indicators. At the top of the columns, the distributions and quality measurements of the data are displayed. These indicate whether the columns will be helpful for machine learning and modeling. Say, for example, the majority of the data in a column is missing, this could confuse a machine learning model, so it is often better to remove it all together. If the column acts as an ID, that means practically all of the values only occur once in the data set, so this not useful for identifying patterns, and also should be removed.

Data centric view of RapidMiner Turbo Prep

Transforming Data

Pivot Tables

As a first step, in order to look at the data in aggregate, we are going to create a pivot table. To generate this pivot table, first, we will look at the airport codes, indicated by ‘Origin’, with the airport name ‘OriginName’, and calculate the average delay at these locations. We can see the result immediately by dragging ‘DepDelay’ into the ‘Aggregates’ area, which calculates the average. In this case, the biggest delays are happening at the Nantucket airport, which is a very small airport; there is a high average delay of more than 51 minutes. In order to take the number of flights into account, we will also add in ‘Origin count’ and sort to show the largest airport by flight. In this case, Boston Logan Airport is the largest with almost 130,000 flights.

Pivot table in RapidMiner Turbo Prep

Applying a filter

Next, we’re going to bring in some additional data about the weather in New England for the same year. This data set can be found in the same ‘Transportation’ folder as the flight data. We know from personal experience, that weather can create delays, so we want to add this data in to see if the model picks up on it. In a longer scenario, we might take a look at the flight data alone at first and discover that the model is 60% accurate. Then add in the weather information and see how the accuracy of the model improves. But for this demonstration, we will go straight to adding in the weather. In this data, there is a single ‘Date’ column but in our flight data there were two columns, one for the day and one for the month, so we’ll need to transform the weather data to match.

Single ‘Date’ Column in weather data

Start the transformation by copying the ‘Date’ column so there are two duplicate columns next to each other. Then rename the columns to ‘W_Day’ and ‘W_Month’ for consistency.

Copied and renamed ‘Date’ columns in weather data

Extracting the day from the month

Extracting the month from the year

Merging data

Now, we need to merge the two data sets together. Turbo Prep uses smart algorithms to intelligently identify data matches. Two data sets are a good match if they have two columns that match with each other. And two columns match well with each other if they contain similar values. In this example, we see a pretty high match of 94%.

% match of the two data sets

Joins 

Merged data view

Generating columns

Generating a ‘Delay Class’ column

Cleansing data

RapidMiner Turbo Prep auto cleansing option

Defining a target in auto cleanse

Removing columns with quality issues in auto cleanse

Viewing the Process

History

History view

RapidMiner Studio process

Process view in RapidMiner Studio

Predicting Delays using Automated Machine Learning

RapidMiner Auto Model

Predicting the ‘Delay Class’

In the ‘Prepare Target’ view, we can choose to map values or change the class of highest interest, but we are most interested in predicting delays, so we will keep the default settings here. 

Prepare target view in RapidMiner Auto Model

Removing suspicious columns in yellow

Selecting the model types

Actual Lapsed Time

Min Humidity

Naïve Bayes simulator with average inputs

Naïve Bayes simulator with decreased visibility

In ‘Overview’ we can see how well the different models performed, here we see that GLM and Logistic Regression performed better than Naïve Bayes. We could also look at the ROC Comparison, or the individual Model Performance and Lift Chart.

Auto Model results overview

Important influence factors

And just like Turbo Prep, Auto Model processes can be opened in RapidMiner Studio, showing the full process with annotations. With Auto Model, every step is explained with its importance and why certain predictions are made during model creation. We can see exactly how the full model was created; there are no black boxes!

Auto Model process

Data Preparation and Machine Learning Simplified

You can also create repeatable data prep steps, making it faster to reuse processes. Data can also be saved as Excel or CSV or sent to data visualization products like Qlik. 

If you haven’t tried these two features yet, we’re offering a 30-day trial of Studio Large to all free users so download it now.   

About RapidMiner

This sponsored post has been written by RapidMiner and all opinions expressed in this post are entirely those of RapidMiner.

Related

How Lucrative Is Machine Learning?

Introduction How Lucrative is Machine Learning?

Machine learning is a very lucrative field, to put it simply. The average yearly salary for a machine learning engineer in the US is $114,121, according to Glassdoor. This is much more than the $51,960 yearly average pay for all Americans. In fact, one of the highest-paying positions in the tech sector is in machine learning.

But machine learning is valuable in many ways than just the pay. Experts in machine learning are in high demand, thus there are many job opportunities accessible. Machine learning engineering is one of the top developing professions in the United States, according to LinkedIn. Since 2024, there have been 344% more opportunities for machine learning engineers.

Machine learning is becoming more and more crucial in a range of industries, which is one of the reasons why experts are in such high demand. For example, machine learning is used in the healthcare sector to examine medical data and spot trends that can be exploited to enhance patient outcomes. Machine learning is used in the finance sector to evaluate financial data and spot trends that may be utilised to improve investment selection. Machine learning is utilised in the transportation sector to improve traffic flow and lessen congestion.

Machine learning is so profitable in part because it necessitates specific knowledge and abilities. Experts in machine learning require a solid foundation in computer science, statistics, and mathematics. They also need to have experience creating machine learning algorithms and working with massive datasets. Because of this, machine learning specialists are highly talented and in high demand.

But still big businesses aren’t the only ones in need of machine learning specialists. Furthermore, starting to make investments in machine learning technologies are small and medium-sized organisations. As a result, machine learning specialists might find employment possibilities with both large organisations and start-ups.

Machine learning professionals can work as consultants or independent contractors in addition to more conventional employment options. Instead of hiring machine learning specialists on a full-time basis, many companies are opting to recruit them on a project-by-project basis. This enables professionals in machine learning to work on a range of various projects and get experience in a range of sectors.

Making and marketing their own machine learning models and algorithms is another way that specialists in the field might profit. Machine learning developers can sell their models and algorithms to organisations and people on a few marketplaces. As a result, machine learning specialists can monetize their knowledge and produce passive revenue.

But how can one become an expertise in machine learning? Those who are interested in learning more about machine learning can choose from a variety of educational resources and programs. Data science and machine learning courses are available at several universities. Also, several online resources and courses are offered, including Coursera and Udemy. There are also numerous machine learning groups and forums where people can interact with other machine learning professionals and exchange knowledge.

Machine learning experts are highly competent professionals because it calls for knowledge and skills. Machine learning professionals can work as consultants, freelancers, and even create and market their own machine learning models and algorithms in addition to typical employment prospects. The need for machine learning professionals is only expected to increase in the upcoming years due to the growing significance of big data and AI.

The availability of data is one of the elements supporting machine learning’s development. There is now more data available than ever before thanks to the development of the internet and the rising popularity of digital devices. This data can be analyzed by machine learning algorithms to find patterns and trends that can be used to guide decision-making.

Machine learning is also becoming more accessible to businesses of all sizes. Thanks to cloud computing platforms like Amazon Web Services and Microsoft Azure, businesses can now utilise machine learning technologies more easily without having to invest money on expensive hardware or software.

It’s important to remember that not all machine learning jobs are created equal. There are many different job titles in the machine learning industry, some of which might be more lucrative than others. For example, data scientists and analysts usually make more money than machine learning engineers. Location can also affect pay, with machine learning specialists in populated areas earning more than those in major places like New York and San Francisco.

The amount of competitiveness within the sector represents an additional negative factor. As more people enter the area, there is more rivalry for employment and projects because of machine learning’s growing popularity. This suggests that the need for machine learning specialists is increasing, indicating that the discipline will remain successful in the future.

Conclusion

In conclusion, the machine learning industry is quite lucrative and offers a wide range of career and business opportunities. The sector demands specialized skills and knowledge, but as big data and AI gain importance, the demand for machine learning experts will only increase. Even though any job has challenges and competition, individuals who have the right education and skills can succeed in the field of machine learning.

Why Does Machine Learning Use Gpus?

The GPU (graphics processing unit) is now the backbone of AI. Originally developed to speed up graphics processing, GPUs can greatly expedite the computing operations needed in deep learning. Many modern applications failed because machine learning needed more active, accurate, or both. Large neural networks benefited significantly from the incorporation and use of GPUs.

Autonomous vehicles and face recognition are two examples of how deep learning has revolutionized technology. In this article, we’ll discuss why GPUs are so useful for machine learning applications −

How do Graphics Processing Units Work?

As with every neural network, the deep learning model’s training phase is the process’s most time- and energy-consuming part. The original intent of these chips was to handle visual information. To improve predictions, weights are tweaked to identify patterns. But these days, GPUs are also used to speed up other kinds of processing, like deep learning. This is because GPUs lend themselves well to parallelism, making them ideal for large-scale distributed processing.

The Function of Graphics Processing Units (GPU)

First, let’s take a step back while ensuring we fully grasp how GPUs work.

Nvidia’s GeForce 256, released in 1999, was instrumental in popularizing the phrase “graphics processing unit” due to its ability to do graphical operations such as change, illumination, and triangle clipping. Processes can be optimized and hastened thanks to the engineering that is specific to these tasks. This involves complex calculations that aid in the visualization of three-dimensional environments. Repetition is produced when millions of calculations are performed, or floating point values are used. The conditions are ideal for parallel execution of tasks.

With cache and additional cores, GPUs can easily outperform dozens of CPUs. Let’s take an example −

Adding more processors will increase the speed linearly. Even with 100 CPUs, the procedure would still take over a week, and the cost would be fairly high. The issue can be resolved in less than a day using parallel computing on a small number of GPUs. So, we were able to accomplish the unimaginable by developing this gear.

How Machine Learning got Benefits Through GPU?

GPU has a lot of processor cores, which is great for running parallel programs. Graphics processing units (GPUs) enable the accumulation of numerous cores that consume fewer resources without compromising efficiency or power. As a result of its ability to handle several computations simultaneously, GPUs are particularly well-suited for use in the training of artificial intelligence and deep learning models. This allows for the decentralization of training, which in turn speeds up machine learning processes. Furthermore, machine learning computations need to deal with massive amounts of data, so the memory bandwidth of a GPU is ideal.

Usage of GPU Quantity of Data

In order to train a model with deep learning, a sizable amount of data must be collected. A graphics processing unit (GPU) is the best option for fast data computation. The size of the dataset is irrelevant to the scalability of GPUs in parallel, which makes processing large datasets much quicker than on CPUs.

Bandwidth of Memory

One of the key reasons GPUs are quicker for computing is that they have more bandwidth. Memory bandwidth, particularly that provided by GPUs, is available and necessary for processing massive datasets. Memory on the central processing unit (CPU) can be depleted rapidly during instruction regarding a large dataset. This is because modern GPUs come equipped with their own video RAM (VRAM), freeing up your CPU for other uses.

Optimization

Due to the extensive work involved, parallelization in dense neural networks is notoriously challenging. One drawback of GPUs is that it can be more challenging to optimize long-running individual operations than it is with CPUs.

Choices in GPU Hardware for Machine Learning

There are a number of possibilities for GPUs to use in deep learning applications, with NVIDIA being the industry leader. You have the choice of picking among managed workstations, GPUs designed for use in data centers, or GPUs aimed at consumers.

GPUs Designed for Home Use

These GPUs are an inexpensive add-on to your existing system that can help with model development and basic testing.

The NVIDIA Titan RTX has 130 teraflops of processing power and 24 GB of RAM. Built on NVIDIA’s Turing GPU architecture, it features Tensor and RT Core technologies.

NVIDIA Titan V − Depending on the variant, this GPU offers anywhere from 110 to 125 teraflops of speed and 12 to 32 terabytes of memory. The NVIDIA Volta architecture and Tensor Cores are utilized.

Graphics Processing Units in the Data Center

These graphics processing units (GPUs) are made for massive undertakings and can deliver server-level performance.

NVIDIA A100 − It gives you 624 teraflops of processing power and 40 gigabytes of memory. With its multi-instance GPU (MIG) technology, it is capable of large scaling for use in high-performance computing (HPC), data analytics, and machine learning.

NVIDIA Tesla P100 − The NVIDIA Tesla P100 has 16 GB of RAM and can process 21 teraflops. Based on the Pascal architecture, it’s made with high-performance computing and machine learning in mind.

NVIDIA v100 − The newest NVIDIA v100 graphics card supports up to 32 GB of RAM and 149 TFLOPS of processing power. NVIDIA Volta technology forms the basis for this product, which was made for HPC, ML, and DL.

GPU Performance Indicators for Deep Learning

Due to inefficient allocation, the GPU resources of many deep learning projects are only used between 10% to 30% of the time. The following KPIs should be tracked and used to ensure that your GPU investments are being put to good use.

Use of Graphics Processing Units

Metrics for GPU utilization track how often your graphics processing unit’s kernels are used. These measurements can be used to pinpoint where your pipelines are lagging and how many GPUs you need.

Temperature and Power Consumption

Metrics like power utilization and temperature allow you to gauge the system’s workload, allowing you to better foresee and manage energy needs. Power consumption is measured at the PSU and includes the power needed by the CPU, RAM, and any other cooling components.

Using and Accessing GPU Memory Conclusion

GPUs are the safest choice for fast learning algorithms because the bulk of data analysis and model training comprises simple matrices math operations, the performance of which may be significantly enhanced if the calculations are performed in parallel. Consider purchasing a GPU if your neural network requires extensive computation with hundreds of thousands of parameters.

What Are Machine Learning And Deep Learning In Artificial Intelligence

Devices connected to the Internet are called smart devices. Pretty much everything related to the Internet is known as a smart device. In this context, the code that makes the devices SMARTER – so that it can work with minimal or without any human intervention – can be said to be based on Artificial Intelligence (AI). The other two, namely: Machine Learning (ML), and Deep Learning (DL), are different types of algorithms built to bring more capabilities to the smart devices. Let’s see AI vs ML vs DL in detail below to understand what they do and how they are connected to AI.

What is Artificial Intelligence with respect to ML & DL

AI can be called a superset of Machine Learning (ML) processes, and Deep Learning (DL) processes. AI usually is an umbrella term that is used for ML and DL. Deep Learning is again, a subset of Machine Learning (see image above).

Some argue that Machine Learning is no more a part of the universal AI. They say ML is a complete science in its own right and thus, need not be called with reference to Artificial Intelligence. AI thrives on data: Big Data. The more data it consumes, the more accurate it is. It is not that it will always predict correctly. There will be false flags as well. The AI trains itself on these mistakes and becomes better at what it is supposed to do – with or without human supervision.

Artificial Intelligence cannot be defined properly as it has penetrated into almost all industries and affects way too many types of (business) processes and algorithms. We can say that Artificial Intelligence is based on Data Science (DS: Big Data) and contains Machine Learning as its distinct part. Likewise, Deep Learning is a distinct part of Machine Learning.

The way the IT market is tilting, the future would be dominated with connected smart devices, called the Internet of Things (IoT). Smart devices mean artificial intelligence: directly or indirectly. You are already using artificial intelligence (AI) in many tasks in your daily life. For example, typing on a smartphone keyboard that keeps on getting better on “words suggestion”. Among other examples where you unknowingly are dealing with Artificial Intelligence are searching for things on the Internet, online shopping, and of course, the ever-smart Gmail and Outlook email inboxes.

What is Machine Learning

Machine Learning is a field of Artificial Intelligence where the aim is to make a machine (or computer, or a software) learn and train itself without much programming. Such devices need less programming as they apply human methods to complete tasks, including learning how to perform better. Basically, ML means programming a computer/device/software a bit and allowing it to learn on its own.

There are several methods to facilitate Machine Learning. Of them, the following three are used extensively:

Supervised,

Unsupervised, and

Reinforcement learning.

Supervised Learning in Machine Learning

Usually, it is confirmed using the 80/20 rule. Huge sets of data are fed to a computer that tries and learns the logic behind the answers. 80 percent of data from an event is fed to the computer along with answers. The remaining 20 percent is fed without answers to see if the computer can come up with proper results. This 20 percent is used for cross-checking to see how the computer (machine) is learning.

Unsupervised Machine Learning

Unsupervised Learning happens when the machine is fed with random data sets that are not labeled, and not in order. The machine has to figure out how to produce the results. For example, if you offer it softballs of different colors, it should be able to categorize by colors. Thus, in the future, when the machine is presented with a new softball, it can identify the ball with already present labels in its database. There is no training data in this method. The machine has to learn on its own.

Reinforcement Learning

Machines that can make a sequence of decisions fall into this category. Then there is a reward system. If the machine does good at whatever the programmer wants, it gets a reward. The machine is programmed in a way that it craves maximum rewards. And to get it, it solves problems by devising different algorithms in different cases. That means the AI computer uses trial and error methods to come up with results.

For example, if the machine is a self-driving vehicle, it has to create its own scenarios on road. There is no way a programmer can program every step as he or she can’t think of all the possibilities when the machine is on the road. That is where Reinforcement Learning comes in. You can also call it trial and error AI.

How is Deep Learning different from Machine Learning

Deep Learning is for more complicated tasks. Deep Learning is a subset of Machine Learning. Only that it contains more neural networks that help the machine in learning. Manmade neural networks are not new. Labs across the world are trying to build and improve neural networks so that the machines can make informed decisions. You must have heard of Sophia, a humanoid in Saudi that was provided regular citizenship. Neural networks are like human brains but not as sophisticated as the brain.

There are some good networks that provide for unsupervised deep learning. You can say that Deep Learning is more neural networks that imitate the human brain. Still, with enough sample data, the Deep Learning algorithms can be used to pick up details from sample data. For example, with an image processor DL machine, it is easier to create human faces with emotions changing according to the questions the machine is asked.

The above explains AI vs MI vs DL in easier language. AI and ML are vast fields – that are just opening up and have tremendous potential. This is the reason some people are against using Machine Learning and Deep Learning in Artificial Intelligence.

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