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Imagine you are about to sit down to play a game with a friend. But this isn’t just any friend – it’s a computer program that doesn’t know the rules of the game. It does, however, understand that it has a goal, and that goal is to win.

Because this friend doesn’t know the rules, it starts by making random moves. Some of them make absolutely no sense, and winning for you is easy. But let’s just say you enjoy playing with this friend so much that you decide to devote the rest of your life (and future lives if you believe in that idea) to exclusively playing this game.

The digital friend will eventually win because it gradually learns the winning moves required to beat you. This scenario may seem far-fetched, but it should give you a basic idea of how reinforcement learning (RL) – an area of machine learning (ML) – roughly works.

Just how intelligent is reinforcement learning?

Human intelligence encompasses many characteristics, including the attainment of knowledge, a desire to expand intellectual capacity, and intuitive thinking. Our capacity for intelligence, however, was largely questioned when Garry Kasparov, a champion chess player, lost to an IBM computer named Deep Blue. Besides capturing the attention of the public, doomsday scenarios depicting a world where robots rule humans took hold of mainstream consciousness.

Deep Blue, however, was not an average opponent. Playing with this program is analogous to a match with a thousand-year-old human that devoted their entire life to continuously playing chess. Accordingly, Deep Blue was skilled in playing a specific game – not in other intellectual pursuits like playing an instrument, writing a book, conducting a scientific experiment, raising a child, or fixing a car.

In no way am I attempting to downplay the achievement of the creation of Deep Blue. Instead, I am simply suggesting that the idea that computers can surpass us in intellectual capability requires careful examination, starting with a breakdown of RL mechanics.

How Reinforcement Learning Works

As mentioned previously, RL is a subset of ML concerned with how intelligent agents should act in an environment to maximize the notion of cumulative reward.

In plain terms, RL robot agents are trained on a reward and punishment mechanism where they are rewarded for correct moves and punished for the wrong ones. RL Robots don’t “think” about the best actions to make – they just make all the moves possible in order to maximize chances of success.

Drawbacks of Reinforcement Learning

The main drawback of reinforcement learning is the exorbitant amount of resources it requires to achieve its goal. This is illustrated by the success of RL in another game called GO – a popular 2-player game where the goal is to use playing pieces (called stones) to maximize territory on a board while avoiding the loss of stones.

AlphaGo Master, a computer program that defeated human players in Go, required a massive investment that included many engineers, thousands of years worth of game-playing experience, and an astonishing 256 GPUs and 128,000 CPU cores.

That’s a lot of energy to use in learning to win a game. This then begs the question of whether it is rational to design AI that cannot think intuitively. Shouldn’t AI research attempt to mimic human intelligence?

One argument favoring RL is that we should not expect AI agents to behave like humans, and its use to solve complex problems warrants further development. On the other hand, an argument against RL is that AI research should focus on enabling machines to do things that only humans and animals are presently capable of doing. When viewed in that light, AI’s comparison to human intelligence is appropriate.

Quantum Reinforcement Learning

There’s an emerging field of reinforcement learning that purportedly solves some of the problems outlined above. Quantum reinforcement learning (QRL) has been studied as a way to speed up calculations.

Primarily, QRL should speed up learning by optimizing the exploration (finding strategies) and exploitation (picking the best strategy) phases. Some of the current applications and proposed quantum calculations improve database search, factoring large numbers into primes, and much more.

While QRL still hasn’t arrived in a groundbreaking fashion, there’s an expectation that it may resolve some of the great challenges for regular reinforcement learning.

Business Cases for RL

As I mentioned before, in no way do I want to undermine the importance of RL research and development. In fact, at Oxylabs, we have been working on RL models that will optimize web scraping resource allocation.

With that said, here is just a sample of some real-life uses for RL derived from a McKinsey report highlighting current use cases across a wide range of industries:

Optimizing silicon and chip design, optimizing manufacturing processes, and improving yields for the semiconductor industry

Increasing yields, optimizing logistics to reduce waste and costs, and improving margins in agriculture

Reducing time to market for new systems in the aerospace and defense industries

Optimizing design processes and increasing manufacturing yields for the automotive industries

Optimizing mine design, managing power generation and applying holistic logistics scheduling to optimize operations, reduce costs and increase yields in mining

Increasing yields through real-time monitoring and precision drilling, optimizing tanker routing and enabling predictive maintenance to prevent equipment failure and outages in the oil and gas industry

Facilitating drug discovery, optimizing research processes, automating production and optimizing biologic methods for the pharmaceutical industry

Optimizing and managing networks and applying customer personalization in the telecom industry

Optimizing routing, network planning, warehouse operations in transport and logistics

Extracting data from websites with the use of next-generation proxies

  Rethinking Reinforcement Learning

Reinforcement learning may be limited, but it’s hardly overrated. Moreover, as research and development into RL increases, so do potential use cases across almost every sector of the economy.

Wide-scale adoption depends on several factors, including optimizing the design of algorithms, configuring learning environments, and the availability of computing power.

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Reinforcement Learning Techniques Based On Types Of Interaction

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

Introduction

With the ubiquitous adoption of deep learning, reinforcement learning (RL) has seen a sharp rise in popularity, scaling to problems that were intractable in the past, such as controlling robotic agents and autonomous vehicles, playing complex games from pixel observations, etc.

This article will cover what reinforcement learning is and different types of reinforcement learning paradigms based on the types of interaction.

Now, let’s begin…

Highlights

Reinforcement Learning (RL) is a general framework that enables an agent to discover the best way to maximize a given reward signal through trial and error using feedback from its actions and experiences, i.e., actively interacting with the environment by taking actions and observing the reward.

In online RL, the agent is free to interact with the environment and must gather new experiences with the latest policy before updating.

In off-policy RL, an agent interacts with the environment and appends its new experiences to a replay buffer, which can then be sampled to update the policy. This paradigm allows for the reuse of prior experiences while relying on a steady stream of fresh ones.

In offline RL, a behavior policy is used to gather experiences that are used to collect experiences that are stored in a static dataset. Then a new policy is learned without any further interactions with the environment.

What is Reinforcement Learning?

Reinforcement Learning (RL) is a general framework for adaptive control that enables an agent to learn to maximize a specified reward signal through trial and error using feedback from its actions and experiences, i.e., actively interacting with the environment by taking actions and observing the reward.

Figure 1: Diagram illustrating Reinforcement Learning

In figure 1, we consider that the agent interacts with the environment. Even though the agent and the environment are separately drawn, we can also picture the agent to be somewhere existing inside the environment. Imagine a huge world in which the agent exists somewhere inside that world and interacts with it, e.g., the Super Mario game.

For instance, in the animation shown below, Mario exists inside the game environment and can move and jump in both left and right directions. When Mario interacts with the flower, he gets a positive reward; however, if Mario comes in contact with the monster, he gets penalized (negative reward). So Mario learns by actively interacting with the environment by taking actions and observing the rewards. Furthermore, it learns to maximize positive rewards through trial and error.

Animation 1: Example of Reinforcement Learning

In essence, reinforcement learning essentially provides a mathematical formalism for learning-based control. By using reinforcement learning, we can automatically develop near-optimal behavioral skills, represented by policies, to optimize user-specified reward functions. The reward function determines what an agent should do, and a reinforcement learning algorithm specifies how to do it.

Now that we are familiar with Reinforcement Learning, let’s explore different RL paradigms based on the type of interaction.

Different RL Techniques Based on the Type of Interaction

In this section, we will focus on the following types of Reinforcement Learning techniques based on interaction-type:

i) Online/On-policy Reinforcement Learning

ii) Off-policy Reinforcement Learning

iii) Offline Reinforcement Learning

i) Online/On-policy RL: The reinforcement learning process involves gathering experience by interacting with the environment, generally with the latest learned policy, and then using that experience to improve the policy. In online RL, the agent is free to interact with the environment and must gather new experiences with the latest policy before updating.

with streaming data collected by πk itself.

Figure 2: Diagram illustrating Online Reinforcement Learning (Source: Arxiv)

ii) Off-policy RL: In off-policy, the agent is still free to interact with the environment. However, it can update its current policy by leveraging experiences gathered from any previous policies. As a result, the sample efficiency of training increases because the agent doesn’t have to discard all of its prior interactions and can rather maintain a buffer where old interactions can be sampled multiple times.

In Figure 3, shown below, the agent interacts with the environment and appends its new experiences to a data buffer (also called a replay buffer) D. Each new policy πk collects additional data, such that D comprises samples from π0, π1, . . . , πk, and all of this data is used to train an updated new policy πk+1.

Figure 3: Diagram illustrating Off-policy Reinforcement Learning (Source: Arxiv)

iii) Offline RL/ Batch RL:  In offline RL, a behavior policy is used to gather experiences that are used to collect experiences that are stored in a static dataset. Then a new policy is learned without any further interactions with the environment. After learning an offline policy, one can opt to fine-tune their policy either via online or off-policy RL methods, with the added benefit that their initial policy is likely safer and cheaper to interact with the environment than an initial random policy.

In figure 4, the offline reinforcement learning employs a dataset D gathered by some (potentially unknown) behavior policy πβ. The dataset is collected once and is not altered during training, which makes it feasible to use large previously collected datasets. The training process doesn’t interact with the MDP, and the policy is only deployed after being fully trained.

Figure 4: Diagram illustrating Offline Reinforcement Learning (Source: Arxiv)

Caveats in Reinforcement Learning

1. The offline RL paradigm can be incredibly beneficial in settings where online interaction is impractical, either due to data collection being expensive (e.g., in healthcare, educational agents, or robotics) or dangerous (e.g., in autonomous driving, etc). Furthermore, even in domains where online interaction is viable, one might still prefer to utilize previously collected data for improved generalization in complex domains.

offline RL. Moreover, this issue is further exacerbated by the ubiquitous use of high-capacity function approximators. To navigate this, most offline RL algorithms address this issue by proposing different losses or training methods that can reduce distributional shifts.

3. After learning an offline policy, one can still opt to tune the policy online, with the added benefit that their initial policy is more likely safer and cheaper to interact with the environment than an initial random policy.

Conclusion

To sum up, in this article, we learned the following:

1. Reinforcement Learning (RL) is a general framework for adaptive control that enables an agent to le imize a specified reward signal through trial and error using feedback from its actions and experiences, i.e., actively interacting with the environment by taking actions and observing the reward.

2. In online RL, the agent is free to interact with the environment and must gather new experiences with the latest policy before updating.

3. In off-policy RL, an agent interacts with the environment and appends its new experiences to a replay buffer, which can then be sampled to update the policy. This paradigm allows for the reuse of prior experiences while relying on a steady stream of fresh ones.

4. In offline RL, a behavior policy is used to gather experiences that are used to collect experiences that are stored in a static dataset. Then a new policy is learned without any further interactions with the environment.

5. The offline RL paradigm can be incredibly beneficial in settings where online interaction is impractical due to expensive or dangerous data collection. Also, even in domains where online interaction is viable, one might still prefer to utilize previously collected data for improved generalization in complex domains.

6. After learning an offline policy, one can still opt to tune the policy online, with the added benefit that their initial policy is more likely safer and cheaper to interact with the environment than an initial random policy.

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Related

How Machine Learning Is Enhancing Mobile Gadgets And Applications

Mobile developers have a great deal to pick up from progressive changes that on-device ML can offer. This is a result of the innovation’s capacity to reinforce mobile applications—in particular, taking into consideration smoother customer experiences equipped for utilizing incredible highlights, for example, giving exact area-based recommendations or promptly detecting plant illnesses. This fast improvement of mobile machine learning has occurred as a response to various basic issues that traditional machine learning has toiled with. In truth, the composing is on the divider. Future mobile applications will require faster preparing paces and lower latency.  

Better Voice Services and Reduction in Churn Rate

Voice services are telecoms’ characteristic specialized field. A few organizations are banding together with the pioneers in speech and voice services, joining, for instance, Alexa environment. Others build up their own solutions or secure smaller new businesses. South Korean organizations are standing out. As of late, SK Telecom has presented its artificial intelligence-based voice assistant service for the home, which was a response to the move by its local rival – KT deployed its artificial intelligence collaborator to a hotel in South Korea with English language support. Machine Learning is additionally helpful in decreasing churn rates, which can average every year from 10 to as much as 67%. Telecoms can prepare algorithms to anticipate when a customer is probably going to go to another organization, and what offer could keep them from doing it.  

Smart Home Services Improved Security

Since information shouldn’t be sent to a server or the cloud for processing, cybercriminals have fewer chances to misuse any vulnerabilities in this data transference, along these lines safeguarding the sanctity of the information. This enables mobile developers to meet GDPR guidelines on data security all the more effectively. On-device ML solutions likewise offer decentralization, much similarly that blockchain does. As it were, it’s harder for hackers to bring down a connected system of concealed devices through a DDoS assault when compared with a similar assault focusing on a centralised server. This innovation could likewise demonstrate to be valuable for drones and law authorization pushing ahead. The Apple cell phone chips are likewise improving client security and privacy. For example, they fill in as the foundation of Face ID. This iPhone feature depends on an on-device neural net that gathers data on all the various ways its user’s face may look, filling in as a more precise and secure identification strategy. This and future classes of AI-empowered hardware will make ready for more secure cell phone experiences for clients, offering mobile engineers an extra layer of encryption to protect clients’ information.  

Predictive Maintenance

Mobile towers are the ideal product for ML predictive maintenance solutions. They are hard to access and require tedious on-site investigations of muddled modules, for example, power generators or air conditioners. Also, towers are powerless against interruptions, as they contain a ton of significant hardware. There are different potential uses of ML in the maintenance of mobile towers, for example, empowered surveillance, where video and picture analysis can help recognize peculiarities. The media communications infrastructure is as of now outfitted with different sensors. The information those sensors gather can be utilized for preparing ML models, which will anticipate potential disappointments. This would diminish downtime and fix expenses, and furthermore improve the coverage. Nokia utilizes ML algorithms are to change the best setup for 5G antennas and indoor positioning of articles or designing uplink and downlink channels. An active system configuration data can be cross-checked with asset management frameworks to amplify network use and improve coverage.  

Lower Costs

On-device MLI is likewise intended to spare you a fortune, as you won’t need to pay external suppliers to actualize or keep up these solutions. As recently referenced, you won’t require the cloud or the Internet for such solutions. GPUs and AI-explicit chips are the costliest cloud services you can purchase. Running models on-device implies you don’t have to pay for these clusters, because of the inexorably complex Neural Processing Units (NPUs) cell phones have nowadays. Keeping away from the overwhelming, data-processing bad dream among mobile and the cloud is an enormous cost-saver for organizations that pick on-device ML solutions. Having this on-device derivation likewise brings down data bandwidth demands, at last sparing a heavy sum in expenses.

Data Scientist Vs Machine Learning

Differences Between Data Scientist vs Machine Learning

Hadoop, Data Science, Statistics & others

Data Scientist

Standard tasks:

Allocate, aggregate, and synthesize data from various structured and unstructured sources.

Explore, develop, and apply intelligent learning to real-world data and provide essential findings and successful actions based on them.

Analyze and provide data collected in the organization.

Design and build new processes for modeling, data mining, and implementation.

Develop prototypes, algorithms, predictive models, and prototypes.

Carry out requests for data analysis and communicate their findings and decisions.

In addition, there are more specific tasks depending on the domain in which the employer works, or the project is being implemented.

Machine Learning

The Machine Learning Engineer position is more “technical.” ML Engineer has more in common with classical Software Engineering than Data Scientists. It helps you learn the objective function, which plots the inputs to the target variable and independent variables to the dependent variables.

The standard tasks of ML Engineers are generally like Data scientists. You also need to be able to work with data, experiment with various Machine Learning algorithms that will solve the task, and create prototypes and ready-made solutions.

Strong programming skills in one or more popular languages (usually Python and Java) and databases.

Less emphasis on the ability to work in data analysis environments but more emphasis on Machine Learning algorithms.

R and Python for modeling are preferable to Matlab, SPSS, and SAS.

Ability to use ready-made libraries for various stacks in the application, for example, Mahout, Lucene for Java, and NumPy / SciPy for Python.

Ability to create distributed applications using Hadoop and other solutions.

As you can see, the position of ML Engineer (or narrower) requires more knowledge in Software Engineering and, accordingly, is well suited for experienced developers. The case often works when the usual developer must solve the ML task for his duty, and he starts to understand the necessary algorithms and libraries.

Head-to-Head Comparison Between Data Scientist and Machine Learning (Infographics)

Below are the top 5 differences between Data scientists and Machine Learning:

Key Difference Between Data Scientist and Machine Learning

Below are the lists of points that describe the key Differences Between Data Scientist and Machine Learning:

Machine learning and statistics are part of data science. The word learning in machine learning means that the algorithms depend on data used as a training set to fine-tune some model or algorithm parameters. This encompasses many techniques, such as regression, naive Bayes, or supervised clustering. But not all styles fit in this category. For instance, unsupervised clustering – a statistical and data science technique – aims at detecting clusters and cluster structures without any a-prior knowledge or training set to help the classification algorithm. A human being is needed to label the clusters found. Some techniques are hybrid, such as semi-supervised classification. Some pattern detection or density estimation techniques fit into this category.

Data science is much more than machine learning, though. Data in data science may or may not come from a machine or mechanical process (survey data could be manually collected, and clinical trials involve a specific type of small data), and it might have nothing to do with learning, as I have just discussed. But the main difference is that data science covers the whole spectrum of data processing, not just the algorithmic or statistical aspects. Data science also covers data integration, distributed architecture, automated machine learning, data visualization, dashboards, and Big data engineering.

Data Scientist and Machine Learning Comparison Table

Feature Data Scientist Machine Learning

Data It mainly focuses on extracting details of data in tabular or images. It mainly focuses on algorithms, polynomial structures, and word adding.

Complexity It handles unstructured data, and it works with a scheduler. It uses Algorithms and mathematical concepts, statistics, and spatial analysis.

Hardware Requirement Systems are Horizontally scalable and have High Disk and RAM storage. It requires Graphic processors and Tensor Processors, that is very high-level hardware.

Skills Data Profiling, ETL, NoSQL, Reporting. Python, R, Maths, Stats, SQL Model.

Focus Focuses on abilities to handle the data. Algorithms are used to gain knowledge from huge amounts of data.

Conclusion

Machine learning helps you learn the objective function, which plots the inputs to the target variable and independent variables to the dependent variables.

A Data scientist does a lot of data exploration and arrives at a broad strategy for tackling it. He is responsible for asking questions about the data and finding what answers one can reasonably draw from the data. Feature engineering belongs to the realm of Data scientists. Creativity also plays a role here, and An Machine Learning engineer knows more tools and can build models given a set of features and data – as per directions from the Data Scientist. The realm of Data preprocessing and feature extraction belongs to ML engineers.

Data science and examination utilize machine learning for this archetypal validation and creation. It is vital to note that all the algorithms in this model creation may not come from machine learning. They can arrive from numerous other fields. The model desires to be kept relevant always. If the situations change, the model we created earlier may become immaterial. The model must be checked for certainty at different times and adapted if its confidence reduces.

Data science is a whole extensive domain. If we try to put it in a pipeline, it would have data acquisition, data storage, data preprocessing or cleaning, learning patterns in data (via machine learning), and using knowledge for predictions. This is one way to understand how machine learning fits into data science.

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How Machine Learning Improves Cybersecurity?

Here is how machine learning improves cybersecurity

Today, deploying robust cybersecurity solutions is unfeasible without significantly depending on machine learning. Simultaneously, without a thorough, rich, and full approach to the data set, it is difficult to properly use machine learning. MI can be used by cybersecurity systems to recognise patterns and learn from them in order to detect and prevent repeated attacks and adjust to different behaviour. It can assist cybersecurity teams in being more proactive in preventing dangers and responding to live attacks. It can help businesses use their assets more strategically by reducing the amount of time invested in mundane tasks.  

Machine Learning in Cyber Security

ML may be used in different areas within Cyber Security to improve security procedures and make it simpler for security analysts to swiftly discover, prioritise, cope with, and remediate new threats in order to better comprehend previous cyber-attacks and build appropriate defence measures.  

Automating Tasks

The potential of machine learning in cyber security to simplify repetitive and time-consuming processes like triaging intelligence, malware detection, network log analysis, and vulnerability analysis is a significant benefit. By adding machine learning into the security workflow, businesses may complete activities quicker and respond to and remediate risks at a rate that would be impossible to do with only manual human capabilities. By automating repetitive operations, customers may simply scale up or down without changing the number of people required, lowering expenses. AutoML is a term used to describe the process of using machine learning to automate activities. When repetitive processes in development are automated to help analysts, data scientists, and developers be more productive, this is referred to as AutoML.  

Threat Detection and Classification

In order to identify and respond to threats, machine learning techniques are employed in applications. This may be accomplished by analysing large data sets of security events and finding harmful behaviour patterns. When comparable occurrences are recognised, ML works to autonomously deal with them using the trained ML model. For example, utilising Indicators of Compromise, a database to feed a machine learning model may be constructed (IOCs). These can aid in real-time monitoring, identification, and response to threats. Malware activity may be classified using ML classification algorithms and IOC data sets. A study by Darktrace, a Machine Learning based Enterprise Immune Solution, alleges to have stopped assaults during the WannaCry ransomware outbreak as an example of such an application.  

Phishing

Traditional phishing detection algorithms aren’t fast enough or accurate enough to identify and distinguish between innocent and malicious URLs. Predictive URL categorization methods based on the latest machine learning algorithms can detect trends that signal fraudulent emails. To accomplish so, the models are trained on characteristics such as email headers, body data, punctuation patterns, and more in order to categorise and distinguish the harmful from the benign.  

WebShell

WebShell is a malicious block of software that is put into a website and allows users to make changes to the server’s web root folder. As a result, attackers have access to the database. As a result, the bad actor is able to acquire personal details. A regular shopping cart behaviour may be recognised using machine learning, and the system can be programmed to distinguish between normal and malicious behaviour.  

Network Risk Scoring

Quantitative methods for assigning risk rankings to network segments aid organisations in prioritising resources. ML may be used to examine prior cyber-attack datasets and discover which network regions were more frequently targeted in certain assaults. With regard to a specific network region, this score can assist assess the chance and effect of an attack. As a result, organisations are less likely to be targets of future assaults. When doing company profiling, you must determine which areas, if compromised, can ruin your company. It might be a CRM system, accounting software, or a sales system. It’s all about determining which areas of your business are the most vulnerable. If, for example, HR suffers a setback, your firm may have a low-risk rating. However, if your oil trading system goes down, your entire industry may go down with it. Every business has its own approach to security. And once you grasp the intricacies of a company, you’ll know what to safeguard. And if a hack occurs, you’ll know what to prioritise.  

Human Interaction

Computers, as we all know, are excellent at solving complex problems and automating things that people might accomplish, but which PCs excel at. Although AI is primarily concerned with computers, people are required to make educated judgments and receive orders. As a result, we may conclude that people cannot be replaced by machines. Machine learning algorithms are excellent at interpreting spoken language and recognising faces, but they still require people in the end.  

Conclusion

Machine learning is a powerful technology. However, it is not a magic bullet. It’s crucial to remember that, while technology is improving and AI and machine learning are progressing at a rapid pace, technology is only as powerful as the brains of the analysts who manage and use it.

Machine Learning Vs Predictive Analytics

Difference Between Machine Learning and Predictive Analytics

Hadoop, Data Science, Statistics & others

Machine Learning

Machine learning internally uses statistics, mathematics, and computer science fundamentals to build logic for algorithms that can do classification, prediction, and optimization in both real times as well as batch mode. Classification and Regression are two main classes of a problem under machine learning. Let’s understand both Machine Learning and Predictive Analytics in detail.

Classification

Under these buckets of a problem, we tend to classify an object based on its various properties into one or more classes. For example, classifying a bank customer to be eligible for a home loan or not based on his/her credit history. Usually we would have transactional data available for the customer like his age, income, educational background, his work experience, industry in which he is working, number of dependents, monthly expenses, previous loans if any, his spending pattern, credit history, etc. and based on this information we would tend to calculate if he should be given loan or not.

To measure the accuracy of regression models, metrics like false positive rate, false-negative rate, sensitivity, etc. are used.

Regression is another class of problems in machine learning where we try to predict the continuous value of a variable instead of a class unlike in classification problems.  Regression techniques are generally used to predict the share price of a stock, sale price of a house or car, a demand for a certain item, etc. When time-series properties also come into play, regression problems become very interesting to solve. Linear regression with ordinary least square is one of the classic machine learning algorithms in this domain. For time series based pattern, ARIMA, exponential moving average, weighted moving average, and simple moving average are used.

To measure the accuracy of regression models, metrics like to mean square error, absolute mean square error, root measure square error, etc. are used.

Predictive Analytics

A predictive analyst mostly uses tools like excel. Scenario or goal seek are their favourite. They occasionally use VBA or micros and hardly write any lengthy code. A machine learning engineer spends all his time writing complicated code beyond common understanding, he uses tools like R, Python, Saas. Programming is their major work, fixing bugs and testing on the different landscapes a daily routine.

Head to Head Comparison between Machine Learning and Predictive Analytics (Infographics)

Below is the top 7 Comparision between Machine Learning and Predictive Analytics:

Machine Learning and Predictive Analytics Comparison Table

Below is the detailed explanation of Machine Learning and Predictive Analytics.

Machine Learning Predictive Analytics

It is an overall term encompassing various subfields including predictive analytics. It can be treated as a subfield of machine learning.

Heavily coding oriented. Mostly standard software-oriented where a user need not code much themselves

It is considered to be generated from computer science i.e. computer science can be treated as the parent here. Statistics can be treated as a parent here.

It is the technology of tomorrow. It is so yesterday.

It is a machine dominated by many techniques that are hard to understand but work like charm like deep learning. It is user dominated with techniques that must be intuitive for a user to understand and implement.

Tools like R, Python, SaaS are used. Excel, SPSS, Minitab are used.

It is very broad and continuously expanding. It has a very limited scope and application.

Conclusion

From the above discussion on both Machine Learning vs Predictive Analytics, it is clear that predictive analytics is basically a sub-field of machine learning. Machine learning is more versatile and is capable to solve a wide range of problems.

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