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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.


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.


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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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.


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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The Difference Between Data Scientist Vs Machine Learning Scientist

Detail analysis of the career differences among data scientists and machine-learning scientists. You can learn AI skills now, whether you are just starting out or recently laid off.

Today we’ll learn more about the differences in career between Data Scientists & Machine Learning Scientists

What is Data Science?

Data science is the in-depth analysis of huge amounts of data stored in an organization’s or company’s archive. This includes analyzing the data’s origin and quality, as well as determining if it can be used for future corporate development.

Data scientists specialize in the transformation of unstructured data into business information. These experts are familiar with algorithms, data processing, artificial intelligence, statistics, and other forms of programming.

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What’s Machine Learning?

Machine learning is a branch of computer science that allows computers to learn by themselves without needing to be programmed.

Machine learning is the use of algorithms to analyze data and make predictions, without the involvement of humans. Machine Learning relies on a series of instructions, information, or observations as inputs. Machine learning is used extensively by companies like Facebook, Google, etc.

The Difference between Data Scientists & Machine Learning Scientists

These jobs may seem similar to recruiters. However, if you’re a specialist in one of these areas, you will know that there’s a difference. Both professions depend on machine learning algorithms but their day-to-day tasks may be quite different.

Machine learning scientists specialize in use cases such as signal processing, object identification, automobile/self-driving, and robots, whereas data scientists work on use factors like fraud detection, product categorization, or customer segmentation.

Data Scientists

Data scientists may have more standard job descriptions. They might also be required to learn the skills and education that they need.

A data scientist is expected to identify a problem and create a dataset. Then, they will evaluate machine learning algorithms, produce results, analyze those results, and then communicate the results with stakeholders. Data scientists are focused on business and stakeholder collaboration.

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Data scientists can expect to get the following education and skills.


BS or MS degree oriented

Data Science


Business Analytics


Python or R

Data Analysis


Jupyter Notebook



Model Building

Data scientists are often able to use code in Python or R to automate projections using machine-learning tools.

There may be a different path to becoming either a data scientist or a machine-learning scientist. For example, a data scientist may have worked as a statistician, business analyst, data analyst, or business intelligence analyst before becoming one.

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Machine Learning Scientists

Machine learning scientists are, however, more focused on algorithms and the software engineering involved in implementing them. Machine learning scientists often use the term “research” in their titles.

This means that you need to spend more time learning algorithms before creating a simpler method. These positions might be identical at different companies, so it is up to you to spot the differences when you read job descriptions.

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The following are some of the variations in education and abilities required:


degree oriented

Machine Learning

Computer Science






Signals & Distributed Systems


C++ or C

Quality Assurance


Model Deployment


Artificial Intelligence


Data science is an interdisciplinary field that draws insights from large amounts of data and high computing power. Machine learning is one of the most exciting developments in modern data science.

Machine learning allows machines to learn from large amounts of data and operate independently. Although these technologies have many applications, they do not come without their limitations. Data science is powerful but can only be used to its full extent if there are skilled workers and high-quality data.

Can I Use Predictive Analytics To Predict The World Cup Winner?

Predictive Analytics is the future of marketing So, I agree with the general thrust of one of those.

The results. The results are phenomenal. They really are startling – to the extent that I’m looking at our client’s results and going back to our Head of Insight because there’s clearly a mis-type here (there isn’t).  But if we take the potential success as a given, why are so few marketers investing in and committing to AI-Driven Predictive Analytics? Well speaking to my fellow marketers, the same phrases keep cropping up: ‘we can’t really implement the full set of predictive models yet, so we’ll hang on a bit’; ‘GDPR’s been a bit of a nightmare’; ‘I’m not sure we’ve got the data, it’s all over the place’; ‘it all seems really complex’.

But why complex? We predict all the time using the data available to us.

I predict my partner will want to watch the golf this weekend as opposed to visiting the cinema with me. How? Well, I’m looking at his previous tv preferences (past behaviour), the fact he’s been looking at the TV guide to see what’s on (current behaviour), and extra contextual information I hold (he’s a keen golfer himself).

So I can predict, with a high degree of certainty, his likely action this weekend. This is not the action I want. I want to go to the cinema. I want to see Oceans 8 (don’t judge). I want to change his likely action.

How? Well I need to market it to him with the right message. This is a message I can convincingly craft because I know his likely action. So I will wax lyrical about the film. I’ll remind him Sandra Bullock’s in it (don’t ask), maybe add an incentive like dinner beforehand and remind him that the last time he watched the US Open golf he fell asleep overnight on the sofa and got a crick in his neck.

I’ve predicted his likely action and then used my marketing skills to modify this to the result I want to see.

Now let’s visualise our marketing database and apply the same logic.

If we focus on an area of the customer journey where we’re struggling to realise the maximum potential, we can see how a predictive model can help. Let’s say I want to convert more single purchasers into multi-purchasers. After all multi-purchasers only account for 8% of the average customer database, but generate 40% of the value. This is an area we want to grow! Following on from my homelife analogy above, we’re going to analyse the following:

Past behaviour

Recent behaviour

Any other useful factors (for example, is this a model where demographic, contextual or location data might play a part?)

What behaviours are we interested in? Well chatting to our Head of Insight there’s 100-150 fields that are considered in every model. Now thankfully these models are built in to our Marketing Automation technology, so no one is literally having to sift 100 plus fields. But anything that can make our predictions more accurate is given consideration. One of the other marketer concerns was that they didn’t have enough of the right kind of data. Quoting our Head of Insight again, you can start with what you have! Work out what you need to know more about and then work from there. She’s also written a great blog on when Predictive is a good idea – you can read it here.

Anyway, back to our mission to increase our multi-purchasers. Our model has combined past behaviours, current behaviours and any other useful information to form two groups. Likely to become a multi-purchaser and unlikely to become a multi-purchaser. Easy, right? This is the time to use your marketer super-hero skills and craft two messages to specifically target those two groups of customers. Obviously, the group who are likely to become multi-purchasers are a slightly easier proposition than those who aren’t. However, you’re not shooting in the dark here. You know they’re the difficult group, unlikely, at the moment, to shop with you again. So how do you get them back? You know your brand, you know what works, you know what messages have real impact – and now you know exactly who to send it to! Bingo!

So there we have it. The best models will echo the customer journey, following customer acquisition through to multi-purchase, VIP, unsubscribe and churn reduction. The idea is that your customers will move automatically through all of these models in time – leaving you to hone the messaging, strategy and add value in every way without faffing with the database.

Before I go I owe you one more prediction. And what are betting odds but another form of prediction looking at exactly what we’ve just discussed? A combination of past behaviour, current behaviour, and other relevant factors to predict a likely outcome!

Go for Brazil. Currently sitting at 4/1*. You’re welcome.

*Disclaimer – as of 1630 20/6/18 – don’t gamble your mortgage on it…

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.  


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 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.  


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.

Is Reinforcement (Machine) Learning Overhyped?

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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