Machine Learning with Python vs Big Data Which one is best !
Difference between Big Data and Machine Learning
Big Data: It is huge, large or voluminous data, information, or the relevant statistics acquired by the large organizations and ventures. Many software and data storage created and prepared as it is difficult to compute the big data manually. It is used to discover patterns and trends and make decisions related to human behavior and interaction technology.
Machine Learning: Machine learning is a subset of artificial intelligence that helps to automatically learn and improve the system without being explicitly programmed. Machine learning is applied using Algorithms to process the data and get trained for delivering future predictions without human intervention. The inputs for Machine Learning is the set of instructions or data or observations.
Now we know what Big Data vs Machine Learning are, but to decide which one to use at which place we need to see the difference between both.
Head to Head Comparison between Big Data and Machine Learning
Both data mining and machine learning are rooted in data science. They often intersect or are confused with each other. They superimpose each other’s activities and the relationship is best described as mutualistic. It is impossible to see a future with just one of them. But there are still some unique identities that separate them in terms of definition and application. Here’s a look at some of the differences between big data and machine learning and how they can be used.
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| Machine Learning with Python vs Big Data |
- Usually, big data discussions include storage, ingestion & extraction tools commonly Hadoop. Whereas machine learning is a subfield of Computer Science and/or AI that gives computers the ability to learn without being explicitly programmed.
- Big data analytics as the name suggest is the analysis of big data by discovering hidden patterns or extracting information from it. So, in big data analytics, the analysis is done on big data. Machine learning, in simple terms, is teaching a machine how to respond to unknown inputs and give desirable outputs by using various machine learning models.
- Though both big data and machine learning can be set up to automatically look for specific types of data and parameters and their relationship between them big data can’t see the relationship between existing pieces of data with the same depth that machine learning can.
- Normal big data analytics is all about extracting and transforming data to extract information, which then can be used to fed to a machine learning system in order to do further analytics for predicting output results.
- Big data has got more to do with High-Performance Computing, while Machine Learning is a part of Data Science.
- Machine learning performs tasks where human interaction doesn’t matter. Whereas, big data analysis comprises the structure and modeling of data which enhances decision-making system so require human interaction.
The added dimensions of Big Data
According to the “3D Data Management: Controlling Data Volume, Velocity and Variety”, besides the three big V’s there are two more factors which most people include when defining the value of Big Data in computing and analysis.
i. Complexity: data comes from more than one source; to simply weed through the influx of data to link, match and transform it across systems is an elephantine task. It is necessary to deduce a connection and hierarchy between multiple data sets. The lack of such organization will cause torrents of incoming data to flood your system and memory without any valid utility.
ii. Variability: data flow rates are never uniform. Just like the widely varying sources of data, the flow rate can be highly inconsistent. From trending issues in social media to sales and promotion in ecommerce sites, anything and everything can affect data flow. Because of unstructured data, management of unpredictable data flow rates becomes challenging. Hence big data has multiple sources, including the following-
• Social Media: all kinds of data that reaches IT systems from a network of connected devices.
• Streaming Data: social interactions like trending topics, sales and marketing online, support functions - everything generates unstructured data.
• Public Sources: the CIA World Factbook and European Union Open Data Portal are the most common ones.
What can machine learning do?
• Fraud detection
• Real-time ads on mobile devices and WebPages
• Credit scoring and best offer ads
• Web search results
• New pricing models
• Email spam filtering
• Prediction of equipment failure
• Detection of network intrusion(s)
• Image recognition
• Pattern recognition
• Sentiment analysis from texts
How does machine learning pull all that off?
Machine learning methods can vary according to the necessities of an organization or depending upon the big data methods. Most common methods include supervised, unsupervised, semi-supervised and reinforcement learning. A huge part of the most common procedures is supervised learning (70 percent). The algorithms of supervised learning are trained using labels like R (runs) and F (failed). The algorithm receives inputs based on the actual output which trains the model to find errors. The model has the scope to evolve and modify accordingly. Such an algorithm is a common example of how systems are trained to anticipate fraudulent transactions on credit cards or when insurance policy owners are most likely to file a claim with the insurance office.
The below Venn diagram shows the relationship with that of Big Data and Machine Learning along with their related fields.
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| Data Science vs Machine Learning |
Big data gives the immense opportunity for machine learning to evolve and adapt to the everyday requirements of data analysis and organization. Machine learning overcomes the limitations of human thinking; as a result, this process works effectively for mining valuable data from the cornucopia created by big data without any bias. Machine learning is the most effective way to excavate buried patterns in the chunks of unstructured data, collected from everyday activities and transactions. They superimpose each other’s activities and the relationship is best described as mutualistic.
It is Below is a table of differences between Big Data and Machine Learning:
| BIG DATA | MACHINE LEARNING |
|---|---|
| Big Data is more of extraction and analysis of information from huge volumes of data. | Machine Learning is more of using input data and algorithms for estimating unknown future results. |
| Types of Big Data are Structured, Unstructured and Semi-Structured. | Types of Machine Learning Algorithms are Supervised Learning and Unsupervised Learning, Reinforcement Learning. |
| Big data analysis is the unique way of handling bigger and unstructured data sets using tools like Apache Hadoop, MongoDB. | Machine Learning is the way of analysing input datasets using various algorithms and tools like Numpy, Pandas, Scikit Learn, TensorFlow, Keras. |
| Big Data analytics pulls raw data and looks for patterns to help in stronger decision-making for the firms | Machine Learning can learn from training data and acts like a human for making effective predictions by teaching itself using Algorithms. |
| It’s very difficult to extract relevant features even with latest data handling tools because of high-dimensionality of data. | Machine Learning models work with limited dimensional data hence making it easier for recognizing features |
| Big Data Analysis requires Human Validation because of large volume of multidimensional data. | Perfectly built Machine Learning Algorithms does not require human intervention. |
| Big Data is helpful for handling different purposes including Stock Analysis, Market Analysis, etc. | Machine Learning is helpful for providing virtual assistance, Product Recommendations, Email Spam filtering, etc. |
| The Scope of Big Data in the near future is not just limited to handling large volumes of data but also optimizing the data storage in a structured format which enables easier analysis. | The Scope of Machine Learning is to improve quality of predictive analysis, faster decision making, more robust, cognitive analysis, rise of robots and improved medical services. |
In Short:
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| Overview |
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