The Intersection of Big Data and Machine Learning: A Guide to Big Data Analytics

In this newsletter, I take a deeper dive into the important role that big data plays in machine learning.

Key Machine Learning Components

Machine learning requires the following four key components:

1. An input device to bring data from the outside world into the machine in a digital format.
2. One or (usually more) powerful computer processors running in parallel.
3. A number of machine learning algorithms that run on those processors. (An algorithm is a process or set of rules to be followed in calculations or other problem-solving operations.) The combination of processing power and algorithm constitutes an artificial neuron — the smallest unit in an artificial neural network. These neurons must be arranged in layers — an input layer, one or more hidden layers, and an output layer.
4. Data sets that the machine can use to identify patterns in the data. The more high-quality data the machine has, the more it can fine-tune its ability to identify patterns and anything that diverges from those patterns.

Three Types of Machine Learning

Machines learn in the following three ways:

1. Supervised learning: With supervised learning, a trainer feeds a set of labeled data into the computer. This enables the computer to then identify patterns in that data and associate them with the labels provided. For example, a trainer may feed in photographs of 20 cats and tell the machine, "These are cats." She then feeds in 20 photos of dogs and tells the machine, "These are dogs." Finally, she feeds the machine 20 random photographs of dogs and cats without telling the machine whether each picture is of a dog or a cat. When the machine makes an error, the trainer corrects it, so the neural network can tune itself to greater accuracy.
2. Unsupervised learning: With unsupervised learning, data that is neither classified nor labeled is fed into the system. The system then identifies hidden patterns in the data that humans may be unable to detect or may have overlooked. Unsupervised learning is primarily used for clustering. For example, you'd feed in 100 photos of animals and tell the machine to divide them into five groups. The machine then looks for matching patterns in the photos and creates five groups based on similarities and differences in the photos. These may be groups a human would recognize, such as cat, dog, snake, octopus, and elephant, or they may be groups you would never imagine, such as snakes, dogs, and cats all being in the same group because the neural network focused on the cat and dog tails instead of other features.
3. Semi-supervised learning: This is a cross between supervised and unsupervised learning. Supervised learning is used initially to train the system on a small data set, then a large amount of unlabeled data is fed into the system to increase its accuracy.

Big Data with Machine Learning

As you can see, data is important for machine learning, but that is no surprise; data also drives human learning and understanding. Imagine trying to learn anything while floating in a deprivation tank; without sensory, intellectual, or emotional stimulation, learning would cease. Likewise, machines require input to develop their ability to identify patterns in data.

The availability of big data (massive and growing volumes of diverse data) has driven the development of machine learning by providing computers with the volume and types of data they need to learn and perform specific tasks. Just think of all the data that is now collected and stored — from credit and debit card transactions, user behaviors on websites, online gaming, published medical studies, satellite images, online maps, census reports, voter records, financial reports, and electronic devices (machines equipped with sensors that report the status of their operation). 

This treasure trove of data has given neural networks a huge advantage over the physical-symbol-systems approach to machine learning. Having a neural network chew on gigabytes of data and report on it is much easier and quicker than having an expert identify and input patterns and reasoning schemas to enable the computer to deliver accurate responses (as is done with the physical symbol systems approach to machine learning).

The Evolution of Machine Learning

In some ways, the evolution of machine learning is similar to how online search engines developed over time. Early on, users would consult website directories such as Yahoo! to find what they were looking for — directories that were created and maintained by humans. Website owners would submit their sites to Yahoo! and suggest the categories in which to place them. Yahoo! personnel would then review the user recommendations and add them to the directory or deny the request. The process was time-consuming and labor-intensive, but it worked well when the web had relatively few websites. When the thousands of websites proliferated into millions and then crossed the one billion threshold, the system broke down fairly quickly. Human beings couldn’t work quickly enough to keep the Yahoo! directories current.

 In the mid-1990s Yahoo! partnered with a smaller company called Google that had developed a search engine to locate and categorize web pages. Google’s first search engine examined backlinks (pages that linked to a given page) to determine each page's relevance and relative importance. Since then, Google has developed additional algorithms to determine a page’s rank; for example, the more users who enter the same search phrase and click the same link, the higher the ranking that page receives. With the addition of machine learning algorithms, the accuracy of such systems increases proportionate to the volume of data they have to draw on.

 So, what can we expect for the future of machine learning? The growth of big data isn't expected to slow down any time soon. In fact, it is expected to accelerate. As the volume and diversity of data expand, you can expect to see the applications for machine learning grow substantially, as well.

Frequently Asked Questions

What is the significance of using big data and machine learning in data analytics?

Big data means a huge amount of data created every day. Machine learning helps us look at this data and find useful information. When we use both together, we can quickly understand big and complicated datasets. This makes it easier to see patterns and learn new things.

How does one apply machine learning to big data?

To apply machine learning to big data, data scientists often use specialized algorithms and frameworks that can handle large datasets.

This process involves data preprocessing, selecting the right machine learning techniques, training the model with a large amount of data, and then validating it to ensure accuracy and reliability.

What role does Google Cloud play in big data analytics?

Google Cloud offers multiple services and tools for big data analytics, such as:

• BigQuery for data warehousing and analysis
• Google Cloud AI for building and deploying machine learning models
• Dataproc for processing large datasets. 

These tools help businesses utilize the power of big data effectively and make informed decisions.

What are the common challenges in integrating big data and machine learning?

Integrating big data and machine learning poses several challenges, including:

• Data quality issues
• Handling unstructured data
• Ensuring efficient data processing
• The need for substantial computational resources.

Properly addressing these challenges is crucial to achieving accurate and useful insights.

How does artificial intelligence relate to big data analysis?

Artificial intelligence (AI) plays a key role in big data analysis by providing advanced methods for data interpretation.

Machine learning, a subset of AI, is used to detect patterns in data, make predictions, and automate decision-making processes based on the analysis of large datasets.

Can traditional data processing methods be used with big data?

Traditional data processing methods often fall short when handling the enormous scale and complexity of big data.

Modern data processing techniques and tools, such as distributed computing and parallel processing, are typically required to efficiently manage and analyze large volumes of data.

What are some real-world Machine learning applications for big data?

Real-world applications of machine learning for big data include:

• Recommendation systems
• Fraud detection
• Predictive maintenance
• Personalized marketing
• Healthcare analytics

By leveraging big data, these applications can provide more accurate and actionable insights.

What is the difference between deep learning and reinforcement learning in the context of big data?

Deep learning and reinforcement learning are both subsets of machine learning but differ in their approaches.

Deep learning uses neural networks to learn from large amounts of data, while reinforcement learning involves training agents to make decisions by rewarding desirable behaviors.

Both methods are useful for different types of big data analysis tasks.

How can businesses use data analytics to gain a competitive advantage?

Businesses can gain a competitive advantage by using data analytics to uncover insights into:

• Customer behavior
• Optimize operations
• Improve marketing strategies
• Drive innovation

By efficiently analyzing large datasets, companies can make data-driven decisions that enhance performance and growth.

This is my weekly newsletter that I call The Deep End because I want to go deeper than results you’ll see from searches or LLMs. Each week I’ll go deep to explain a topic that’s relevant to people who work with technology. I’ll be posting about artificial intelligence, data science, and data ethics.

This newsletter is 100% human written 💪 (* aside from a quick run through grammar and spell check).

More sources

1. https://www.geeksforgeeks.org/unsupervised-neural-network-models/
2. https://www.altexsoft.com/blog/unsupervised-machine-learning/
3. https://christophm.github.io/modeling-mindsets/unsupervised-ml.html
4. https://towardsdatascience.com/unsupervised-learning-and-data-clustering-eeecb78b422a
5. https://clanx.ai/glossary/unsupervised-learning
6. https://www.freecodecamp.org/news/8-clustering-algorithms-in-machine-learning-that-all-data-scientists-should-know/
7. https://www.cliffsnotes.com/study-notes/14620793
8. https://www.ibm.com/think/topics/supervised-vs-unsupervised-learning
9. https://www.simplilearn.com/tutorials/machine-learning-tutorial/supervised-and-unsupervised-learning
10. https://www.geeksforgeeks.org/clustering-in-machine-learning/
11. https://stackoverflow.com/questions/34518656/how-to-interpret-loss-and-accuracy-for-a-machine-learning-model
12. https://datascience.stackexchange.com/questions/16298/neural-network-accuracy-and-loss-guarantees
13. https://www.datacamp.com/blog/introduction-to-unsupervised-learning
14. https://blog.invgate.com/ai-vs-machine-learning-vs-deep-learning-vs-neural-networks
15. https://www.ibm.com/topics/unsupervised-learning