Machine learning is probably not new to you unless you’ve been on a desert island without phones or internet for the last several years. It was impossible to ignore this trend. Whether it’s AlphaGo, chatbots, self-driving cars, or predictive analytics, machine learning is always brought up. Machine learning has plenty of advocates and success stories, but companies still don’t consider it a must-have. There is still a significant hurdle to overcome before ML can be widely adopted, and the public views the algorithms used in ML as almost science fiction.
This article isn’t meant to offer a vision or promote a trend; rather, it’s meant to give practical answers. We will go over the various subfields that make up data science, how they relate to one another, the most pressing issues that machine learning can address, and how to articulate these issues in terms that businesses can understand. Additionally, we will go over the important choices you’ll have to make when recruiting experts, as well as the obstacles that should be considered beforehand.
- Definitions of key terms used in data science
Machine learning as a concept emerged during the 1950s, a watershed period in artificial intelligence. The now-famous Turing test was first proposed by Alan Turing in his 1950 paper “Computing Machines and Intelligence.” It is used to evaluate the quality of artificial intelligence. The term “machine learning” was first used in 1959 by Arthur Lee Samuel. Many of the theoretical advancements that are used today were made during this time. Then why is data science and machine learning being discussed so much these days?
Computing power and the amount of data we can gather and analyze now are probably the most significant differences between then and now. More data can be stored and processed by a tiny, portable smartphone than by a massive, room-consuming mainframe from the 1960s. Instead of using tiny, hand-picked datasets, we now train algorithms and make predictions using massive, unordered datasets with thousands of parameters. The amount and quality of data used by contemporary machine learning methods also differentiates them from statistics. While traditional statistical methods use a small set of variables to draw conclusions, machine learning makes good use of thousands of data features.
Here we’ll go over the data science industries and the connections between them.
chapter one. Information analyst
In the 1960s, the phrase “data science” was first used. John Forman, chief data scientist at MailChimp, coined the term “business-oriented” from among many others:
“Insights, solutions, and products that are valuable are created through the transformation of data using mathematics and statistics.”
Data scientists become more proficient with new “tools” as time goes on, but discovering patterns and insights in data is still fundamental to any successful company. Data science now aids in the resolution of numerous analytical issues and finds applications in numerous sectors. For instance, in marketing, it is possible to create highly targeted campaigns by studying buyer behavior, age, gender, and location. This research estimates the number of buyers who are likely to make a purchase. One way to spot fraudulent activity in the banking industry is to keep an eye out for suspicious client activity. For example, healthcare providers can learn about a patient’s disease risk by analyzing their medical records.
Data science is an interdisciplinary discipline that draws from a wide range of disciplines and tool sets.
section 1.2. Exploring database data for insights and new information
Data mining is the foundational set of practices in data science, as shown in the diagram, thus it is related to all areas of data science. Data mining is an inaccurate and misleading term. Instead of physically mining the data, we build algorithms to sift through massive amounts of data, most of which is unstructured, and find the insights that matter. Gathering existing data and transforming it into patterns that are easy to process is the main goal of data mining. Knowledge Discovery in Databases (KDD), first proposed by Gregory Piatetsky-Shapiro in 1984, is a broader process that includes data mining.
Data mining and KDD on their own may appear to address the primary issue, but it is machine learning that genuinely generates value for businesses.
section 1.3. Artificial intelligence
Data mining and the increasingly popular machine learning are not the same thing. Algorithms for extracting valuable insights are also a focus of machine learning, which actively involves tuning, retraining, and modifying algorithms based on past experience in order to continuously exploit a dynamically changing environment. Machine learning, on the other hand, aims to learn from fresh data by spotting previously unseen patterns or rules. In some cases, programming and human intervention are not necessary for its implementation.
Machine learning has surpassed all others in terms of rate of theoretical and technological advancement. Thanks to these innovations, new capabilities like image recognition and natural language processing have emerged, and machines can now even create their own music, images, and text. The primary “tool” for developing AI is still machine learning.
section 1.4. Machine learning
When it comes to data science, the term “artificial intelligence” (AI) is among the most nebulous. Not to mention that he keeps to himself. A primary goal of artificial intelligence research is to develop a system that can mimic human intelligence in terms of thought and reasoning via the application of pattern recognition and machine learning. The lack of a consensus on a definition of intelligence is largely attributable to the nebulous nature of the terms used. Intelligence is a complex concept with multiple competing definitions, making it challenging to pin down. The capacity to find novel solutions to existing problems is one definition of AI used in business contexts. Perception, generalization, reasoning, and judgment are the basic building blocks of problem-solving.
The general public typically thinks of artificial intelligence as computers’ capacity to answer questions across many domains of study. They resemble humans to a certain extent because of this. But strong artificial general intelligence (AGI) is still far from a reality that reflects the state of technology today. In recent times, famous systems like Libratus, AlphaGo, and IBM Watson have demonstrated artificial narrow intelligence (ANI) by defeating humans at Texas Hold ’em. His expertise lies in a particular field, and he is able to complete tasks that pertain to data processing in a similar vein. In other words, data scientists still need to advance artificial general intelligence (AGI) beyond artificial neural networks (ANI), but this advancement is highly improbable to occur in the next few decades. The increasing concern that machines will eventually replace humans in most occupations is partially warranted, but the world as we know it is still a long way off.
Section 1.5. “Big data”
A lot of people have the wrong idea about what “big data” is and how it works. More and more businesses are going digital, which means they can gather bigger datasets with all sorts of information about customers, employees, and company resources, most of which isn’t structured. Almost anything that can be monitored electronically or by hand falls under this category, including demographics, interactions and behaviors, endpoints, and so on. On the other hand, big data does not yet include such unstructured datasets.
“Collection is not understanding”—Sean McClure, Director of Data Science at Space-Time Insight.
It is not guaranteed that valuable patterns will be found in massive amounts of data. The idea behind big data is to use data mining and machine learning methods to discover patterns in massive datasets.
The current focus on big data begs the question: why? The increase in processing power is the main reason why big data is so popular among tech advocates. We can handle all the raw data, give high accuracy, and discover more hidden dependencies than if we used limited subsets of data to detect and extrapolate results across the whole domain. To achieve this goal, one must acquire the necessary skills, knowledge, and infrastructure to manage massive amounts of unstructured data, as well as the means to effectively visualize and derive insights from this data.
- Detailed procedure for machine learning
How can we train algorithms to recognize meaningful trends in data? The capacity to process data without directly programming is the key differentiator between machine learning and traditionally programmed algorithms. Because of this, the engineer can save time by not having to give the machine specific instructions for processing different kinds of data records. Based on the data input, the machine decides on these rules automatically.
The general process stays the same and is iterated upon as needed to improve accuracy or replace out-of-date results in any machine learning application. You will be introduced to the fundamental ideas of machine learning in this section.
A mathematical model describing the algorithm’s processing of new data after training on a subset of historical data is the main output of any machine learning process. A model that can formulate the desired value (attribute)—some unknown value for each data object—is what we aim for during training. Despite how it sounds, it’s actually quite simple.
If you run an online store, for instance, you need to know if customers will stay or go. We need these buy/leave predictions as search attributes.
There are typically only a few basic steps involved in the process:
Collecting data. To create a dataset, you gather as much relevant information as you can from various sources, including your digital infrastructure.
Get your data ready. The goal of data cleansing and preprocessing, which can be intricate, is to fix errors and fill in missing values. For instance, if two columns contain the same value, such as “14/12/2016” and “14/14/2016,” the algorithm will interpret them differently.
Data segmentation. Data partitioning allows for more precise model training and subsequent evaluation of accuracy with fresh data.
Instruction for the model. Having an algorithm look for patterns in a subset of the historical data.
Model testing and validation. Find out how accurate the model is at making predictions by testing and validating it with subsets of the historical data.
Implementation of the model. Including the validated model in a decision-making framework for analysis or so users can make use of its features (like better product recommendation targeting, for instance).
Stuffing oneself silly. Adding more data after running the model to make small improvements.
- Five categories of issues addressed by ML
From an organizational standpoint, machine learning addresses numerous challenges; however, when looking at the bigger picture, the issues that algorithms address can be broadly categorized into five groups: generation, classification, cluster analysis, regression, and ranking.
Chapter 3.1. Classification
Classification algorithms sort dataset objects into predetermined categories. Typically, classes are used to describe categories. Numerous questions can be answered by resolving classification problems.
Classification issues with two variables:
How likely is it that this lead will bring in a sale?
Could this be considered spam?
Could this be a fraudulent transaction?
Classification in binary form
Additional types of assignments include:
Could you tell me if this apartment is in Boston, San Francisco, or New York?
Can you tell me if this is a bird, a dog, or a cat?
Is this buyer more likely to purchase a desktop computer, a laptop, or a smartphone?
Anomaly detection is another very precise form of classification. Since the objective of anomaly detection is to identify outliers—irregular objects in the data that do not follow a normal distribution—it is commonly referred to as one-class classification. To what extent is this useful for resolving issues?
Does our dataset contain any unusual customers?
Are our bank customers likely to act in an unusual way?
Does the patient’s medical history set them apart from others?
Chapter 3.2. Analysis of clusters
The algorithm’s job in clustering differs from traditional classification in that it must group elements into clusters without any predefined classes. What this means is that he, and no one else, will have to settle on the rules of separation. We will soon go over the typical unsupervised learning approach to cluster analysis. Clustering offers solutions for the following issues:
Based on their characteristics and actions, how can we classify our customers into the primary groups?
Does the conduct of some bank customers correlate with the likelihood that they will default on their loans?
Can we find a way to group the search terms that lead people to our site?
Section 3.3. Regression analysis
Classes are not determined by regression algorithms, but numerical target values are. In order to forecast things like product demand, sales numbers, marketing ROI, etc., these algorithms compute numerical variables. Various instances:
In the next month, what is the projected sales volume for this product?
How much will the cost of this flight be?
In order to maximize a vehicle’s longevity, what is the optimal top speed?
section 3.4. Ranging
When compared to other objects, ranking algorithms establish an object’s or element’s relative importance. One well-known example is Google’s PageRank algorithm, which determines a page’s position in search engine results. To further refine the presentation of user-generated content in news feeds, Facebook* employs ranking algorithms. Is there anything else that ranking can fix?
What are the user’s preferred movie genres?
Which hotels would you suggest to this user?
How can one improve a store’s product search rankings?
Section 3.5. Generation
Texts, images, or music can be created using generation algorithms. These days, you can find them in programs like Prisma, which makes pictures look like paintings, and WaveNet, developed by DeepMind, which can make music or mimic human speech. There is a lot of room for growth in the entertainment software industry for generative tasks because they are more commonly used in consumer applications than in predictive analytics solutions.
Is there anything that generative algorithms could possibly fail at?
Using a certain style to turn photos into paintings.
Building voice assistant apps that can convert text to speech (like Google Assistant) for mobile devices.
Making musical samples that sound like or are reminiscent of a particular artist’s work.
From a photograph, an artwork inspired by the engraving “The Great Wave off Kanagawa” was created.
Various model training techniques, also known as learning styles, are employed to achieve these goals. The process of creating a customized mathematical model that is fine-tuned to dependencies in values of past data is called training. With this knowledge under its belt, the trained model can spot these interdependencies in forthcoming data and make accurate predictions. Three distinct approaches to training models exist.
- Three approaches to training models
You need to be aware of the values you’re seeking before you can choose a learning style. Put simply, you might have training datasets that already have the values you need mapped; then, all you have to do is tell the algorithm to predict those exact values in new data. Finding unspoken relationships between values could also be your aim. In this situation, the necessary values for past and future data are uncertain. Both the learning style and the algorithms selected are affected by these divergent objectives.
Section 4.1. Individual instruction
When training, supervised learning algorithms use pre-existing values in historical data. In training datasets, labeling refers to the comparison of these desired values. To rephrase, humans instruct the algorithm on what values to prioritize and how to determine correct decisions. The algorithm learns to discover the desired values in future data by treating the label as an example of a successful prediction. As supervised ML typically makes use of training datasets that already contain the necessary values, it is now commonly employed in classification and regression problems.
This is why supervised learning is the preferred method in the corporate world. For instance, you can tell which leads converted and which ones didn’t if you use binary classification to forecast the chance of a lead converting. After selecting the appropriate values (conversion, no conversion, 0/1), you can begin training the model. Other applications of supervised learning algorithms include image object recognition, social media post sentiment analysis, and numerical value prediction (e.g., weather, prices, etc.).
Chapter 4.2. Unsupervised ML
Using unlabeled target values, unsupervised learning attempts to classify data. Here, machine learning is aiming to find meaning patterns and organize objects based on their similarities and differences. Unsupervised learning is widely employed in clustering algorithms, anomaly detection, and generative problems within the domain of classification problems. Among their many applications, these models help with segmentation issues, uncovering hidden relationships between elements, and more.
A financial institution, for instance, may divide its clientele into various categories using unsupervised learning. This is useful for creating individualized plans for interacting with each group. When it comes to ranking algorithms, unsupervised learning techniques are also utilized for the creation of personalized recommendations.
Section 4.3. Learning with reinforcements
Taking cues from behaviorism and game theory, reinforcement learning is arguably the most advanced machine learning style. The algorithmic agent is “rewarded” or “punished” based on its decision-making performance in response to input data. The agent learns to change its decisions and get better results over time by receiving “rewards” and “punishments” in an iterative fashion.
The fields of robotics and artificial intelligence make extensive use of reinforcement learning techniques. Rather than attempting every conceivable configuration of stones on the board, DeepMind’s renowned AlphaGo algorithm utilized reinforcement learning to assess the most productive moves in the game of Go. It is likely that Tesla Autopilot also makes use of supervised learning and reinforcement learning. In order for the driver to fix its own mistakes while using the autopilot, this kind of learning is employed.
Most algorithms can only learn effectively with constant rules, objectives, and environments, which makes it difficult to apply reinforcement learning in the corporate world. Because of the stability of these three parameters, many of the achievements of contemporary reinforcement learning can be traced back to games such as Go and older Atari games. The duration of training cycles is another issue with reinforcement learning. In games, the time it takes to go from the first solution to the points is very short, but in real life, it can take weeks to determine if a solution was successful.
Part 5.1. An innovative “translator for business”
The absence of competent analytics and data science leadership is the primary barrier to establishing a data-driven company culture. Nearly half of the businesses surveyed by the McKinsey Global Institute said they are having trouble figuring out how to approach data science and AI strategically. It is no secret that finding and keeping qualified specialists can be a real challenge. For example, data science professionals are in short supply, and the costs associated with doing so can add up quickly. The poll found that while data scientists are hard to come by, analytical project managers are even more of a challenge. To effectively manage data processing processes, however, this role is crucial. Machine learning implementations and missing link filling can be done without an analytics leader, but the process will still be reactive instead of proactive.
This chief analytics officer (CAO), also known as a “business translator,” possesses interdisciplinary expertise that allows them to connect the dots between data science tasks and business values. The person in this role will be accountable for coordinating the efforts of marketing, data science, information technology, and other relevant departments to create and refine the data strategy.