Relationship Between Machine Learning Data Science and Artificial Intelligence 2024

What is the fundamental relationship between machine learning data science and artificial intelligence? Artificial intelligence, data science, and machine learning are in the same technological domain.

These three fields are interrelated and have several applications that must be paired to provide a particular solution. Also, there are overlaps in these three realms, and these three technologies have unique utilities.

Despite that, these three terms have been used interchangeably because of their connections. However, there are significant differences. Let us then discuss the similarities and differences between data science, machine learning, and artificial intelligence in detail.

What is the Relationship Between Machine Learning, Data Science and Artificial Intelligence?

Of course, there is a close relationship between data science, artificial intelligence, and machine learning. All of them are interconnected in a close way. Machine learning, data science, and artificial intelligence have a wide field of applications and systems with one sole objective – replicating human intelligence with machines’ aid.

By developing these technologies, multiple possibilities can be explored. Artificial intelligence is all about representing perception with the help of action-planned feedback. This can describe the relationship between data science, machine learning, and artificial intelligence.

What is Machine Learning?

Machine learning is the science of creating software that can run on itself. The software can learn freely or in tandem with other machines or humans. Machine learning forms a vital component of artificial intelligence.

Without this technology, machines are incapable of making decisions like humans. Also, machine learning aids data science in discovering patterns and automating the data analysis process. Data science contributes enough to help AI and ML grow.

Machine learning also increases the ability of the machines to logically analyze an action and then perform the necessary actions to achieve a specific goal. Machine learning, being an AI subset, helps computer programs to automatically learn from the various sample datasets and collect a set of inferences.

It then adapts that data to create a new dataset(s) without human assistance or supervision. These techniques are called deep learning, enabling computers or machines to perform automatic learning by absorbing the absorption of huge unstructured data in large volumes. These datasets could include anything, like text, images, or videos.

What is Artificial Intelligence?

Artificial intelligence is a set of activities that replicates and helps perform human intelligence simulations in machines. These machines have been programmed so they can think like humans and perform actions by mimicking them. Machine learning also involves programming any machine that can then show the characteristics exhibited by a human mind, like problem-solving and learning.

What is Data Science?

Data science is the system development process for gathering and analyzing dissimilar data to unravel solutions to several business challenges. A pattern or a loop is formed after chalking out the solutions to real-world problems. Then, it is used in solving various problems. For example, data scientists identify patterns by analyzing multiple data types when we refer to perception.

After that, they focus on finding all the feasible solutions to the problems and the one that is best among them. Data science helps in creating a system that forms a correlation between all the points above and helps businesses to move forward.

Understanding The Relationship Between Machine Learning Data Science And Artificial Intelligence

Despite being a standalone subject, machine learning is best explained with the help of the environmental context and is heavily responsible for the system it is used within. It is the bridge that connects data science and AI.

That’s because data science is the process of learning from data over time, and machine learning helps it to achieve this goal. Conversely, AI is a tool that allows data science to achieve a specific result or a solution to a unique real-life problem. However, it can do that only with the help of machine learning.

Use Cases

Google

Google’s search engine is a real-life example of the combined relationship between machine learning, data science, and artificial intelligence. Although Google’s search engine is a product of data science, it employs predictive analysis, a system used by artificial intelligence for delivering legible and intelligent results to the people who are using the search engine.

For example, if a person enters an input, ‘best jackets in New Delhi’, on Google’s search engine, then the AI system employed by Google collects this data through its BL tools that keep working in the backend.

Now follow how Google gives the search result to the person who has entered the input. As soon as the person writes these words – ‘the best place to buy,’ the AI activates, and using the location settings along with the predictive analysis tools, it completes the phrase, ‘best place to buy jackets in New Delhi’. It is the suffix with the highest probability to that query, and this is what the user also had in mind.

Netflix

Another interesting example of how artificial intelligence, data science, and machine learning work together in unison. Netflix uses all three interlinked technologies for running all its web operations.

By applying algorithms, Netflix uses AI to guarantee high-quality streaming service even with low bandwidth. There are other applications of machine learning, artificial intelligence and data science at Netflix.

These three technologies help in the personalization of thumbnails. It is one of the most prevalent trends for all types of streaming platforms in modern times. People first view the thumbnail, and then maybe they view the titles.

Viewers are tuned in such a way, which is why thumbnails are so important for any streaming platform. It also helps to entice new viewers. How is this mechanism performed? For that, we will have to understand how the technology works.

Here, in this case, the AI generates thumbnails of the videos. For that, it ranks and marks hundreds or maybe thousands of frames from a movie that pre-exists on that platform. After that, it chooses the most clickable picture and then forms a thumbnail by combining the frame and the movie title.

It also uses machine learning and data science to maintain optimal streaming quality. It is a difficult job considering that around 220.67 million people actively use the Netflix video streaming platform every month. Here, Netflix predicts using the three technologies and estimates how many subscribers will access its services in the future.

After making that estimation, Netflix enhances the video quality for the spectators. It can improve video quality when the viewing times are busy, and multiple people are on the platform. Netflix places the video assets near the viewers beforehand.

But that is not all Netflix’s AI + ML + DS system does. It also helps to customize the movie recommendation according to a user’s viewing preferences. The algorithm for this recommendation has been developed in such a manner that it constantly keeps on learning about the user’s preferences.

This system has been developed so that if you view from the same account from two different locations, you will be shown different recommendations in these two places.

It is all because of the special AI and ML system of Netflix. The system sends you better recommendations if you spend more time on the Netflix platform. The annualized cost of the Netflix Recommendation Engine is around $1 million. The sole purpose of this Recommendation Engine is to improve overall customer satisfaction.

By seeing these use cases, we can conclude that all these three technologies are beneficial in the real world. Data science, artificial intelligence, machine learning and an essential subset of it, Deep Learning, are being used a lot in modern online applications like Google and Netflix.

Even Neural Networks, one of the vital components of machine learning, are being heavily employed in applications like these. Neural Networks replicate the function of a brain and use a 3D hierarchy in the data for identifying the useful patterns from among all the noise. Now let us understand the differences between data science, machine learning and artificial intelligence.

What is the Difference Between Machine Learning Data Science And Artificial Intelligence?

How do Data Science, Artificial Intelligence, and Machine Learning Work Together?

The biggest thing that data science, artificial intelligence, and machine learning can achieve is combining human and machine intelligence and making significant innovations. Whatever the outcome, machine learning will turn out to be a precious tool for both artificial intelligence and data science. That way, it will help to enhance human decision-making and make it worthwhile.

Artificial intelligence systems will make an efficient and remarkable use that will make tools like recommendation systems more powerful. In other words, powerful algorithms will refine the likes of Google’s search engine or Netflix’s recommendation system.

ML, in the future, will automate most of the learning process and squeeze every iota of data from real-world data. The performance based on feedback would constantly improve, making it a customized affair for the users. It would be an incredible thing in itself.

There is even a possibility that these three technologies could collaborate with the human mind, take all the information from the bio-neurons in the brain, and teach ML systems. This way, many remarkable inventions could be made in the field of healthcare and Augmented Reality and Virtual Reality (AR and VR). Some fields combine data science with machine learning.

Machine learning will also be aiding in the automation field. Much of the Analytical Modelling processes that data scientists do manually would be done by themselves after the data scientists have done the training bit.

This helps to relieve the data scientists (or any other type of human intervention involved in the repetitive processes of data processing) to spend this additional time to help out organizations analyze and generate newer data insights and transform them into actions that create a considerable impact.

But how will it happen exactly? Here we will provide some examples of how data science, after intersecting with artificial intelligence, is placed on machine learning systems.

For this, we need to make an assumption. In this example, we will suppose that a web search engine organization wishes to arrange for ‘allergy medicine for kids’. It also wants to monetize the most relevant searches that are made online in response to this query. So, what are the actions it could take? They are as follows:

• Data scientists could be involved in this social experiment. They could help in the collection of big data. After that, these large datasets, which contain millions or billions of online user search results about kids’ allergy medication, could be organized efficiently. Then, after this has been done, these data scientists could work with engineers or software developers to build ML models that learn from all the information provided in these datasets. These are then sent to the secondary level of the data processing activity.
• After training the models on this data, the ML models can then identify all the users’ choices and preferences. This can be done for multiple search results. For example, the information about what allergy medications are either chewable pills or which ones are syrups could be used for updating the ML models. This information will help the search engine to deliver relevant results that even rank higher in the Search Engine Results Pages (SERPs). This way, the users won’t have to scourge the internet to find a particular piece of information.
• Also, information on other relevant data apart from the search results, like the click trends, can be taken into account and then can be used to identify specific results. The ML models could use this information, provide specific search results about people’s medical requirements and consider a person’s medicine shopping habits. Say a person prefers a particular allergy medication brand. This could either be an effective medication or be popular among doctors or families in a particular geographic area or during a specific time. Data scientists use all this information to analyze these trends to find business insights. They then share this data with pharmaceutical companies and online advertising companies.

Combined with data science, machine learning predictions about any industry or market trends can produce insights that can help the users and the stakeholders take a bird’s eye view of the whole situation. It will also help them to extract information and make more prominent and high-level corporate decisions when it comes to the launch of newer products or services.

The three technologies (AI, ML, and DS) can also deliver instant insight into which some action could be taken. It will also help the employees of an organization to give better customers or after-sales services.

They could attend to specific complaints of the customers and attend to their grievances by giving them customized services. Nowadays, such technologies understand the customers’ choices and aid them in making decisions while shopping, driving a car, or booking a vacation.

Organizations and individuals have invested in leveraging data science, machine learning, and artificial intelligence capabilities. They can enhance their human decision-making at every level and improve their performances.

What are the Skills Needed for DS, AI, and ML?

The fields of data science, artificial intelligence, and machine learning have multiple requirements, and this part will mention all the required information about academic and analytical needs. Have a look at the section that has been provided below:

Skills Needed for Data Science

To become a data scientist, you need two types of essential skill sets. One is the technical skills required to extract and process the data. The second set of skills is the non-technical ones or the soft skills necessary to deliver the data to the relevant stakeholders.

In the non-technical skills, you, as a data scientist, are expected to have excellent communicative capabilities. You need to be able to present the data as a story so that it becomes easy and absorptive.

The technical skills that are required to become a data scientist are given below:

• Advanced Maths
• Elementary and Advanced Statistics
• Data Analytics and Modelling
• Database Management
• Data Visualisation Techniques
• ML Methodology
• Collaboration and Communication
• Data Science Programming, specifically in Python

Skills Needed for AI

Since AI is a broader term and multiple technologies are encompassed, you need to be extremely strong on the technical side, especially in mathematics or technical programming. As an AI engineer, you should have a high visualization capacity and be able to present your ideas cohesively. Apart from that, you need to be quite innovative and develop several out-of-the-box solutions.

The technical skills that are required in the field are:

• Advanced Maths
• Programming in Languages like Python, R, Java, and C++
• Probability and Statistics
• Big Data Technologies like Spark

Skills Needed for ML

The companies that hire machine learning engineers usually prefer those candidates that have a master’s or a Ph.D. degree in subjects like mathematics or computer science. These candidates should have a working knowledge of modern-day programming languages and be able to apply them in real-world situations or be more practical in their usage of computer languages.

They should also be well-versed in Cloud Technologies and Cloud deployment techniques. Apart from their strong computer programming skills, they must also be mathematics experts. Communication skills need to be excellent to explain what they have made. Their analytical skills should be spot on. They should also have some high-quality certifications in machine learning.

• Applied Mathematics
• Programming Languages like Scala, Python and Java
• Neural Network Architectures
• Physics
• Data Modelling & Evaluation
• Advanced Signal Processing Techniques
• Natural Language Processing
• Audio & Video Processing
• Reinforcement Learning

This is complete information on the relationship between machine learning data science and artificial intelligence. Hopefully, this has shed ample light on the matter and cleared any confusion. In conclusion, the relationship between Data Science, Machine Learning, and Artificial Intelligence in 2024 remains symbiotic and transformative.

As technology advances, the demand for professionals skilled in these fields continues to soar. Pursuing a data science and machine learning course equips individuals to tackle complex challenges, unlocking unprecedented opportunities to innovate and revolutionize industries in this data-driven era. Embracing this dynamic connection will undoubtedly shape the future of technology and society.