Role of Machine Learning in Data Science Simplified 101
Machine Learning is really a big buzzword in the world today. Interestingly, Machine Learning has existed for a long time without you even realizing it. Ever wondered, on what basis does YouTube recommend you the next video? It looks at what videos you are watching, what channel the videos are from, what is the duration of the videos, and what topic the videos are on. So, YouTube takes into consideration all these factors before recommending you the next video. In short, YouTube is “learning” from your watching habits, and based on that it suggests similar videos. This is how Machine Learning works and you have been seeing examples of this for years.
Table of Contents
Data Science, as you probably know, covers a wide spectrum of domains and Machine Learning is one of them. Data Science basically comprises various fields and techniques, like Statistics and Artificial Intelligence for Data Analysis to draw meaningful insights.
In this article, you’ll understand how Machine Learning is being used in Data Science for Data Analysis and the extraction of valuable insights from data.
Table of Contents
- What is Data Science?
- Importance of Data Science
- What is Machine Learning?
- What are the Applications of Machine Learning in Data Science?
- What are the Challenges of Machine Learning in Data Science?
- What is the Role of Machine Learning in Data Science?
- 5 Major Steps of Machine Learning in the Data Science Lifecycle
- 3 Key Machine Learning Algorithms in Data Science
- 3 Machine Learning Use Cases in Data Science
What is Data Science?
Back in the day, Businesses and other Institutions were able to store most of their data in Microsoft Excel Sheets. Even the modest Business Intelligence tools were capable of analyzing and processing this data. The presence of a lesser amount of data made the handling and managing of data easier. But with the passage of time, the amount of data produced every day kept increasing.
You’d have come across this study by Forbes which states that nearly 2.5 Quintillion Bytes of data are generated every day. According to Raconteur, by 2025, 463 Exabytes of data are expected to be generated every day globally.
This is the scale of data that will be available to be analyzed in the future. For processing data of this magnitude, traditional Spreadsheets and conventional Business Intelligence tools are not going to come in handy. You need sophisticated Data Infrastructure and cutting-edge tools/technologies to process data of such magnitudes. This is where Data Science comes into the picture.
Data Science is all about using data to create as much impact as possible for your company. The impact can be in the form of multiple things. It could be in the form of viewing insights of the audience that Netflix mines to produce an original series or in the form of video recommendations for YouTube. Now to do those things, you need to make complicated Models, write code and make use of Data Visualization tools.
The Journal of Data Science described Data Science as “almost everything that has something to do with data: Collecting, Analyzing, Modeling…… yet the most important part is its applications — all sorts of applications”. Yes, all sorts of applications like Machine Learning. Machine Learning, Deep Learning, and Artificial Intelligence are all used in Data Science for the analysis of data and extraction of useful information from it.
Importance of Data Science
The amount of data has never been this much huge as they are in today’s age. Similarly, the complexity of the data is also increasing with time. Today a Data Scientist is simultaneously dealing with a variety of data formats to derive predictions and reach conclusions. This increasing volume and growing complexity gave rise to a need for such techniques, methods, or tools that can help Data Science Data Analysts to analyze more efficiently and quickly.
To fulfill this need, the researchers discovered Data Science, a combination of complex Machine Learning techniques integrated with a variety of tools to help the Data Science Data Analysts in decision making, finding the new patterns, and discovering new ways of Predictive Analysis.
To know more about Data Science, visit this link.
What is Machine Learning?
It is now possible to Train Machines with a Data-Driven approach. On a wider spectrum, if you think of Artificial Intelligence as the main umbrella, Machine Learning is a subset of Artificial Intelligence. Machine Learning, a set of Algorithms, gives Machines or Computers the ability to learn from data on their own without any human intervention.
The idea behind Machine Learning is that you teach and Train Machines by feeding them data and defining features. Computers learn, grow, adapt, and develop by themselves when they are fed with new and relevant data, without relying on explicit programming. Without data, there is very little that Machines can learn. The Machine observes the dataset, identifies patterns in it, learns automatically from the behavior, and makes predictions.
It is the Machine Learning technology that Online Recommendation Engines use to offer relevant recommendations to the user, be it YouTube Video Recommendations or Facebook Friend Recommendations. One of the most recent technologies, Google’s Self Driving Car also makes use of Machine Learning Algorithms to understand the patterns and definitions, learn automatically, and execute the operation.
What are the Applications of Machine Learning in Data Science?
Listed below are some of the most popular applications of Machine Learning in Data Science:
- Real-Time Navigation: Google Maps is one of the most commonly used Real-Time Navigation applications. But have you ever wondered why despite being of the usual traffic, you are on the fastest route? It is because of the data received from people currently using this service, and the database of Historical Traffic Data. Everyone who uses this service contributes to making this application more accurate. When you open the application, it constantly sends the data back to Google, providing information about the route being traveled and traffic patterns at any given time of the day. All the information given by the number of users using the application on regular basis has given Google a huge database of traffic data which allows Google Maps not only to track the traffic at that instance but also predicts what will happen if you continue in the same route.
- Image Recognition: Image Recognition is one of the most common applications of Machine Learning in Data Science. Image Recognition is used to identify objects, persons, places, etc. The most popular use cases of this application are Face Recognition in Smartphones, Automatic Friends Tagging Suggestions on Facebook, etc.
- Product Recommendation: Product Recommendation is profoundly used by eCommerce and Entertainment companies like Amazon, Netflix, Hotstar, etc. They use various Machine Learning algorithms on the data collected from you to recommend products or services that you might be interested in.
- Speech Recognition: Speech Recognition is a process of translating spoken utterances into text. This text can be in terms of words, syllables, sub-word units, or even characters. Some of the well-known examples are Siri, Google Assistant, Youtube Closed Captioning, etc.
What are the Challenges of Machine Learning in Data Science?
Machine Learning in Data science has revolutionized the face of the industries. It has helped companies to take intelligent decisions to grow their business. But it still faces a couple of challenges that a Data Scientist must consider. Listed below are the Top 3 challenges of Machine Learning in Data Science:
- Lack of Training Data: Data is the core of any Machine Learning model. However, it is extremely difficult and expensive to obtain labeled data. Training a Machine Learning model without a large amount of data is something that haunts every Data Scientist. Transfer Learning is one of the methods to solve this problem. It enables the model to utilize knowledge from previously learned tasks and applies them to the new related ones. Self-Supervised Learning is another way to solve this problem. It opens up a huge opportunity for better utilizing large amounts of unlabeled data.
- Discrepancies between Data: The second challenge is that there are usually some discrepancies between the training data and production data. Sometimes the model works well in your prototyping environment but fails to generalize in real-world cases. For example, the model may work well in one country but fail in another due to geographical differences, the model may work in winter but fail in summer due to seasonal differences, and the model may work on mobile but fail on desktop due to user differences, etc. To solve this problem, you need to be very careful while collecting your training data. To make it as close to your target domain as possible, you need to keep updating your model frequently.
- Model Scalability: This is one of the major challenges that industries face. As a Data Scientist, you need to make sure that your model can is fast but at the same time also not very bulky. One of the solutions to this problem is Post-Training Quantization. It is a conversion technique that reduces the model size but at the same time improves CPU and hardware Accelerator Latency, with a little degradation in your model accuracy.
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What is the Role of Machine Learning in Data Science?
Machine Learning and Artificial Intelligence have dominated the industry overshadowing every other aspect of Data Science like Data Analytics, ETL, and Business Intelligence.
Machine Learning analyzes large chunks of data automatically. Machine Learning basically automates the process of Data Analysis and makes data-informed predictions in real-time without any human intervention. A Data Model is built automatically and further trained to make real-time predictions. This is where the Machine Learning Algorithms are used in the Data Science Lifecycle.
The typical flow for Machine Learning starts from you feeding the data to be analyzed, then you define the specific features of your Model, and a Data Model is built accordingly. The Data Model is then Trained using the Training dataset that was fed initially. Once the Model is Trained, the Machine Learning Algorithm is ready to make a prediction the next time you upload a new dataset.
Let’s understand this with the help of an example. You must have probably come across Google Lens, an app that allows you to take a picture of, suppose, someone, having a good dressing sense, and then it helps you find similar clothes.
So, the first step for the App is to recognize what product it is looking at. Is it a pair of Jeans, a Jacket or a Dress? The features of different products are defined, the App is told that a Dress has shoulder traps, it doesn’t have any zippers, it has holes for arms on each side of the neck, etc. So, the features of what a Dress looks like are defined. Now, the App can actually create a Model of a Dress based on the defined features.
When the picture is uploaded, the App looks at all the existing Models and tries to define what it is actually looking at. The App then makes a prediction using the Machine Learning Algorithm and shows you similar Models of the clothes it has.
Let’s discuss this workflow for Machine Learning in Data Science briefly.
5 Major Steps of Machine Learning in the Data Science Lifecycle
- Data Collection: Collecting data is considered the foundation step of Machine Learning. Collecting relevant and reliable data becomes very important as the quality and extent of data directly impact the outcome of your Machine Learning Model. As discussed in the previous section, this dataset is further used for Training your Data Model.
- Data Preparation: Data Cleaning is the first step in the overall Data Preparation process. This is an essential step in making the data analysis-ready. Data Preparation ensures that the dataset is free of erroneous or corrupt data points. It also involves standardizing the data into a single format. The dataset is also split into two parts to be used for Training your Data Model and evaluating the performance of the Trained Model, respectively.
- Training the Model: This is where the “learning” starts. The Training dataset is used to predict the output value. This output is bound to diverge from the desired value in the first iteration. But practice makes a “Machine” perfect. The step is repeated again after making some adjustments in the initialization. The Training data is used to incrementally improve the prediction accuracy of your Model.
- Model Evaluation: Once you’re done Training your Model, it’s time to evaluate its performance. The evaluation process makes use of the dataset that was set aside in the Data Preparation process. This data has never been used for Training the Model. So, Testing your Data Model against a new dataset will give you an idea of how your Model is going to perform in real-life applications.
- Prediction: Now that your Model is Trained and evaluated, it doesn’t mean that it’s perfect and is ready to be deployed. The Model is further improved by tuning the parameters. Prediction is the final step of Machine Learning. This is the step where your Data Model is deployed and the Machine makes use of its learning to answer your questions.
Now that you’ve got an idea about the Machine Learning workflow, let’s discuss the various Machine Learning Algorithms in Data Science.
3 Key Machine Learning Algorithms in Data Science
When you have a dataset, you can classify the problem into three types:
When the output variable is in continuous space, Regression is used. You probably would have come across the Curve-Fitting Techniques in Mathematics. “y=mx+c”, rings a bell? Regression, also, is based on the same techniques. Regression is more like finding the equation of a curve that fits the data points and once you have the equation, you can predict the output values accordingly.
Some famous Regression Algorithms are Linear Regression, Perceptron, and Neural Networks.
Regression is useful for Financial Predictions like Stock Market Prediction and Housing Price Prediction.
When the output variables are discrete values, Classification is used. If you want to find the category that your data belongs to, then it is a Classification problem. Classification Algorithms look at existing data to help you to predict the Class or Category of the new data.
Classification is more like finding curves that separate the data points into different Classes/Categories.
Labeling an Email as Spam is a Classification problem. Gmail, for example, will check any Email for the features that define Spam and will start putting it into your Spam Folder if 80% or 90% of the features match.
Some famous Classification Algorithms are Support Vector Machines, Neural Networks, Naive Bayes, Logistic Regression, and the K Nearest Neighbour.
If you just want to group your data points, having similar characteristics, without labels, it is then a Clustering problem. Ideally, the similar data points are grouped together in the same Cluster based on different definitions of similarity. The points in different Clusters should be as dissimilar as possible. The Clustering Algorithms try to find a pattern in a dataset without associating labels with it.
Some famous Clustering Algorithms are K-Means Clustering and Agglomerative Clustering.
Buying behaviour of customers is Clustered using this Algorithm.
Regression and Classification come under the Supervised Learning Model of Machine Learning while Clustering comes under the Unsupervised Learning Model.
3 Machine Learning Use Cases in Data Science
As discussed, Machine Learning has been existing for years now, without you probably even realizing it. Machine Learning finds its application in almost every sector, from Finance Institutions to Entertainment Industry. It is Machine Learning that goes behind the Apps you use on a regular basis to make your life easier such as Google Maps, Microsoft Cortana, and Alexa. Given below are some of the most popular real-life applications of Machine Learning in Data Science:
- Fraud Detection: Banks use Machine Learning for fraud detection to keep their customers safe. Machine Learning Models are Trained to flag transactions that appear suspicious based on the defined features and transaction patterns. Machine Learning can ensure the safety of consumers not just to Banks but to Private Enterprises as well.
- Speech Recognition: Ever wondered what goes behind Siri? The Voice Assistants on Smartphones also leverage Machine Learning to recognize what you say and craft a response accordingly. Machine Learning Models are Trained on human languages and various accents to convert the speech into words, and then make a response a smart response.
- Online Recommendation Engines: As already discussed in the previous sections, Online Recommendation Engines make use of Machine Learning to suggest relevant recommendations to their users. Amazon often lists Recommended Products for its customers, YouTube provides personalized Video Recommendations to its users, and similarly, Facebook suggests Friends’ Recommendations. Machine Learning Models are Trained on Customer Behaviour, Past Purchases, Browsing History, and any other behavioral information about consumers.
Nowadays, organizations really emphasize using data to improve their products. Data Science is just Data Analysis without Machine Learning. Data Science and Machine Learning go hand in hand. Machine Learning makes the life of a Data Scientist easier by automating the tasks. In the near future, Machine Learning is going to be used prominently to analyze a humongous amount of data. Therefore, Data Scientists must be equipped with in-depth knowledge of Machine Learning to boost their productivity.
This article gave you an introduction to Data Science and Machine Learning with the help of real-life examples. It helped you understand how Machine Learning is being used in Data Science for Data Analysis and the extraction of valuable insights from data. The article also briefed you on the workflow of Machine Learning in Data Science. The article discussed the most popular Machine Learning Algorithms that are used in Data Science. The article concluded with a peek into the real-life applications of Machine Learning in Data Science.
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Does the idea of Machine Learning intrigue you? Share your thoughts on the role of Machine Learning in Data Science in the comments section below.