The world is filled with data and in various formats such as Pictures, Music, Spreadsheets, Videos, etc. There were times when most of these data were stored in Excel sheets. Back then, businesses used simple Business Intelligent (BI) tools to analyze and process the data. But with the evolution in technology, businesses are now generating trillions of data on daily basis and it is ever-evolving. The data generated these days is mostly Unstructured or Semi-structured. To process such data, businesses cannot use simple BI tools anymore but more complex and effective algorithms. They need a more advanced field to process and visualize their data effectively. This is exactly where Machine Learning in Data Science comes in.
Machine Learning in Data Science makes machines get into a Self-Learning mode without any human intervention or explicit programming. These machines Learn, Grow, Change, and Develop by themselves. Hence the term, Machine Learning. Machine Learning in Data Science is increasingly being used by every business in the world. From Amazon for product recommending to their customers to hospitals for detecting diseases in the early stage, every sector of the society is highly dependent on Machine Learning in Data Science.
This article will introduce you to Data Science and the importance of Machine Learning in Data Science. It will also introduce you to the 3 types of Machine Learning in Data Science. It will also brief you on the 4 most popular and widely used algorithms of Machine Learning in Data Science. It will also introduce you to some of the most common applications of Machine Learning in Data Science. This article will also help you understand the 3 challenges people generally face while working in this field and their possible solutions.
Table of Contents
- Introduction to Data Science
- The Pillars of Data Science
- Introduction to Machine Learning in Data Science
- Types of Machine Learning in Data Science
- Popular Algorithms of Machine Learning in Data Science
- Applications of Machine Learning in Data Science
- Challenges of Machine Learning in Data Science
Introduction to Data Science
In the era where data is an invaluable commodity for any company, Data Science is something that has become a necessity no matter the type of business you run. Data Science is all about uncovering findings from data. This can be done by exploring data at a granular level to mine and understand complex behaviors, trends, and influences in the data. It includes surfacing hidden insights that can help enable companies to make smarter business decisions. Data Science encompasses fields like Machine Learning, Artificial Intelligence, Data Analysis, Data Engineering, Data Visualization, etc.
Those who practice Data Science are called Data Scientists. A Data Scientist is someone who can extract meaningful information and insights from existing data sources. A Data Scientist also identifies and uses new data sources to help, support, and drive important business decisions that eventually achieve business goals. A very common example of Data Science is your recommendation list on Netflix. Netflix mines its data for movie viewing patterns of its users to understand what drives user’s interest and then uses this information to change the recommendation list of the user.
The Pillars of Data Science
Data Scientists most often come from different educational fields and working backgrounds but in an ideal world, a Data Scientist should have a stronghold on 4 fundamental areas. The 4 fundamental pillars of Data Science expertise are as follows:
- Business/Domain Expertise: This includes expertise in Management or Business-Related areas with a good grasp of a technical domain. You can think of an MBA graduate who is also an expert in an industry or domain.
- Mathematics Expertise: This majorly includes expertise in Statistics and Probability as these are the 2 domains of Mathematics that are a part of a Data Scientist’s daily life.
- Computer Science Expertise: This majorly includes Software or Data Architecture Engineering. A Data Scientist should have the ability to use all relevant Programming Languages, Software Packages, and Libraries, Data Infrastructure, and so on.
- Communication Expertise: This includes fluent written and verbal communication so that Data Scientists can deliver results, conclusions, and reports to senior executives or clients.
A Data Scientist with expertise in all 4 pillars is quite wishful. In reality, people are usually strong in one or two of these pillars but not equally strong in all 4.
For more information on Data Science, click here.
Introduction to Machine Learning in Data Science
The idea behind Machine Learning in Data Science is to teach machines by feeding them data and letting these machines learn on their own without any human intervention. People often get confused between Data Science and Machine Learning. They think that the words can be used interchangeably but it is not true. Machine Learning is a subset of Artificial Intelligence which in turn is a subset of Data Science that provides machines the ability to learn automatically and improve from their experience without being explicitly programmed.
Data Scientists use Machine Learning models to extract meaningful information and insights for strategic decision-making. Machine Learning in Data Science begins with reading and observing the training data to find useful insights and patterns to build a model which could predict the correct results. The performance of the model is then evaluated by using the testing datasets. This process is then carried out until the machine automatically learns and maps the input with the correct output without any human intervention.
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Types of Machine Learning in Data Science
You need to understand the different types of Machine Learning in Data Science. This will help you craft a proper learning environment and understand why what you did worked. There are mainly 3 types of Machine Learning in Data Science:
1) Supervised Learning
Supervised Learning is one of the most popular models of Machine Learning in Data Science. As the name implies, you have to supervise the Machine Learning while you train it to work on its own. It requires labeled training data. Given the data in the form of examples with labels, you need to feed a Learning Algorithm for pairing or predicting these examples with their corresponding labels and provide feedback on whether the Learning Algorithm predicted the right answer or not.
Over time, the algorithm learns to approximate the exact nature of the relationship between examples and their corresponding labels. Supervised Learning is highly focused on a singular task. It is required to feed the Learning Algorithm more and more examples until it can accurately predict the labels for never-before-seen examples. This is why Supervised Learning is often described as Task-oriented Learning. Common examples of Supervised Learning are, Face Recognition, Spam Classification, Advertisement Popularity, etc
2) Unsupervised Learning
Unsupervised Learning is quite the opposite of Supervised Learning as the training data used in Unsupervised Learning is not labeled. Instead, the Learning Algorithm is given a lot of data and the tools to understand the properties of the data. As the majority of data is unlabeled these days, Unsupervised Learning is taking over Supervised Learning. Unsupervised Learning takes terabytes of unlabeled data and forms clusters, or groups to make predictions.
For example, consider you have an Unsupervised Learning model which can group a large dataset of every research paper ever published in such a way that you can track the progression within a particular domain of research. Now, you started your research project and hooked your work into this network that the algorithm can track. As you keep building your research project, the algorithm makes suggestions about related work which boosts your productivity and helps you push the research forward.
Some areas where you will see Unsupervised Learning are Recommender Systems, Grouping User Logs, etc.
3) Reinforcement Learning
Reinforcement Learning is fairly different from Supervised and Unsupervised Learning. Unlike Supervised and Unsupervised Learning models wherein the machine learns from the training data that is labeled and unlabeled respectively, in Reinforcement Learning, the system learns on its own. In simpler terms, a Reinforcement Learning model learns from the mistakes and the feedback provided on those mistakes.
For example, imagine there is a newborn baby. You put a burning candle in front of the baby. Now, the baby does not know that if it touches the flame, its fingers might get burn. So it does that anyway and gets hurt. The next time you put a candle in front of the baby, it will remember what happened last time and would not repeat the same mistake. This is exactly how Reinforcement Learning works.
You provide the machine with the dataset wherein you ask it to identify a particular kind of fruit (in this case, a mango). As a response, it tells you that it is an apple. As it is not the right answer, you provide the feedback stating that it is not an apple but a mango. The machine learns from the feedback and predicts the right result when asked the same question again.
Popular Algorithms of Machine Learning in Data Science
This subsection will introduce you to the 4 most popular and widely used Machine Learning algorithms in Data Science:
1) K-Nearest Neighbors (KNN)
K-Nearest Neighbour (KNN) is a Classification Algorithm. In KNN, similar Data Points form Clusters. Now, if the algorithm gets a new and unknown Data Point then it is classified based on the Cluster closest to it or most similar to it. “K” in K-Nearest Neighbour is the number of Data Points the user wishes to compare the unknown Data Point with. The value of “K” is always greater than 1.
One of the most common use cases of K-Nearest Neighbor is forecasting the stock market. It is used to predict the price of a stock based on the company’s Performance Measures and Economic Data.
For more information on K-Nearest Neighbors, click here.
2) Linear Regression
Linear Regression is a Supervised Learning Algorithm in Machine Learning. This algorithm is used to establish a linear relationship between the variables. One of these variables is independent and the other is dependent.
For example, Linear Regression is used to predict the weight of a person based on his/her height. Here, the weight would be the dependent variable and height would be the independent variable.
For more information on Linear Regression, click here.
3) Decision Tree
A Decision Tree, in simple terms, is a graph that uses the branching method to realize the problem and make decisions based on the conditions. It illustrates every possible outcome of a decision and eventually predicts the outcome.
For example, when you buy something on any eCommerce website, it gives you several recommendations based on what you are looking for. This is where a Decision Tree algorithm is used for Classification.
For more information on Decision Tree, click here.
4) Naive Bayes
The Naive Bayes algorithm is mostly used in the cases where prediction needs to be done on a very large dataset. It makes use of Conditional Probability. Conditional Probability is the probability of an event A occurring given that another event B has already occurred.
This algorithm is most commonly used in filtering spam mails in your email account. For example, you received a new mail. The model goes through your old spam mail records and uses the Naive Bayes algorithm to predict if the mail received is spam mail or not.
For more information on Naive Bayes, click here.
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.
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, the model may work on mobile but fail on desktop due to user difference, 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.
Machine Learning is an ever-growing field of Data Science. It has applications in every other sector and helps in growing business. The article introduced you to Data Science and the importance of Machine Learning in Data Science. It helped you understand the 3 different types of Machine Learning in Data Science. The article also briefed you on the 4 most popular and widely used Machine Learning algorithms in Data Science. It also introduced you to some of the most commonly used applications of Machine Learning in Data Science and the challenges you might face while working in this field with possible solutions.
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