Data science engineers will have a clear understanding of machine learning and generative AI. In order to learn complex machine learning concepts, you need to understand more about supervised and unsupervised learning. Whether you are a fresher or an established professional, you need to have a good grasp of the core concepts of machine learning as a data science engineer. Gone are the days where knowing SQL, Power BI, and Excel is enough to be a data science professional. While those skills are crucial, depending on them will take you nowhere in the current competitive world. Did you know that in today’s day and age, an entry-level data scientist will know machine learning and generative AI? Here is a deep dive into what unsupervised and supervised learning is in machine learning and why it is essential for data scientists.
What is Supervised and Unsupervised Learning?
Supervised learning is when a machine is trained with structured and labeled data where each input corresponds to an output. Unsupervised learning involves training the model with unlabeled data that helps uncover patterns, relationships, and structures without any predefined outputs. In unsupervised learning, the machine learning model is trained with unlabeled data that uncovers patterns, structures, or relationships within the data without any predefined outputs.
A Dive into Supervised Learning
Picture a teacher or mentor who guides and trains you to use the correct data. That is what a supervised learning does to a machine learning model. In this learning method, each input has the correct output in the form of an answer or category attached to it. Once this is done, the machine gets a new set of examples that helps in analyzing training data and producing a new set of examples that it analyses the training data and produces the correct outcome from labeled data. Here’s a live example that will help you understand the concept better.
In a basket full of different fruits, if we want a machine to identify a particular set of fruits. The machine will first look at the image of the fruit and narrow down its features like the shape, texture, and color as well. After this, it compares the features with the fruits that it has already learned during training. If the features match those of a pineapple, the machine will predict that the fruit is an apple.
Types and Applications of Supervised Learning
Supervised learning is usually classified as two types of algorithms regression and classification. Regression helps in predicting continuous values like stock prices, house prices, or temperature. They help in connecting and correlating input data to a specific number or value. Few commonly used regression algorithms are linear regression, polynomial regression, lasso regression, and ridge regression.
In classification categorical values like whether a customer will make a purchase decision or not will be made. Did you know that classification algorithms learn how to connect input data to the probability of belonging to different groups or categories? Some commonly used classification algorithms are logistic regression, support vector machines, decision trees, random forests, and naïve baye.
Supervised learning is used to automatically classify images in image search and content moderation. It is used in assisting medical diagnosis by analyzing patient data. It is used to analyze patient history, identifying patterns like specific diseases or conditions. Supervised learning is used to analyze financial transactions and identify patterns that show fraudulent activity that protects customers. It also plays an important role in NLP tasks like sentiment analysis, machine translation, and text summarization, enabling machines to learn and understand human language effectively.
Supervised Learning: Advantages and Disadvantages
Here are a few advantages of supervised learning:
- ● Makes accurate predictions on new and unseen data.
- ● Models increase accuracy, leading to better performance and reliable predictions.
- ● Helps detect spam emails and predict house prices.
- ● Has the ability to handle various computational challenges easily.
- ● Can handle classification and regression, making it flexible in handling different problems.
Here are some disadvantages of supervised learning:
- ● It requires a well labeled dataset which demands a lot of time, effort, and money.
- ● Works well on many tasks but struggles with complex unstructured problems like understanding patterns and abstract ideas.
- ● The models may over fit the training data, perform well on training data but poor on new unstructured data.
- ● Models need to be constantly updated with new labelled data to remain accurate as real world data changes over time.
Understanding Unsupervised Learning
In unsupervised learning, there is not teacher or supervisor involved to guide the machine. Here, the machine has data with no labels or categories. The data is analyzed on its own to find patterns, groups, or any relationships without any prior knowledge. Here, the machine learns by discovering hidden structures within the data without being informed of the correct output. Picture a machine learning model with unlabeled images of dogs and cats. If a data science professional gives a new image that contains both dogs and cats, it will not be able to label them but will group parts of the image based on the similarities and differences in features.
Types and Applications of Unsupervised Learning
Clustering and associate rule learning are two categories of unsupervised learning. Clustering involves grouping similar data points together. Here, clustering algorithms work repeatedly by moving data points closer to the center of their group and farther from points in other groups. This helps in creating clear and meaningful clusters. Associate rule learning helps in finding patterns and relationships between different items in a dataset. Some associate rule learning algorithms are Apriori algorithm, Eclat algorithm, and FP-Growth algorithm.
Unsupervised learning is used to solve anomaly detection to identify unusual patterns or behaviors in data mainly security breaches or system problems. It shows hidden relationships and patterns in scientific data that results in new insights and ideas. It helps in finding similarities in user behavior and preferences to recommended products, movies, or music that aligns with interests. It also helps in identifying groups of customers with similar characteristics that help in targeting marketing campaigns with improved customer service.
Advantages and Disadvantages of Unsupervised Learning
Here are the advantages of unsupervised learning:
- ● Data scientists can start working with large datasets quickly and effectively.
- ● Handles large data by reducing it into simpler forms without losing important patterns, making it manageable and efficient.
- ● Discovers patterns and relationships in data that were previously unknown, offering valuable insights.
- ● Analyzing unlabeled data with meaningful trends and groups for understanding data deeply.
Some disadvantages of unsupervised learning:
- ● Challenges in understanding the effectiveness of the model.
- ● Lack of guidance can result in less precise results for complex issues.
- ● Can be more time-consuming as the grouped data needs to be labeled properly.
- ● Noise in the data can impact results.
Now that you have understood the core concept of supervised and unsupervised learning, you may find it overwhelming on how to implement these concepts practically. Here is where Eduinx will help you out. As a leading edtech institute in Bangalore, our mentors have over a decade of experience in data science and AI. They will help you understand core data science concepts and implement them in a holistic manner. At Eduinx, our mentors will guide you at every point in your data science journey. We also help you in building a good resume and provide career guidance with placement assistance in landing your dream job.
