What Is the Difference Between Supervised and Unsupervised Learning?

The difference between supervised and unsupervised learning is primarily based on the type of data they use and the problems they solve, and this is clearly explained by LEARNS.EDU.VN. Supervised learning utilizes labeled datasets to make predictions, while unsupervised learning uncovers hidden patterns in unlabeled data. Understand the nuances of these approaches to enhance your machine learning skills. Discover more about data analysis, model training, and algorithm selection with LEARNS.EDU.VN.

1. What is Supervised Learning?

Supervised learning is a machine learning approach where the algorithm learns from labeled data. Labeled data means that each example in the dataset comes with a correct answer or output. It is akin to learning with a teacher who provides guidance and correct answers.

In supervised learning, the process generally includes these steps:

• A dataset with input features (like age, salary, or temperature) and corresponding labels (like “yes/no,” “high/low,” or “rainy/sunny”) is provided to the machine.
• The machine learns by finding patterns in the data. For example, it might learn that if the temperature is high, it’s likely to be sunny.
• Once trained, the machine can predict the label for new input data. For instance, if you give it a new temperature value, it can predict whether it will be sunny or rainy.

1.1. Supervised Learning Analogies

1. The Teacher-Student Analogy: Supervised learning is analogous to a teacher guiding a student. The teacher provides examples (labeled data) and explains the correct answers (output labels). This helps the student learn from known outcomes.

*   For example, a teacher shows a child pictures of animals and labels them as “cat” or “dog.” The child learns to recognize the features that distinguish cats from dogs.
*   If the child makes a mistake, the teacher corrects them, helping them improve over time.

This analogy emphasizes the role of labeled data in supervised learning, where the algorithm learns from examples with known outputs, enabling it to generalize and make accurate predictions on new, unseen data.

2. Sorting Mail: Think of sorting mail into categories like “bills,” “ads,” or “personal letters.” This reflects how supervised learning uses labeled data to classify new inputs into predefined categories.

*   You are given labeled examples of each type of mail (e.g., envelopes marked as “bill” or “ad”).
*   By examining these examples, you learn patterns such as bills often having company logos or ads being colorful.
*   Once trained, you can sort new mail into categories even without explicit labels.

1.2. Real-World Applications of Supervised Learning

Supervised learning algorithms have found applications in diverse domains, including:

• Image Classification: Classifying images into predefined categories, such as identifying objects in a picture (e.g., cats, dogs, cars). According to a study by Stanford University, convolutional neural networks (CNNs), a type of supervised learning model, have achieved impressive accuracy in image recognition tasks, often surpassing human-level performance.
• Spam Detection: Identifying spam emails based on patterns in the email content and metadata. Research from the University of California, Berkeley, indicates that machine learning models can accurately filter out spam emails, improving email security and user experience.
• Medical Diagnosis: Predicting diseases based on patient symptoms and medical history. A study published in the Journal of the American Medical Association (JAMA) found that supervised learning models can assist healthcare professionals in diagnosing diseases with high accuracy, leading to earlier and more effective treatments.
• Credit Risk Assessment: Evaluating the creditworthiness of loan applicants based on their financial information. According to data from Experian, machine learning models can help lenders assess credit risk more accurately, reducing losses from loan defaults.
• Sentiment Analysis: Determining the sentiment (positive, negative, or neutral) of text data, such as customer reviews or social media posts. Research from MIT suggests that sentiment analysis models can provide valuable insights into customer opinions and preferences, enabling businesses to make data-driven decisions.

These examples demonstrate the versatility and impact of supervised learning algorithms in solving real-world problems across various industries.

1.3. Advantages of Supervised Learning

• Clear Guidance: Labeled data provides clear guidance for the model, making it easier to learn and generalize.
• Accurate Predictions: With sufficient labeled data, supervised learning models can achieve high accuracy in predicting outcomes.
• Wide Applicability: Supervised learning can be applied to various types of problems, including classification and regression tasks.
• Easy to Evaluate: Supervised learning models can be easily evaluated using labeled test data.

1.4. Disadvantages of Supervised Learning

• Requires Labeled Data: Labeled data can be expensive and time-consuming to obtain.
• Overfitting: Supervised learning models are prone to overfitting, especially when the dataset is small or noisy.
• Bias: Supervised learning models can inherit biases from the labeled data, leading to unfair or discriminatory outcomes.
• Limited to Known Outcomes: Supervised learning models can only predict outcomes that are present in the labeled data.

To overcome these disadvantages, data scientists at LEARNS.EDU.VN use techniques like cross-validation, regularization, and ensemble methods to improve the performance and generalization of supervised learning models.

2. What is Unsupervised Learning?

Unsupervised learning is a type of machine learning where the algorithm learns from unlabeled data. Here, the machine is given a dataset with only input features (like customer purchase history or website click patterns) but no labels.

The machine then tries to find structure in the data. It might group similar data points together or identify trends. At last, it provides insights, such as clusters of similar data or patterns that were not obvious before. This is like letting a child explore and learn on their own without a teacher to find hidden patterns or groupings in the data on its own.

2.1. Unsupervised Learning Analogies

1. Sorting Books Without Labels: Imagine you are given a box of books with no labels or categories. Your task is to organize them:

*   You notice that some books are mystery novels, so you group them together.
*   Others are textbooks, which you set aside in a separate pile.
*   Comic books form another group because of their distinct style.

Here, you create groups based on the books’ characteristics (e.g., genre, content) without any prior guidance. This reflects how unsupervised learning clusters data based on similarities.

This analogy reflects customer segmentation in marketing. Businesses use unsupervised learning to group customers based on purchasing behavior, preferences, or demographics, enabling targeted marketing strategies. According to a report by McKinsey, companies that leverage customer segmentation effectively can achieve up to a 5% increase in revenue.

2. Exploring a New City: Imagine visiting a new city without a map or guide. You explore and start grouping landmarks:

*   Buildings with tall spires might be grouped as churches.
*   Open spaces with greenery might be categorized as parks.
*   Streets with lots of shops could be grouped as markets.

You’re identifying patterns and organizing your observations independently, much like how unsupervised learning identifies patterns in data.

This analogy mirrors anomaly detection in cybersecurity. For example, unsupervised learning algorithms analyze network traffic and identify unusual patterns that could indicate potential cyberattacks. A study by the SANS Institute found that unsupervised learning can significantly improve the detection of advanced persistent threats (APTs) by identifying anomalous network behavior that may go unnoticed by traditional security systems.

2.2. Real-World Applications of Unsupervised Learning

• Customer Segmentation: Grouping customers based on purchasing behavior, demographics, or preferences. According to a report by Bain & Company, businesses that excel at customer segmentation can increase profits by as much as 15%.
• Anomaly Detection: Identifying unusual patterns or outliers in data, such as fraudulent transactions or network intrusions. Research from the University of Oxford suggests that anomaly detection algorithms can improve the accuracy of fraud detection systems, reducing financial losses for businesses and consumers.
• Dimensionality Reduction: Reducing the number of variables in a dataset while preserving its essential structure. A study published in the journal Nature found that dimensionality reduction techniques can improve the performance of machine learning models by reducing noise and computational complexity.
• Recommendation Systems: Recommending products or content to users based on their past behavior or preferences. Netflix, for example, uses unsupervised learning to analyze viewing patterns and recommend movies and TV shows to its subscribers.
• Topic Modeling: Discovering the underlying topics or themes in a collection of documents. Researchers at Columbia University have developed topic modeling algorithms that can automatically extract relevant topics from large text corpora, such as news articles or scientific publications.

2.3. Advantages of Unsupervised Learning

• No Labeled Data Required: Unsupervised learning can be applied to unlabeled data, which is often more readily available than labeled data.
• Discovering Hidden Patterns: Unsupervised learning can uncover hidden patterns or structures in data that may not be apparent through manual analysis.
• Exploratory Data Analysis: Unsupervised learning is useful for exploratory data analysis, helping to gain insights into the data and generate hypotheses.
• Versatility: Unsupervised learning can be used for various tasks, including clustering, dimensionality reduction, and anomaly detection.

2.4. Disadvantages of Unsupervised Learning

• Difficult to Evaluate: Evaluating the performance of unsupervised learning models can be challenging, as there are no labels to compare against.
• Subjective Interpretation: The interpretation of results from unsupervised learning models can be subjective and may require domain expertise.
• Computational Complexity: Some unsupervised learning algorithms can be computationally intensive, especially for large datasets.
• Sensitive to Data Quality: Unsupervised learning models are sensitive to the quality of the data, and noisy or incomplete data can lead to poor results.

At LEARNS.EDU.VN, data scientists focus on robust techniques and careful validation to maximize the benefits and minimize the drawbacks of unsupervised learning in real-world applications.

3. Supervised vs. Unsupervised Learning: Key Differences

Aspect	Supervised Learning	Unsupervised Learning
Input Data	Labeled data (input features + corresponding outputs)	Unlabeled data (only input features, no outputs)
Goal	Predict outcomes or classify data based on labels	Discover hidden patterns, structures, or groupings
Computational Complexity	Less complex due to clear guidance	More complex as the model must find patterns unaided
Types	Classification (discrete) or regression (continuous)	Clustering, association, dimensionality reduction
Testing the Model	Model can be tested using labeled test data	Difficult to test; requires subjective interpretation

The choice between supervised and unsupervised learning depends on your data and the problem you’re solving. If you have labels, go for supervised learning; if not, unsupervised learning is your tool. At LEARNS.EDU.VN, we offer courses and resources to help you master both approaches.

3.1. Data Labeling

Supervised Learning:

• Requires meticulously labeled datasets. Each input data point must have a corresponding correct output label.
• The labeling process can be time-consuming and expensive, as it often requires human expertise to ensure accuracy.
• The quality of the labels directly impacts the performance of the supervised learning model. Inaccurate or inconsistent labels can lead to poor results.

Unsupervised Learning:

• Operates on unlabeled data, which is often more readily available than labeled data.
• There is no need for manual labeling, which saves time and resources.
• The absence of labels allows the algorithm to discover patterns and structures in the data without any predefined categories or outputs.

3.2. Model Training

Supervised Learning:

• The model is trained using the labeled dataset, where it learns the relationship between the input features and the output labels.
• The training process involves adjusting the model’s parameters to minimize the difference between the predicted outputs and the actual labels.
• The model’s performance is evaluated using a separate labeled test dataset, where the predicted outputs are compared to the true labels.

Unsupervised Learning:

• The model is trained on the unlabeled dataset, where it attempts to uncover hidden patterns, structures, or groupings in the data.
• The training process involves algorithms that can identify clusters of similar data points, reduce the dimensionality of the data, or detect anomalies.
• The model’s performance is often evaluated using subjective measures or domain expertise, as there are no labels to compare against.

3.3. Interpretability

Supervised Learning:

• The model’s decision-making process is often more interpretable than in unsupervised learning.
• The relationship between the input features and the output labels is explicitly learned during training, making it easier to understand how the model arrives at its predictions.
• Feature importance techniques can be used to identify the most influential features in the model, providing further insights into its behavior.

Unsupervised Learning:

• The model’s decision-making process can be more challenging to interpret, as the algorithm is discovering patterns and structures in the data without any predefined categories or outputs.
• The interpretation of results from unsupervised learning models often requires domain expertise to make sense of the discovered patterns.
• Techniques like visualization and dimensionality reduction can help to gain insights into the model’s behavior, but the interpretation remains subjective.

3.4. Bias and Fairness

Supervised Learning:

• Supervised learning models can inherit biases from the labeled data, leading to unfair or discriminatory outcomes.
• If the labeled dataset reflects existing biases in society, the model may perpetuate or amplify these biases in its predictions.
• It is essential to carefully examine the labeled data for potential biases and take steps to mitigate them during the model development process.

Unsupervised Learning:

• Unsupervised learning models are less susceptible to biases from labeled data, as they operate on unlabeled data.
• However, unsupervised learning models can still be influenced by biases in the data itself, such as sampling bias or measurement bias.
• It is crucial to carefully consider the potential sources of bias in the data and take steps to address them during the data preprocessing and analysis stages.

3.5. Iterative Nature

Supervised Learning:

• Supervised learning is typically an iterative process, where the model is trained, evaluated, and refined based on its performance on a labeled test dataset.
• The model’s parameters are adjusted iteratively to minimize the difference between the predicted outputs and the actual labels.
• This iterative process continues until the model achieves satisfactory performance on the test dataset.

Unsupervised Learning:

• Unsupervised learning is often an iterative process as well, where the model’s parameters or structure are adjusted iteratively to improve its ability to uncover patterns or structures in the data.
• The iterative process may involve techniques like clustering refinement, dimensionality reduction optimization, or anomaly detection threshold adjustment.
• The iterative process continues until the model achieves satisfactory performance based on subjective measures or domain expertise.

4. Practical Examples Showcasing Supervised and Unsupervised Learning

To provide a clearer understanding of how supervised and unsupervised learning are applied in real-world scenarios, let’s explore several practical examples showcasing their distinct applications:

4.1. Supervised Learning Examples

1. Fraud Detection in Financial Transactions:

• Scenario: A financial institution aims to detect fraudulent transactions in real-time to prevent financial losses.
• Approach: Supervised learning algorithms, such as decision trees or neural networks, can be trained on a labeled dataset of past transactions, where each transaction is labeled as either “fraudulent” or “non-fraudulent.”
• Outcome: The trained model can accurately classify new transactions as either fraudulent or non-fraudulent based on patterns in the transaction data, such as transaction amount, location, and time.

2. Medical Diagnosis Based on Patient Data:

• Scenario: A hospital wants to automate the diagnosis of certain diseases based on patient symptoms, medical history, and test results.
• Approach: Supervised learning algorithms, such as support vector machines (SVMs) or random forests, can be trained on a labeled dataset of patient records, where each record includes the patient’s symptoms, medical history, test results, and the corresponding diagnosis.
• Outcome: The trained model can accurately predict the diagnosis for new patients based on their symptoms, medical history, and test results, assisting healthcare professionals in making timely and accurate diagnoses.

3. Predictive Maintenance in Manufacturing:

• Scenario: A manufacturing company wants to predict when equipment is likely to fail to optimize maintenance schedules and reduce downtime.
• Approach: Supervised learning algorithms, such as regression models or time series analysis, can be trained on a labeled dataset of equipment sensor data, where each data point includes sensor readings, maintenance records, and equipment failure events.
• Outcome: The trained model can accurately predict when equipment is likely to fail based on patterns in the sensor data, allowing the company to schedule maintenance proactively and prevent costly equipment failures.

4.2. Unsupervised Learning Examples

1. Customer Segmentation in Retail:

• Scenario: A retail company wants to segment its customers into distinct groups to tailor marketing campaigns and personalize customer experiences.
• Approach: Unsupervised learning algorithms, such as k-means clustering or hierarchical clustering, can be applied to a dataset of customer data, including demographics, purchase history, and website browsing behavior.
• Outcome: The algorithm groups customers into distinct segments based on their similarities, such as “high-value customers,” “occasional shoppers,” and “price-sensitive buyers,” allowing the company to target each segment with personalized marketing messages and offers.

2. Anomaly Detection in Cybersecurity:

• Scenario: A cybersecurity company wants to detect anomalous network traffic patterns that may indicate potential cyberattacks.
• Approach: Unsupervised learning algorithms, such as one-class SVMs or autoencoders, can be trained on a dataset of normal network traffic data.
• Outcome: The trained model can identify unusual network traffic patterns that deviate significantly from the normal patterns, flagging them as potential cyberattacks for further investigation.

3. Topic Modeling in Document Analysis:

• Scenario: A news organization wants to discover the underlying topics or themes in a large collection of news articles.
• Approach: Unsupervised learning algorithms, such as latent Dirichlet allocation (LDA) or non-negative matrix factorization (NMF), can be applied to a dataset of news articles.
• Outcome: The algorithm identifies the most prevalent topics in the collection, such as “politics,” “sports,” and “entertainment,” and assigns each article to one or more topics, allowing the news organization to organize and categorize its content more effectively.

5. How to Choose Between Supervised and Unsupervised Learning

Choosing between supervised and unsupervised learning depends on several factors, including the availability of labeled data, the nature of the problem you are trying to solve, and the desired outcome. Here are some guidelines to help you make the right decision:

5.1. Consider the Availability of Labeled Data

• If you have access to a large, high-quality labeled dataset, supervised learning is likely the better choice. Labeled data provides clear guidance for the model and enables it to learn accurate relationships between inputs and outputs.
• If you only have access to unlabeled data, unsupervised learning is the only option. Unsupervised learning can uncover hidden patterns and structures in the data without any prior knowledge or guidance.

5.2. Define the Problem and Desired Outcome

• If you have a specific prediction or classification task in mind, supervised learning is typically the way to go. Supervised learning algorithms are designed to learn from labeled data and make accurate predictions on new, unseen data.
• If you are interested in exploring the data and discovering hidden patterns or structures, unsupervised learning is a more appropriate choice. Unsupervised learning can help you gain insights into the data and generate hypotheses for further investigation.

5.3. Evaluate the Complexity of the Problem

• Supervised learning is generally less complex than unsupervised learning, as the model has clear guidance from the labeled data. If the problem is relatively simple and the relationships between inputs and outputs are well-defined, supervised learning can provide accurate and reliable results.
• Unsupervised learning can be more complex, as the model must find patterns and structures in the data without any prior knowledge. If the problem is complex and the relationships between variables are not well-defined, unsupervised learning can help you uncover hidden insights that would be difficult to discover manually.

5.4. Consider the Interpretability of the Results

• Supervised learning models are often more interpretable than unsupervised learning models. The relationships between inputs and outputs are explicitly learned during training, making it easier to understand how the model arrives at its predictions.
• Unsupervised learning models can be more challenging to interpret, as the algorithm is discovering patterns and structures in the data without any predefined categories or outputs. The interpretation of results from unsupervised learning models often requires domain expertise to make sense of the discovered patterns.

5.5. Combining Supervised and Unsupervised Learning

In some cases, it may be beneficial to combine supervised and unsupervised learning techniques to solve a problem. For example, you could use unsupervised learning to cluster the data into distinct groups and then use supervised learning to build a predictive model for each cluster. This approach can improve the accuracy and interpretability of the results.

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7. The Future Trends in Supervised and Unsupervised Learning

As the field of machine learning continues to evolve, both supervised and unsupervised learning are undergoing significant advancements and shaping the future of various industries. Here are some key trends to watch out for:

7.1. Advancements in Supervised Learning

1. Explainable AI (XAI):

   • As supervised learning models become more complex, there is a growing need for explainable AI techniques that can provide insights into how these models make decisions.
   • XAI methods aim to make the decision-making process of supervised learning models more transparent and understandable, enabling users to trust and validate the model’s predictions.
   • Research in XAI is focused on developing techniques that can identify the most influential features, explain the reasoning behind individual predictions, and provide visualizations of the model’s decision-making process.

2. Federated Learning:

   • Federated learning is a distributed machine learning approach that enables supervised learning models to be trained on decentralized data sources without sharing the data itself.
   • This approach is particularly useful in scenarios where data privacy is a concern, such as healthcare and finance.
   • Federated learning algorithms aggregate the knowledge gained from each local model to create a global model, while keeping the data private and secure.

3. Automated Machine Learning (AutoML):

   • AutoML tools automate the process of building and deploying supervised learning models, making it easier for non-experts to leverage machine learning.
   • AutoML platforms can automatically select the best model architecture, tune hyperparameters, and evaluate model performance, significantly reducing the time and effort required to build a supervised learning model.
   • AutoML is democratizing machine learning by making it accessible to a wider range of users, regardless of their technical expertise.

7.2. Advancements in Unsupervised Learning

1. Self-Supervised Learning:

   • Self-supervised learning is a type of unsupervised learning where the model learns from unlabeled data by creating its own supervisory signals.
   • For example, a self-supervised learning model might be trained to predict missing parts of an image or to predict the next word in a sentence.
   • Self-supervised learning is proving to be a powerful technique for learning representations from unlabeled data, which can then be used for downstream supervised learning tasks.

2. Contrastive Learning:

   • Contrastive learning is an unsupervised learning approach that learns representations by comparing similar and dissimilar data points.
   • The model is trained to bring similar data points closer together in the representation space, while pushing dissimilar data points further apart.
   • Contrastive learning has shown promising results in various applications, including image recognition, natural language processing, and recommendation systems.

3. Generative Models:

   • Generative models, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), are unsupervised learning models that can generate new data samples that are similar to the training data.
   • Generative models have various applications, including image synthesis, data augmentation, and anomaly detection.
   • Researchers are exploring new architectures and training techniques to improve the quality and diversity of the generated data.

7.3. Emerging Trends in Both Supervised and Unsupervised Learning

1. Multimodal Learning:

   • Multimodal learning involves training models that can process and integrate information from multiple data modalities, such as images, text, and audio.
   • Both supervised and unsupervised learning techniques can be used for multimodal learning.
   • Multimodal learning is enabling new applications in areas such as robotics, healthcare, and entertainment.

2. Reinforcement Learning:

   • Reinforcement learning is a type of machine learning where the model learns to make decisions in an environment to maximize a reward signal.
   • Reinforcement learning can be used in conjunction with both supervised and unsupervised learning techniques.
   • For example, supervised learning can be used to train a model to predict the optimal action in a given state, while unsupervised learning can be used to discover the underlying structure of the environment.

3. Edge Computing:

   • Edge computing involves processing data closer to the source of the data, rather than sending it to a central server.
   • Edge computing is enabling new applications of machine learning in areas such as autonomous vehicles, industrial automation, and smart cities.
   • Both supervised and unsupervised learning models can be deployed on edge devices to perform real-time analysis and decision-making.

8. FAQ about Supervised and Unsupervised Learning

1. What is the primary difference between supervised and unsupervised learning?

Supervised learning uses labeled data for training, while unsupervised learning uses unlabeled data to discover patterns.

2. When should I use supervised learning?

Use supervised learning when you have labeled data and need to predict specific outcomes or classify data based on known labels.

3. When is unsupervised learning more appropriate?

Unsupervised learning is ideal when you want to explore data, discover hidden patterns, or group similar data points without predefined labels.

4. Can supervised learning be used for clustering?

No, clustering is typically an unsupervised learning task. Supervised learning focuses on prediction or classification based on labeled data.

5. How do I evaluate the performance of an unsupervised learning model?

Evaluating unsupervised learning models can be challenging, as there are no labels to compare against. Subjective measures or domain expertise are often required.

6. What are some common applications of supervised learning?

Common applications include image classification, spam detection, medical diagnosis, and credit risk assessment.

7. What are some typical uses of unsupervised learning?

Typical uses include customer segmentation, anomaly detection, dimensionality reduction, and recommendation systems.

8. Is it possible to combine supervised and unsupervised learning?

Yes, combining both techniques can be beneficial in certain scenarios, such as using unsupervised learning for data preprocessing and then applying supervised learning for prediction.

9. How does the complexity of supervised learning compare to that of unsupervised learning?

Supervised learning is generally less complex due to clear guidance from labeled data, while unsupervised learning can be more complex as the model must find patterns unaided.

10. What is the impact of data quality on supervised and unsupervised learning?

Both are sensitive to data quality, but supervised learning is particularly affected by label accuracy, while unsupervised learning can be impacted by noise and incompleteness in the data.

9. AIDA Model in the Context of Supervised and Unsupervised Learning

The AIDA model (Attention, Interest, Desire, Action) is a marketing and advertising framework that outlines the stages a potential customer goes through before making a purchase or taking a desired action. In the context of supervised and unsupervised learning, we can adapt the AIDA model to guide our audience through understanding and appreciating these machine-learning concepts. Here’s how we can apply the AIDA model:

9.1. Attention (Awareness)

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9.2. Interest

Objective: Generate interest in supervised and unsupervised learning by explaining what they are and how they work in a simple, engaging way.

• Provide clear and concise definitions of supervised and unsupervised learning, using analogies and real-world examples to make the concepts more relatable.
• Explain the key differences between supervised and unsupervised learning, using a table or infographic to highlight the main points.
• Showcase the diverse applications of supervised and unsupervised learning across various industries, such as finance, healthcare, and e-commerce.
• Share success stories or case studies that demonstrate the impact of machine learning on real-world problems.

9.3. Desire

Objective: Create a desire to learn more about supervised and unsupervised learning and explore the resources available at LEARNS.EDU.VN.

• Emphasize the benefits of mastering supervised and unsupervised learning, such as:
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9.4. Action

Objective: Encourage the audience to take action and explore the learning resources available at LEARNS.EDU.VN.

• Include a clear and compelling call to action, such as: “Visit LEARNS.EDU.VN to start your machine-learning journey today!”
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