#SupervisedLearning

[edutech-brijesh]

Machine learning algorithms use data to make predictions and decisions without explicit programming, enabling automation and insights for various applications like healthcare and finance.

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Understand SVM – a powerful ML algorithm for classification and regression. Learn with TCCI Ahmedabad.

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Popular Algorithms Used in Supervised Learning | IABAC

Common algorithms used in supervised learning include Neural Networks, KNN, SVM Decision Trees, Logistic Regression, and Linear Regression. These techniques assist machines in learning from labeled data so they can do real-world tasks like predicting and classification with precision. https://iabac.org/blog/supervised-learning

[dataexpertblog]

Reinforcement Learning: The Next Step After Supervised Learning

Reinforcement Learning (RL) is an advanced machine learning approach that goes beyond supervised learning by enabling systems to learn through trial and error. Unlike supervised learning, which relies on labeled data, RL uses rewards and penalties to optimize decision-making in dynamic environments. It is widely used in robotics, gaming, autonomous systems, and financial modeling. As RL continues to evolve, its applications in real-world problem-solving, such as personalized recommendations and self-driving cars, are expanding rapidly Read More..

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Understanding Decision Trees in Data Mining: Everything You Need to Know

Decision trees are widely used for classification and prediction by splitting data into branches based on conditions. They are easy to interpret, handle both numerical and categorical data, and provide clear visual insights. Commonly applied in AI, finance, and healthcare, decision trees help businesses make data-driven decisions efficiently. Understanding their structure and applications enhances predictive accuracy and analytical capabilities Read More..

[enlightenedsloth]

mGPS-An invisible map on your skin

Ever got confused about whether you have to exit from the next diversion or not, while using Google Maps? A trivial problem, owing to the accuracy of GPS (Global Positioning System). Speaking about accuracy, we now have mGPS (Microbiome Geographic Population Structure). At first, the name seems like biology, geography and demography combined into one, but it’s not that complex. It is an AI tool which can track which place you have recently visited, be it a beach or a city centre.

The researchers found that when you touch a particular surface like a tree in the woods, you pick up bacteria that is unique to that area, advocating that unique populations of bacteria exist in different locations. This phenomenon of the unique existence of microorganisms is called microbiome. Eran Elhaik, who led the study stated that they analyzed extensive datasets of microbiome samples from urban environments, soil and marine ecosystems and trained the AI model to identify unique proportions of it and link them to specific locations. Simply put, it’s like analyzing the unique blend of spices in a dish to figure out which country or region it came from. The analyzing bit is done by the AI tool, which already knows which spices belong to which region as the data has been fed into it.

The research team gathered a vast collection of microbiome samples, including 4135 samples from public transit systems in 53 cities, 237 soil samples from 18 countries, and 131 marine samples from nine different water bodies. The tool was successful at identifying the city source for 92% of the urban samples.

But how was the tool trained anyway, or how are any of the AI tools trained?

First thing first- Data collection and labelling. Both go hand-in-hand, for example, the collected microbiome samples must have been labelled with their geographic origin like country or environment type (urban, soil, marine). Then, after cleaning, the data is fed to the model. The model selection part is also a crucial one. An advanced model like a supervised machine learning model or a deep learning model must have been chosen. To improve the efficiency of the tool, it will be trained continuously on the labelled data of different samples with unique compositions.

What’s even more interesting is its application-

Imagine the cops arresting a set of suspects and running their microorganism orientation through the AI tool to know where they were on the night of the crime, or, the AI tool accurately identifying the location from where a particular virus was picked up and eliminating it from the source itself. By analyzing artefacts through mGPS, archaeologists would be able to track human migration accurately.

Technologies like mGPS are not difficult to build, at least in theory. Execution might be an issue due to the collection and feeding of vast amounts of samples. However, with collaboration and support, it can be executed, and with ease as well. Ultimately, it comes down to the extent of your imagination.

You can build it if you can imagine it.

[fortunatelycoldengineer]

Data Mining Quiz . . . . write your answer in the comment section https://bit.ly/3N3Simx Check Q.No. 46 for more information

[anandshivam2411]

Machine Learning Algorithms for Beginners: A Simple Guide to Getting Started

Machine learning (ML) algorithms are powerful tools that allow computers to learn from data, identify patterns, and make decisions without explicit programming. These algorithms are categorized into three types: supervised learning, unsupervised learning, and reinforcement learning.

Supervised Learning involves training a model on labeled data, where each input has a corresponding output. Common algorithms in this category include linear regression (used for predicting continuous values), logistic regression (for binary classification), and decision trees (which split data based on certain criteria for classification or regression tasks).

Unsupervised Learning is used when there are no labels in the data. The algorithm tries to find hidden patterns or groupings. K-means clustering is a popular algorithm that divides data into clusters, while Principal Component Analysis (PCA) helps reduce data complexity by transforming features.

Reinforcement Learning is based on learning through interaction with an environment to maximize cumulative rewards. An example is Q-learning, where an agent learns which actions to take based on rewards and penalties.

Selecting the right algorithm depends on the problem you want to solve. For beginners, understanding these basic algorithms and experimenting with real-world data is key to mastering machine learning. As you practice, you’ll gain the skills to apply these algorithms effectively.

For deeper knowledge on machine learning algorithms, here is a blog where I learned more about these concepts.

[govindhtech]

Supervised & Unsupervised Learning: What’s The Difference?

This essay covers supervised and unsupervised data science basics. Choose an approach that fits you.

The world is getting “smarter” every day, and firms are using machine learning algorithms to simplify to meet client expectations. Unique purchases alert them to credit card fraud, and facial recognition unlocks phones to detect end-user devices.

Supervised learning and unsupervised learning are the two fundamental methods in machine learning and artificial intelligence (AI). The primary distinction is that one makes use of labeled data to aid in result prediction, whilst the other does not. There are some differences between the two strategies, though, as well as important places where one performs better than the other. To help you select the right course of action for your circumstances, this page explains the distinctions.

What is supervised learning?

Labeled data sets are used in supervised learning, a machine learning technique. These datasets are intended to “supervise” or train algorithms to correctly identify data or forecast results. The model may gauge its accuracy and gain knowledge over time by using labeled inputs and outputs.

When it comes to data mining, supervised learning may be divided into two categories of problems: regression and classification.

To correctly classify test data into distinct groups, such as differentiating between apples and oranges, classification problems employ an algorithm. Alternatively, spam in a different folder from your inbox can be categorized using supervised learning algorithms in the real world. Common classification algorithm types include decision trees, random forests, support vector machines, and linear classifiers.

Another kind of supervised learning technique is regression, which use an algorithm to determine the correlation between dependent and independent variables. Predicting numerical values based on several data sources, such sales revenue estimates for a certain company, is made easier by regression models. Polynomial regression, logistic regression, and linear regression are a few common regression algorithms.

What is unsupervised learning?

Unsupervised learning analyzes and groups unlabeled data sets using machine learning methods. These algorithms are “unsupervised” because they find hidden patterns in data without the assistance of a human.

Three primary tasks are addressed by unsupervised learning models: dimensionality reduction, association, and clustering.

A data mining technique called clustering is used to arrange unlabeled data according to similarities or differences. K-means clustering techniques, for instance, group related data points into groups; the size and granularity of the grouping are indicated by the K value. This method works well for picture compression, market segmentation, and other applications.

Another kind of unsupervised learning technique is association, which looks for links between variables in a given data set using a variety of rules. These techniques, such as “Customers Who Bought This Item Also Bought” suggestions, are commonly applied to recommendation engines and market basket analysis.

When there are too many characteristics in a given data collection, a learning technique called “dimensionality reduction” is applied. It maintains the data integrity while bringing the quantity of data inputs down to a manageable level.

This method is frequently applied during the preprocessing phase of data, such as when autoencoders eliminate noise from visual data to enhance image quality.

The main difference between supervised and unsupervised learning

Using labeled data sets is the primary difference between the two methods. In short, an unsupervised learning method does not employ labeled input and output data, whereas supervised learning does.

The algorithm “learns” from the training data set in supervised learning by repeatedly predicting the data and modifying for the right response. Supervised learning algorithms need human interaction up front to properly identify the data, even though they are typically more accurate than unsupervised learning models. For instance, depending on the time of day, the weather, and other factors, a supervised learning model can forecast how long your commute will take. However, you must first teach it that driving takes longer in rainy conditions.

In contrast, unsupervised learning algorithms find the underlying structure of unlabeled data on their own. Keep in mind that human intervention is still necessary for the output variables to be validated. An unsupervised learning model, for instance, can recognize that online buyers frequently buy many items at once. The rationale behind a recommendation engine grouping baby garments in an order of diapers, applesauce, and sippy cups would need to be confirmed by a data analyst.

Other key differences between supervised and unsupervised learning

Predicting results for fresh data is the aim of supervised learning. You are aware of the kind of outcome you can anticipate in advance. The objective of an unsupervised learning algorithm is to extract knowledge from vast amounts of fresh data. What is unique or intriguing about the data set is determined by the machine learning process itself.

Applications

Among other things, supervised learning models are perfect for sentiment analysis, spam detection, weather forecasting, and pricing forecasts. Unsupervised learning, on the other hand, works well with medical imaging, recommendation engines, anomaly detection, and customer personas.

Complexity

R or Python are used to compute supervised learning, a simple machine learning method. Working with massive volumes of unclassified data requires strong skills in unsupervised learning. Because unsupervised learning models require a sizable training set in order to yield the desired results, they are computationally complex.

Cons

Labeling input and output variables requires experience, and training supervised learning models can take a lot of time. In the meanwhile, without human interaction to evaluate the output variables, unsupervised learning techniques can produce radically erroneous findings.

Supervised versus unsupervised learning: Which is best for you?

How your data scientists evaluate the volume and structure of your data, along with the use case, will determine which strategy is best for you. Make sure you accomplish the following before making your choice:

Analyze the data you entered: Is the data labeled or unlabeled? Do you have professionals who can help with additional labeling?

Specify your objectives: Do you have a persistent, clearly stated issue that needs to be resolved? Or will it be necessary for the algorithm to anticipate new issues?

Examine your algorithmic options: Is there an algorithm that has the same dimensionality (number of features, traits, or characteristics) that you require? Are they able to handle the volume and structure of your data?

Although supervised learning can be very difficult when it comes to classifying large data, the outcomes are very reliable and accurate. Unsupervised learning can process enormous data sets in real time. However, data clustering is less transparent and outcomes are more likely to be inaccurate. Semi-supervised learning can help with this.

Semi-supervised learning: The best of both worlds

Unable to choose between supervised and unsupervised learning? Using a training data collection that contains both labeled and unlabeled data is a happy medium known as semi-supervised learning. It is especially helpful when there is a large amount of data and when it is challenging to extract pertinent features from the data.

For medical imaging, where a modest amount of training data can result in a considerable gain in accuracy, semi-supervised learning is perfect. To help the system better anticipate which individuals would need further medical attention, a radiologist could, for instance, mark a small subset of CT scans for disorders or tumors.

Read more on Govindhtech.com

[academic1995]

Machine learning

Machine learning algorithms are data-driven models that analyze large datasets, identify patterns, and make predictions or decisions without explicit programming. These algorithms range from supervised models like decision trees and regression, which rely on labeled data, to unsupervised methods such as clustering and dimensionality reduction, which reveal hidden structures in data. Reinforcement learning techniques empower systems to learn from feedback and optimize actions in real time, while deep learning algorithms leverage neural networks to excel in complex tasks like image and speech recognition.

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Unlocking the Secrets of LLM Fine Tuning! 🚀✨

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AI & Machine Learning Fundamentals in 2 Hours for Beginners

Welcome to "AI & Machine Learning Fundamentals in 2 Hours for Beginners"! This session is designed to provide a comprehensive introduction to Artificial Intelligence (AI) and Machine Learning (ML), covering essential topics and concepts in a concise, easy-to-understand format. Whether you're a novice or looking to refresh your knowledge, this session is perfect for you.

Video Link - https://youtu.be/AYCul4JiryQ

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SVM Algorithm Explained – TCCI Ahmedabad

Support Vector Machines (SVMs) are supervised machine learning algorithms employed in classification and regression tasks.

Hyperplane: A hyperplane separates data points of different classes in feature space; it can also be called a decision boundary.

Mathematical Representation: w•x + b = 0

Example:

For instance, if we will classify animals as cat (+1) and dog (-1) by weight and height, then the hyperplane could be

3×Weight + 2×Height - 50 = 0

Support Vectors: These data points are the nearest ones to hyperplane. These points influence the orientation and position of hyperplane.

Margin: The margin is the distance between hyperplane and the nearest data point of either class; SVM tries to maximize margin to increase confidence of classifications.

Hard Margin: Assuming that the data is perfectly separable by hyperplane. Therefore, all points must lie outside of the margin.

Soft Margin: It allows a few misclassifications or margin violations for non-linearly separable data.

Kernel Function: Kernel function transforms the data into higher dimensional space to make it linearly separable.

Types of Kernels:

Linear Kernel: Suitable for linearly separable data.

Polynomial Kernel: For curved boundaries.

Radial Basis Function (RBF): Captures complex relationships.

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Deep Reinforcement Learning

Get to grips with deep reinforcement learning in Probabs' advanced course. Learn how to tackle complex decision-making problems in AI and robotics like a pro.

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Understanding Decision Trees in Data Mining: Everything You Need to Know

Decision trees are widely used for classification and prediction by splitting data into branches based on conditions. They are easy to interpret, handle both numerical and categorical data, and provide clear visual insights. Commonly applied in AI, finance, and healthcare, decision trees help businesses make data-driven decisions efficiently. Understanding their structure and applications enhances predictive accuracy and analytical capabilities Read More..

[theaifusion]

Hyperparameter tuning in machine learning

The performance of a machine learning model in the dynamic world of artificial intelligence is crucial, we have various algorithms for finding a solution to a business problem. Some algorithms like linear regression , logistic regression have parameters whose values are fixed so we have to use those models without any modifications for training a model but there are some algorithms out there where the values of parameters are not fixed.

Here's a complete guide to Hyperparameter tuning in machine learning in Python!

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