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<!doctype html>
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<title>News App</title>
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<body><nav>
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<a id="aa" href="web.html" >Back To Home
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<p>
<div id="div1">
<h1> What is ML ? </h1>
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<br>
machine Learning definition is the science of teaching machines how to learn by themselves.<br>
Machine learning is a fascinating field within artificial intelligence (AI) that enables
computers to learn and improve their performance without being explicitly programmed.<br>
we can take an example :<br>
There are many things one of them is calculation if humans are asked to multiply 247790675645343 with 34345656776867990656 then
they will take a lot of time or many of them will be unable to do this calculation , but computer will perform it within few of
seconds.<br>
In other way<br>
let's take an example of driving a car or speaking , these things are easily done by humans . humans are expert in it than a
computer.<br>
There are several things in which humans are better than a computer.<br>
Machine Learning is a technique to train a machine to behave like humans.<br>
we can say :<br>
<b> Machine Learning is set of techniques to make computers better at doing things that humans can do better than machines</b><br>
Examples of Machine Learning are :<br><br><br>
<div id="Image1">
<img src="https://images.unsplash.com/photo-1507146153580-69a1fe6d8aa1?w=500&auto=format&fit=crop&q=60&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxzZWFyY2h8MjB8fE1hY2hpbmUlMjBMZWFybmluZ3xlbnwwfHwwfHx8MA%3D%3D" alt="img">
</div><br><br>
1] Alexa (can talk like humans and can communicate with humans)<br>
2] Youtube ( we get recommendations of videos by watch history)<br>
3] self-Drive Car (a car without driver)<br>
Machine learning uses algorithms trained on data sets to create self-learning models capable of
predicting outcomes and classifying information.
Unlike traditional programming, where rules are explicitly defined, machine learning models learn from
data and adapt over time.
</p>
<b>why on earth would we want machines to learn by themselves?</b>
<p> Machines can do high-frequency repetitive tasks with high accuracy without getting bored.
<h1>How does ML works..</h1>
Machine learning (ML) works by analyzing data and identifying patterns within it. Here's a breakdown of the core process:<br>
<b>1. Data Acquisition:</b><br>
Large amounts of data are collected relevant to the task at hand. This could be anything from text and images to financial records or sensor readings.<br>
<b>2. Data Preparation:</b><br>
The data is cleaned and preprocessed to ensure it's suitable for analysis. This might involve removing inconsistencies, formatting the data, and ensuring it's in a usable format.<br>
<b>3. Model Selection:</b><br>
A specific machine learning algorithm is chosen based on the desired outcome. Different algorithms work better for different types of tasks.<br>
<b>4. Training:</b><br>
The chosen algorithm is "trained" using the prepared data. This involves the algorithm analyzing the data and identifying patterns and relationships between different variables.
Imagine showing the algorithm thousands of pictures of cats and dogs, labeled accordingly. It learns to identify the features that differentiate them.<br>
<b>5. Evaluation:</b><br>
Once trained, the model is tested on new, unseen data. This helps assess its accuracy and identify areas where it might need improvement.<br>
<b>6. Refinement:</b><br>
If the model doesn't perform well enough, it's further refined by adjusting the algorithm or providing more training data. This iterative process continues until the desired level of accuracy is achieved.<br><br><br>
<div id="Image1" >
<img class="image2"src="https://www.simplilearn.com/ice9/free_resources_article_thumb/Real-life-Applications-of-Data-Science-Deep-Learning-and-Artificial-Intelligence-1.jpg" alt="img">
</div>
<h1> History Of ML</h1>
Machine learning's journey began in the 1940s with theoretical foundations for neural networks and the Turing Test.<br>
Breakthroughs like the Perceptron and nearest neighbor algorithm fueled progress, followed by a period of decline in the 70s-80s.<br>
However, powerful algorithms and the explosion of data in the late 20th and 21st centuries led to a resurgence, making machine<br>
learning a ubiquitous force in various fields today.<br>
<h1> Applications of Machine Learning </h1>
<b>1. Personalized Recommendations:</b><br>
Have you ever noticed how Amazon suggests products you might like, or how Netflix recommends shows you'll probably enjoy? This is done through ML algorithms that analyze your past purchases, browsing history, and ratings to identify patterns and predict your future preferences.<br>
<b>2. Virtual Assistants:</b>
Virtual assistants like Siri, Alexa, and Google Assistant use ML for speech recognition, natural language processing, and understanding your intent. They learn from your voice commands and preferences to provide increasingly personalized responses and assistance.<br>
<b>3. Smart Filters:</b><br>
Your email spam filter and social media content filtering are powered by ML algorithms. These algorithms analyze vast amounts of data to identify patterns associated with spam or inappropriate content, effectively keeping your inbox and feeds clean.<br>
<b>4. Traffic Prediction and Navigation:</b>
Traffic navigation apps like Google Maps use real-time data and historical traffic patterns to predict traffic conditions and suggest optimal routes. This helps you avoid congestion and reach your destination faster.<br>
<b>5. Facial Recognition:</b><br>
Facial recognition technology, used in unlocking smartphones or tagging photos on Facebook, relies on ML algorithms to analyze facial features and match them against a database.<br>
<b>6. Online Fraud Detection:</b><br>
Financial institutions and online payment platforms use ML to detect fraudulent transactions. These algorithms analyze patterns in spending habits and identify suspicious activities to protect your financial security.<br>
<b>7. Search Engine Optimization:</b><br>
When you search on Google, the results you see are ranked based on ML algorithms that consider various factors like keyword relevance, website quality, and user behavior. This ensures you find the most relevant information quickly.<br>
<b>8. Image and Video Processing:</b><br>
Photo editing apps often use ML features like auto-enhancement, red-eye removal, and background replacement. Similarly, video streaming services use ML for content recommendations and video quality optimization.
These are just a few examples, and the applications of ML in our daily lives continue to grow and evolve. As technology advances, we can expect even more seamless and personalized experiences powered by machine learning.<br>
<h2>Machine Learning problems can be divided into 3 broad classes:</h2>
<div id="Image1">
<img id="image3" src="https://www.wordstream.com/wp-content/uploads/2021/07/machine-learning1-1.png" alt="img">
</div>
<b>1] Supervised Machine Learning:</b><br>
When you have past data with outcomes (labels in machine learning terminology)
and you want to predict the outcomes for the future you would use Supervised Machine Learning algorithms.
Supervised Machine Learning problems can again be divided into 2 kinds of problems:<br>
<b>a] Classification Problems:</b><br>
When you want to classify outcomes into different classes. For example whether the floor needs
cleaning/mopping is a classification problem. The outcome can fall into one of the classes Yes or No.
Similarly, whether a customer would default on their loan or not is a classification problem which is of high interest to any Bank.
<b>b]Regression Problem:</b><br>
When you are interested in answering how much these problems would fall under the Regression umbrella.
For example how much cleaning needs to be done is a Regression problem. Or what is the expected amount of default from a customer
is a Regression problem<br>
<b>2]Unsupervised Machine Learning:</b><br>
There are times when you don't want to exactly predict an Outcome.
You just want to perform a segmentation or clustering. For example a bank would want to have a segmentation of
its customers to understand their behavior.<br>
<b>3]Reinforcement Learning:</b><br>
Reinforcement Learning is said to be the hope of true artificial intelligence.
And it is rightly said so because the potential that Reinforcement Learning possesses is immense.
It is a slightly complex topic as compared to traditional machine learning but an equally crucial one for the future.<br>
<h1>Machine Learning vs. Artificial Intelligence:</h1>
AI aims to create machines with human-like cognitive abilities.
Machine learning specifically refers to using algorithms and data sets to achieve AI goals.
In summary, machine learning powers many digital services we use daily, from personalized recommendations to stock market predictions.
It's an exciting field with vast potential! <br>
For more in-depth learning, consider exploring beginner-friendly machine learning courses on platforms like Coursera.1<br>
Machine learning is like teaching a computer to learn from examples. Imagine you have a smart friend who learns how to identify different animals by looking at pictures.
Initially, you show your friend pictures of cats and dogs, and you tell them which one is which. Over time, your friend starts noticing patterns: cats have pointy ears, while dogs have floppy ears. They also notice that cats are usually smaller than dogs.<br>
Now, when you show your friend a new picture of an animal, they can guess whether it's a cat or a dog based on what they've learned from previous examples. They don't need you to tell them anymore!<br>
Similarly, in machine learning: <br>
Data: We collect lots of examples (like pictures of cats and dogs).<br>
Algorithms: These are like the smart friend. They learn from the examples and find patterns.<br>
Predictions: Once trained, the algorithms can make predictions on new data (like guessing whether a new picture is a cat or a dog).<br>
Machine learning is used in many areas, from recommending movies on Netflix to predicting stock prices.<br>
It's like teaching computers to be smart by showing them lots of examples! <br>
<div id="Image1">
<img id="image4"src="https://media.licdn.com/dms/image/D5612AQFhVaGelozlsQ/article-cover_image-shrink_720_1280/0/1702597301870?e=2147483647&v=beta&t=80XebQa7EtU72Sqwz3xa82yVqt0jw3I35DPhGMyl4rM" alt="img">
</div>
</div>
<div id="div2">
<br>
<b> Want to Learn more about Machine Learning ? </b> <br>
<b> Follow this Link to Explore More... <a id="tag" href="LearnML.html">Explore-ML</a></b>
</div>
</p>
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