#Learn machine learning

[dishachrista]

Discovering the Wonders of Machine Learning and Its Advantages:

Have you ever wondered how computers can learn and make choices all by themselves? It's like teaching them to be smart on their own. That's what machine learning is, and it's changing the way we do things. Let's take a journey to find out why machine learning is so amazing. We'll talk about things like helping computers understand languages, predicting the weather, and even teaching them to drive cars! So, let's explore the magic of machine learning.

The Good Things About Machine Learning:

Understanding InformationThink of machine learning as a super-smart helper that can deal with really big and complicated sets of information. Humans might get confused with all that data, but machine learning can assist us in making decisions based on it.

Changing the GameImagine you have a giant puzzle with lots of pieces. You know they make a picture, but it's just too hard to put it together by yourself. Machine learning is like a super-fast puzzle solver. It not only puts the puzzle together but also shows you amazing things about the picture that you might have missed.

Making Tasks SimpleOne cool thing about machine learning is that it can do boring and repetitive tasks for us. Think about typing lots of numbers into a computer spreadsheet – it can do that for us, and it hardly ever makes mistakes. This means we can use our time for more fun stuff.

Getting Things RightMachine learning models are really good at certain jobs. For example, they can tell if an email is spam much better than we can. That's super useful because we don't want our email inboxes to be filled with spam. It's like having a superhero to keep our emails clean.

Learning and Getting BetterThe more a machine learning model sees, the smarter it becomes. It learns from new information and gets better at what it does. This is really helpful in jobs where things change a lot, like predicting the stock market or understanding what people are talking about on social media.

Personalizing EverythingHave you ever noticed that websites like Netflix or Amazon suggest things you might like? That's machine learning in action. It watches what you do and recommends things you'll enjoy. It's like having a personal shopper who knows your tastes.

Handling Lots of StuffMachine learning models are like super chefs who can cook for a huge party. They can manage a massive amount of data and make quick decisions. This is really important for things like banks, which need to handle lots of transactions quickly and accurately.

Saving Time and MoneyMachine learning can save businesses a ton of time and money. For example, it can predict when machines in a factory might break, so they can be fixed before they cause big problems. This saves a lot of money because it's cheaper to fix things before they break.

Solving Tricky ProblemsSome problems are really tough, like understanding different languages, recognizing objects in pictures, or even playing complicated games. Machine learning can take on these challenges and find solutions that might be hard for humans.

Learning from AnythingMachine learning can learn from almost anything, even from things that don't seem like regular data. For example, it can learn from written words, pictures, or videos. This helps businesses understand what people think and like.

Quick Decision-MakingImagine being in a self-driving car. It needs to make fast decisions to keep you safe. Machine learning helps it do that by processing data from sensors and making decisions in real-time.

Creating Cool ThingsMachine learning is the technology behind cool stuff like virtual assistants (like Siri or Alexa) and language translation tools. These devices make life simpler and more enjoyable, like having a helpful friend who can speak every language.

Being Fair and JustSometimes, people can make unfair decisions because of their biases. Machine learning can be set up to be fair and impartial. It helps make choices in things like hiring or lending money based on facts, not feelings.

Driving DiscoveriesMachine learning isn't only for businesses; it also helps scientists. They use it to study complex data and make discoveries in areas like genetics, space exploration, and materials science. It's like having a super microscope that shows things we couldn't see before.

Protecting Our PlanetMachine learning can also help us take care of the environment. It's used to keep an eye on pollution, track changes in the climate, and predict natural disasters. By studying a lot of data, we can make better decisions to protect the Earth.

Improving HealthcareHealthcare is getting a boost from machine learning. It helps doctors diagnose diseases, discover new medicines, and keep track of patients. It's like having a super-smart medical assistant.

In summary, machine learning is a superpower that's changing the world. It makes handling data easy, automates tasks, personalizes experiences, and solves tough problems. It's fair, it's quick, and it's shaping the future. With machine learning, the possibilities are endless. If you want to learn more about machine learning or need help using it in your business, you can ask experts or take special courses. ACTE Institute offers courses that can give you the knowledge and skills to do well in this field. Think about all the amazing chances for analysing data, automating tasks, and making decisions that machine learning offers.

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[lipikamisakameera]

10 Job-Ready Projects You Should Complete After Taking a Machine Learning Course

Beginner machine learning projects are a necessary part of your machine learning portfolio. If you want to pursue a career in machine learning, it's important that you prepare your portfolio first.  

It's the first step toward impressing potential employers who might hire you in your desired job role.  

10 Beginner Machine Learning Projects 

In this article, we have listed ten machine learning courses for beginners. You can complete these projects on your own and get ready for your potential employer to hire you.  

1. Kaggle Titanic Prediction 

If you want to take on beginner machine learning projects right after completing your machine learning course, you can start with Kaggle Titanic Prediction. The project is available at Kaggle Titanic.  

This project has a dataset about the passengers who were travelling on the Titanic. The data set includes different information like the age, cabin, ticket fare, and gender of each of the passengers travelling.  

The data set presents a simple binary classification problem. The learner has to predict the particular passenger who survived the crash.  

2. House Price Prediction  

House prices data is a great course to start with. If you're looking for beginner machine learning projects, you can try this dataset available at Kaggle. The price of a specific house is the target variable of this project.   

As a machine learning expert, you'll have to predict information such as house area, number of bedrooms, number of bathrooms, and utilities, which are some of the data. It's a regression problem where you can use linear regression to create the model. You can also take other advanced approaches if you want to.  

3. Wine Quality Prediction 

When taking machine learning portfolio projects for beginners, it's best to go with popular projects that include fixed and volatile acidity, alcohol, and density to predict the quality of red wine.  

You can treat this as a regression or classification problem. The quality variable you must predict inside the dataset ranges between 0 and 10. So you must build a regression model for prediction.  

On the other hand, you can take another project and build a regression model for prediction. You can also take another approach to break down the values into discrete intervals and then convert them into diverse variables.   

4. Heart Disease Prediction 

When looking for beginner machine learning projects, you can start with Heart Disease Predictions. It's a dataset that is used to predict the 10-year risk of CHD. 

The risk factors of heart disease in this dataset are the dependent variables. These things include heart disease, diabetes, smoking, high blood pressure, high cholesterol levels, etc.   

5. MNIST Digit Classification 

Machine learning enthusiasts who want to take on deep learning after finishing their course can try the MNIST dataset.  

This is a dataset with grayscale images of handwritten numbers from 0 to 9. If you complete this task, your task will be to identify the digit using a deep learning algorithm. This is a multi-class classification problem with ten different output classes. You can also use CNN to perform this classification.    

The MNIST dataset is prepared in Python inside the Keras library. You'll only have to install Kera's to get started with this.  

6. Stock Price Prediction Model 

If you were looking for real-world machine learning examples you can use for things like stock price prediction, try this one. You can predict stock prices based on historical data and other market indicators present in the course.  

It's a challenging area due to the volatility and unpredictability of financial markets in stocks if you want to start a stock market analysis. This machine learning project will also teach you how to predict investment directions.  

7. Fraud Detection 

The fraud detection course is a beginner machine learning course. There, you'll have to identify fraudulent activities in different domains. You'll find these activities in the insurance claims, online servies, and several different types of card transactions.  

8. Recommendation System  

Another worth mentioning beginner-friendly machine learning project is the idea recommendation system. Here you'll have to use recommendation systems.  

These are algorithms suggesting relevant items to different consumers/users (such as books, movies, and products). These are widely used in e-commerce and entertainment platforms.  

9. Fake News Detection 

Machine learning resume projects that really make you stand out during job hunts are like these ones. The fake news detection project is a Recommendation system.  

These are based on the preferences and past behaviors of the creators. These are widely used in e-commerce platforms and for entertainment as well.  

10. Write an ML algorithm from Scratch  

Many learners are interested in job-ready ML projects with code. If that's what you're looking for, start with this ML project.  

You can start coding a machine learning project where you'll also learn about different tools, along with a good understanding of translating mathematical instructions into working code.  

Conclusion 

Whether starting out or already finished your first year in the industry, these projects are among the best machine learning works you can start with. Also, most are beginner-friendly.  

So, you don't have to worry much about struggling with finishing these projects. These are also easy to self-assess. So, you'll have extra confidence in completing something substantial.  

[tccicomputercoaching]

Machine Learning Basics – A Beginner's Guide

Machine learning is one of the most thrilling branches in the world of tech at present, and it is concerned with teaching computers to perform self-decision making without being programmed into doing something. If you want to make a place for yourself in the artificial intelligence, robotics, or big data worlds, then you should make the learning of Machine Learning Basics availing for your career.

We have this course available for beginners in Machine Learning Basics at TCCI Computer Coaching Institute in Ahmedabad, covering machine learning fundamentals and concepts to give a great understanding of supervised and unsupervised learning algorithms, data preprocessing, and much more.

🔍 What You Will Learn:

Overview of machine learning types (supervised, unsupervised, and reinforcement learning)

Key machine learning algorithms (linear regression, decision trees, etc. )

Data preprocessing and feature selection

Model evaluation techniques and metrics

Introduction to Python libraries for machine learning (Scikit-learn, TensorFlow):

Our experienced faculty at TCCI will guide you through a couple of very simple yet interesting practicals that will take you through learning how machine learning is applied in real-world situations. When done, you will be strong enough to move on to very advanced machine learning techniques.

💻 On the Advantages of Learning Machine Learning-

Machine learning is changing such sectors as healthcare, finance, and e-commerce. Learning from this course will enable you to kick-start work with large data and smart systems that predict results, automate tasks, and more.

At TCCI, our focus is to provide you with personalized coaching and a wealth of experiential learning to develop competence in new emerging fields.

Location: Bopal & Iskon-Ambli Ahmedabad, Gujarat

Call now on +91 9825618292

Visit Our Website: http://tccicomputercoaching.com/

[monsoon-of-art]

[learnwithcadl123]

Learn Machine Learning In Zirakpur With CADL

Learn machine learning at CADL Zirakpur, where our extensive course will enable you to understand the fundamentals as well as more complex methods. With practical experience working on real-world projects and receiving mentoring from experts, you will be well-equipped to master this game-changing technology.

[dishachrista]

Getting Machine Learning Accessible to Everyone: Breaking the Complexity Barrier

Machine learning has become an essential part of our daily lives, influencing how we interact with technology and impacting various industries. But, what exactly is machine learning? In simple terms, it's a subset of artificial intelligence (AI) that focuses on teaching computers to learn from data and make decisions without explicit programming. Now, let's delve deeper into this fascinating realm, exploring its core components, advantages, and real-world applications.

Imagine teaching a computer to differentiate between fruits like apples and oranges. Instead of handing it a list of rules, you provide it with numerous pictures of these fruits. The computer then seeks patterns in these images - perhaps noticing that apples are round and come in red or green hues, while oranges are round and orange in colour. After encountering many examples, the computer grasps the ability to distinguish between apples and oranges on its own. So, when shown a new fruit picture, it can decide whether it's an apple or an orange based on its learning. This is the essence of machine learning: computers learn from data and apply that learning to make decisions.

Key Concepts in Machine Learning

Algorithms: At the heart of machine learning are algorithms, mathematical models crafted to process data and provide insights or predictions. These algorithms fall into categories like supervised learning, unsupervised learning, and reinforcement learning, each serving distinct purposes.

Supervised Learning: This type of algorithm learns from labelled data, where inputs are matched with corresponding outputs. It learns the mapping between inputs and desired outputs, enabling accurate predictions on unseen data.

Unsupervised Learning: In contrast, unsupervised learning involves unlabelled data. This algorithm uncovers hidden patterns or relationships within the data, often revealing insights that weren't initially apparent.

Reinforcement Learning: This algorithm focuses on training agents to make sequential decisions by receiving rewards or penalties from the environment. It excels in complex scenarios such as autonomous driving or gaming.

Training and Testing Data: Training a machine learning model requires a substantial amount of data, divided into training and testing sets. The training data teaches the model patterns, while the testing data evaluates its performance and accuracy.

Feature Extraction and Engineering: Machine learning relies on features, specific attributes of data, to make predictions. Feature extraction involves selecting relevant features, while feature engineering creates new features to enhance model performance.

Benefits of Machine Learning

Machine learning brings numerous benefits that contribute to its widespread adoption:

Automation and Efficiency: By automating repetitive tasks and decision-making processes, machine learning boosts efficiency, allowing resources to be allocated strategically.

Accurate Predictions and Insights: Machine learning models analyse vast data sets to uncover patterns and make predictions, empowering businesses with informed decision-making.

Adaptability and Scalability: Machine learning models improve with more data, providing better results over time. They can scale to handle large datasets and complex problems.

Personalization and Customization: Machine learning enables personalized user experiences by analysing preferences and behaviour, fostering customer satisfaction.

Real-World Applications of Machine Learning

Machine learning is transforming various industries, driving innovation:

Healthcare: Machine learning aids in medical image analysis, disease diagnosis, drug discovery, and personalized medicine. It enhances patient outcomes and streamlines healthcare processes.

Finance: In finance, machine learning enhances fraud detection, credit scoring, and risk analysis. It supports data-driven decisions and optimization.

Retail and E-commerce: Machine learning powers recommendations, demand forecasting, and customer behaviour analysis, optimizing sales and enhancing customer experiences.

Transportation: Machine learning contributes to traffic prediction, autonomous vehicles, and supply chain optimization, improving efficiency and safety.

Incorporating machine learning into industries has transformed them. If you're interested in integrating machine learning into your business or learning more, consider expert guidance or specialized training, like that offered by ACTE institute. As technology advances, machine learning will continue shaping our future in unimaginable ways. Get ready to embrace its potential and transformative capabilities.

[bitchesgetriches]

31% of employees are actively ‘sabotaging’ AI efforts. Here’s why

"In a new study, almost a third of respondents said they are refusing to use their company’s AI tools and apps. A few factors could be at play."

[river-taxbird]

AI hasn't improved in 18 months. It's likely that this is it. There is currently no evidence the capabilities of ChatGPT will ever improve. It's time for AI companies to put up or shut up.

I'm just re-iterating this excellent post from Ed Zitron, but it's not left my head since I read it and I want to share it. I'm also taking some talking points from Ed's other posts. So basically:

We keep hearing AI is going to get better and better, but these promises seem to be coming from a mix of companies engaging in wild speculation and lying.

Chatgpt, the industry leading large language model, has not materially improved in 18 months. For something that claims to be getting exponentially better, it sure is the same shit.

Hallucinations appear to be an inherent aspect of the technology. Since it's based on statistics and ai doesn't know anything, it can never know what is true. How could I possibly trust it to get any real work done if I can't rely on it's output? If I have to fact check everything it says I might as well do the work myself.

For "real" ai that does know what is true to exist, it would require us to discover new concepts in psychology, math, and computing, which open ai is not working on, and seemingly no other ai companies are either.

Open ai has already seemingly slurped up all the data from the open web already. Chatgpt 5 would take 5x more training data than chatgpt 4 to train. Where is this data coming from, exactly?

Since improvement appears to have ground to a halt, what if this is it? What if Chatgpt 4 is as good as LLMs can ever be? What use is it?

As Jim Covello, a leading semiconductor analyst at Goldman Sachs said (on page 10, and that's big finance so you know they only care about money): if tech companies are spending a trillion dollars to build up the infrastructure to support ai, what trillion dollar problem is it meant to solve? AI companies have a unique talent for burning venture capital and it's unclear if Open AI will be able to survive more than a few years unless everyone suddenly adopts it all at once. (Hey, didn't crypto and the metaverse also require spontaneous mass adoption to make sense?)

There is no problem that current ai is a solution to. Consumer tech is basically solved, normal people don't need more tech than a laptop and a smartphone. Big tech have run out of innovations, and they are desperately looking for the next thing to sell. It happened with the metaverse and it's happening again.

In summary:

Ai hasn't materially improved since the launch of Chatgpt4, which wasn't that big of an upgrade to 3.

There is currently no technological roadmap for ai to become better than it is. (As Jim Covello said on the Goldman Sachs report, the evolution of smartphones was openly planned years ahead of time.) The current problems are inherent to the current technology and nobody has indicated there is any way to solve them in the pipeline. We have likely reached the limits of what LLMs can do, and they still can't do much.

Don't believe AI companies when they say things are going to improve from where they are now before they provide evidence. It's time for the AI shills to put up, or shut up.

[blorbosinmyheadcentral]

This came to me in a vision

[suntails]

ride the carousel!

[maodun]

shout out to machine learning tech (and all the human-input adjustment contributors) that's brought about the present developmental stage of machine translation, making the current global village 地球村 moment on rednote小红书 accessible in a way that would not have been possible years ago.