#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.

[techinsightshq]

What is AI ? A Comprehensive Introduction

What is AI ?

Artificial intelligence, or AI, is the emulation of human intellect in computers that are trained to carry out activities that typically require human intelligence, such as speech recognition, visual perception, decision-making, and language translation.

Machine learning, natural language processing, computer vision, robotics—all fall under the umbrella of artificial intelligence (AI).

Natural language processing involves teaching computers how to read and interpret human language, whereas machine learning focuses on teaching computers how to learn from data and make predictions or choices based on that data. Robotics is the process of building machines that can execute physical activities and interact with the physical world, whereas computer vision enables machines to recognize and analyze visual input.

Numerous industries, including healthcare, banking, transportation, manufacturing, and education, among others, have adopted AI in various ways. As AI technology develops and advances, it has the potential to drastically alter a variety of facets of our life, including how we work, communicate, study, and access healthcare.

Brief history of AI and its evolution

The idea of building intelligent machines was first explored by scientists and thinkers in the early 20th century. Here is a synopsis of artificial intelligence's development over time:

Early developments

Dartmouth College, usually regarded as the origin of AI, hosted a symposium in 1956 that was organized by a group of researchers. The researchers put out the notion of building machines that are capable of learning, reasoning, and problem-solving.

The AI Winter

 In the 1970s and 1980s, research funding for artificial intelligence (AI) declined, which resulted in the slow progress in AI that is now referred to as the "AI winter." Due to its poor performance and lack of funding, AI research fell out of favor at this time.

Expert systems

This became the primary focus of AI research in the 1980s. These systems formed judgements and adhered to rules that were knowledge-based in order to deal with certain problems.

Neural networks

In the 1990s, neural networks, a breakthrough AI technique, initially emerged. Neural networks can learn and adapt to changing environments since they are based on the human brain.

Machine learning and big data

In the early 2000s, a resurgence in interest in AI was sparked by the development of machine learning algorithms and the accessibility of massive amounts of data. New technologies, such deep learning, were developed during this time, allowing computers to learn and carry out complicated tasks like speech and picture recognition.

Developments since then

AI is now being incorporated more and more into our daily lives. AI-powered personal assistants like Siri and Alexa are gaining popularity, while self-driving cars are being tested on public roads. AI is also being utilized to enhance productivity and decision-making in the financial, healthcare, and other sectors.

Finally, since its inception in the 1950s, AI has undergone a great amount of development. While gains in AI were modest during the "AI winter," recent developments in neural networks and machine learning have sparked a resurgence in enthusiasm. The potential for AI to change many industries is enormous, and it is currently becoming more and more interwoven into our daily lives.

Learn More

[destiel-news-network]

(Source)

[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.

[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.

[cordrawroy]

got distracted by imagining bocchi undergoing feature space transformations during lecture