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can you tell me about how can we teach technology in english

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Joan Haider Rizvi: (Reflection on Machine Learning)

Machine learning is significant for human beings it has gradually reduces the human effort, it has also reduce the cost and moreover it has been very significant in performing task in a fraction of seconds. On the other hand it has an in built system for rectifying all of those errors that has been done in the past and has a well developed system for self correction and not to repeat those mistake.

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Machine learning is very beneficial as it helps in learning as well as provides entertainment.

The positive impact of machine learning is that it solves health issues by providing smart watches and fitness trackers

The negative impact of machine learning is it makes you sedentary.

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Human vs. Machine (Artificial Intelligence) - Unveiling the Uniqueness #artificialintelligence

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It's all about machine learning.

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The class was much informative 👍🌎

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Machine learning is a type of Artificial intelligence. It is used to improve the accuracy and resolve the problems and human errors.

It helps in new drug discovery, efficient and accurate disease diagnosis and prevention of disease.

It helps teacher in teaching new patterns and techniques according to the need of each and every student. It helps teacher to add fun and game based learning and activities in which students become more involved.

It helps in environmental monitoring of climatic changes and helps to improve the sustainability of the climate.

As there are so many benefits of machine learning, there are so many bad effects of machine learning. Some of them are it invades the privacy, it is emotionless, it could create ethical issues.

S.Amina Reyaz

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Hi,

We are learning technology

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Machine Learning for Everybody – Full Course

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create a picture of a beach with palm trees

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View points of Machine learning.

Machine learning can help in education, health system, business development.

Machine learning is use across the world to ease daily life work.for example

Financial accuracy

Healthcare systems

Social media uses

Product making

Teaching and etc

Impact on daily life.

Machine learning can ease our work in systemetic ways .it's very impactful in daily life .here will share very common impact on machine learning are

Self driving cares and planes

Detecting fraud in transaction

Netflix

Search engine for example Google.

By farhat Azeem

How harmful machine learning is:

We can discuss various harm about machine learning maybe people can use it in a wrong ways.poor data quality

Security risk and more .

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Saima Wali

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Mehreen

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What Is AI? | Artificial Intelligence | What is Artificial Intelligence? | AI In 5 Mins |Simplilearn

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This is a song about social performance, about what happens when we construct so many versions of ourselves to please others that we lose sight of the 'real' us. Its like we are constantly programming our behaviours to maximise our social output and make others happy.

By: Maliha

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What is machine learning?

Machine learning (ML) is a type of artificial intelligence ( AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.

Recommendation engines are a common use case for machine learning. Other popular uses include fraud detection, spam filtering, malware threat detection, business process automation (BPA) and Predictive maintenance.

Why is machine learning important?

Machine learning is important because it gives enterprises a view of trends in customer behavior and business operational patterns, as well as supports the development of new products. Many of today's leading companies, such as Facebook, Google and Uber, make machine learning a central part of their operations. Machine learning has become a significant competitive differentiator for many companies.

What are the different types of machine learning?

Classical machine learning is often categorized by how an algorithm learns to become more accurate in its predictions. There are four basic approaches: supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. The type of algorithm data scientists choose to use depends on what type of data they want to predict.

Supervised learning: In this type of machine learning, data scientists supply algorithms with labeled training data and define the variables they want the algorithm to assess for correlations. Both the input and the output of the algorithm is specified.

Unsupervised learning: This type of machine learning involves algorithms that train on unlabeled data. The algorithm scans through data sets looking for any meaningful connection. The data that algorithms train on as well as the predictions or recommendations they output are predetermined.

Semi-supervised learning: This approach to machine learning involves a mix of the two preceding types. Data scientists may feed an algorithm mostly labeled training data, but the model is free to explore the data on its own and develop its own understanding of the data set.

Reinforcement learning: Data scientists typically use reinforcement learning to teach a machine to complete a multi-step process for which there are clearly defined rules. Data scientists program an algorithm to complete a task and give it positive or negative cues as it works out how to complete a task. But for the most part, the algorithm decides on its own what steps to take along the way.

How does supervised machine learning work?

Supervised machine learning requires the data scientist to train the algorithm with both labeled inputs and desired outputs. Supervised learning algorithms are good for the following tasks:

Binary classification: Dividing data into two categories.

Multi-class classification: Choosing between more than two types of answers.

Regression modeling: Predicting continuous values.

Ensembling: Combining the predictions of multiple machine learning models to produce an accurate prediction.

How does unsupervised machine learning work?

Unsupervised machine learning algorithms do not require data to be labeled. They sift through unlabeled data to look for patterns that can be used to group data points into subsets. Most types of deep learning, including neural networks, are unsupervised algorithms. Unsupervised learning algorithms are good for the following tasks:

Clustering: Splitting the dataset into groups based on similarity.

Anomaly detection: Identifying unusual data points in a data set.

Association mining: Identifying sets of items in a data set that frequently occur together.

Dimensionality reduction: Reducing the number of variables in a data set.

How does semi-supervised learning work?

Semi-supervised learning works by data scientists feeding a small amount of labeled training data to an algorithm. From this, the algorithm learns the dimensions of the data set, which it can then apply to new, unlabeled data. The performance of algorithms typically improves when they train on labeled data sets. But labeling data can be time consuming and expensive. Semi-supervised learning strikes a middle ground between the performance of supervised learning and the efficiency of unsupervised learning. Some areas where semi-supervised learning is used include:

Machine translation: Teaching algorithms to translate language based on less than a full dictionary of words.

Fraud detection: Identifying cases of fraud when you only have a few positive examples.

Labelling data: Algorithms trained on small data sets can learn to apply data labels to larger sets automatically.

How does reinforcement learning work?

Reinforcement learning works by programming an algorithm with a distinct goal and a prescribed set of rules for accomplishing that goal. Data scientists also program the algorithm to seek positive rewards -- which it receives when it performs an action that is beneficial toward the ultimate goal -- and avoid punishments -- which it receives when it performs an action that gets it farther away from its ultimate goal. Reinforcement learning is often used in areas such as:

Robotics: Robots can learn to perform tasks the physical world using this technique.

Video gameplay: Reinforcement learning has been used to teach bots to play a number of video games.

Resource management: Given finite resources and a defined goal, reinforcement learning can help enterprises plan out how to allocate resources.

Machine learning is like statistics on steroids.

Who's using machine learning and what's it used for?

Today, machine learning is used in a wide range of applications. Perhaps one of the most well-known examples of machine learning in action is the recommendation engine that powers Facebook's news feed.

Facebook uses machine learning to personalize how each member's feed is delivered. If a member frequently stops to read a particular group's posts, the recommendation engine will start to show more of that group's activity earlier in the feed.

Behind the scenes, the engine is attempting to reinforce known patterns in the member's online behavior. Should the member change patterns and fail to read posts from that group in the coming weeks, the news feed will adjust accordingly.

In addition to recommendation engines, other uses for machine learning include the following:

Customer relationship management. CRM software can use machine learning models to analyze email and prompt sales team members to respond to the most important messages first. More advanced systems can even recommend potentially effective responses.

Business intelligence. BI and analytics vendors use machine learning in their software to identify potentially important data points, patterns of data points and anomalies.

Human resource information systems. HRIS systems can use machine learning models to filter through applications and identify the best candidates for an open position.

Self-driving cars. Machine learning algorithms can even make it possible for a semi-autonomous car to recognize a partially visible object and alert the driver.

Virtual assistants. Smart assistants typically combine supervised and unsupervised machine learning models to interpret natural speech and supply context.

What are the advantages and disadvantages of machine learning?

Machine learning has seen use cases ranging from predicting customer behavior to forming the operating system for self-driving cars.

When it comes to advantages, machine learning can help enterprises understand their customers at a deeper level. By collecting customer data and correlating it with behaviors over time, machine learning algorithms can learn associations and help teams tailor product development and marketing initiatives to customer demand.

Some companies use machine learning as a primary driver in their business models. Uber, for example, uses algorithms to match drivers with riders. Google uses machine learning to surface the ride advertisements in searches.

But machine learning comes with disadvantages. First and foremost, it can be expensive. Machine learning projects are typically driven by data scientists, who command high salaries. These projects also require software infrastructure that can be expensive.

There is also the problem of machine learning bias. Algorithms trained on data sets that exclude certain populations or contain errors can lead to inaccurate models of the world that, at best, fail and, at worst, are discriminatory. When an enterprise bases core business processes on biased models it can run into regulatory and reputational harm.

How to choose the right machine learning model

The process of choosing the right machine learning model to solve a problem can be time consuming if not approached strategically.

Step 1: Align the problem with potential data inputs that should be considered for the solution. This step requires help from data scientists and experts who have a deep understanding of the problem.

Step 2: Collect data, format it and label the data if necessary. This step is typically led by data scientists, with help from data wranglers.

Step 3: Chose which algorithm(s) to use and test to see how well they perform. This step is usually carried out by data scientists.

Step 4: Continue to fine tune outputs until they reach an acceptable level of accuracy. This step is usually carried out by data scientists with feedback from experts who have a deep understanding of the problem.

Importance of human interpretable machine learning

Explaining how a specific ML model works can be challenging when the model is complex. There are some vertical industries where data scientists have to use simple machine learning models because it's important for the business to explain how every decision was made. This is especially true in industries with heavy compliance burdens such as banking and insurance.

Complex models can produce accurate predictions, but explaining to a lay person how an output was determined can be difficult.

What is the future of machine learning?

While machine learning algorithms have been around for decades, they've attained new popularity as artificial intelligence has grown in prominence. Deep learning models, in particular, power today's most advanced AI applications.

Machine learning platforms are among enterprise technology's most competitive realms, with most major vendors, including Amazon, Google, Microsoft, IBM and others, racing to sign customers up for platform services that cover the spectrum of machine learning activities, including data collection, data preparation, data classification, model building, training and application deployment.

As machine learning continues to increase in importance to business operations and AI becomes more practical in enterprise settings, the machine learning platform wars will only intensify.

Continued research into deep learning and AI is increasingly focused on developing more general applications. Today's AI models require extensive training in order to produce an algorithm that is highly optimized to perform one task. But some researchers are exploring ways to make models more flexible and are seeking techniques that allow a machine to apply context learned from one task to future, different tasks.

Deep learning works in very different ways than traditional machine learning.

How has machine learning evolved?

1642 - Blaise Pascal invents a mechanical machine that can add, subtract, multiply and divide.

1679 - Gottfried Wilhelm Leibniz devises the system of binary code.

1834 - Charles Babbage conceives the idea for a general all-purpose device that could be programmed with punched cards.

1842 - Ada Lovelace describes a sequence of operations for solving mathematical problems using Charles Babbage's theoretical punch-card machine and becomes the first programmer.

1847 - George Boole creates Boolean logic, a form of algebra in which all values can be reduced to the binary values of true or false.

1936 - English logician and cryptanalyst Alan Turing proposes a universal machine that could decipher and execute a set of instructions. His published proof is considered the basis of computer science.

1952 - Arthur Samuel creates a program to help an IBM computer get better at checkers the more it plays.

1959 - MADALINE becomes the first artificial neural network applied to a real-world problem: removing echoes from phone lines.

1985 - Terry Sejnowski's and Charles Rosenberg's artificial neural network taught itself how to correctly pronounce 20,000 words in one week.

1997 - IBM's Deep Blue beat chess grandmaster Garry Kasparov.

1999 - A CAD prototype intelligent workstation reviewed 22,000 mammograms and detected cancer 52% more accurately than radiologists did.

2006 - Computer scientist Geoffrey Hinton invents the term deep learning to describe neural net research.

2012 - An unsupervised neural network created by Google learned to recognize cats in YouTube videos with 74.8% accuracy.

2014 - A chatbot passes the Turing Test by convincing 33% of human judges that it was a Ukrainian teen named Eugene Goostman.

2014 - Google's AlphaGo defeats the human champion in Go, the most difficult board game in the world.

2016 - LipNet, DeepMind's artificial intelligence system, identifies lip-read words in video with an accuracy of 93.4%.

2019 - Amazon controls 70% of the market share for virtual assistants in the U.S.

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What Is Machine Learning and Why Is It Important?

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Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.

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Actually I have connectivity issues perhaps???

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Hello maam.... What to write

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Asma Gilani

Example:

AI’s impact on healthcare is offering revolutionary changes in diagnosis, treatment, and patient care. Machine learning models can predict health outcomes, assist in early detection of diseases, and personalize treatment plans. AI-driven robots assist in surgeries,and reducing recovery times.

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Machine learning stem out of AI. Which has remarkable ability of decision making based on the saved data. The positive side of ML is that it save man power ,time and eventually money. For example if a school install Alexa & feed information related to school the school can minimized cost & work load of FDO.

The negative aspect of this is that it lack humanly warmth & emotional attachment

.

Maria Raheel

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Asma Gilani

Drawbacks: AI tools are a threat to Teacher's Job Security.

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View points of Machine learning.

Machine learning can help in education, health system, business development.

Machine learning is use across the world to ease daily life work.for example

Financial accuracy

Healthcare systems

Social media uses

Product making

Teaching and etc

Impact on daily life.

Machine learning can ease our work in systemetic ways .it's very impactful in daily life .here will share very common impact on machine learning are

Self driving cares and planes

Detecting fraud in transaction

Netflix

Search engine for example Google.

By farhat Azeem

How harmful machine learning is:

We can discuss various harm about machine learning maybe people can use it in a wrong ways.poor data quality

Security risk and more .

# data[].attributes.body
Asma Gilani

Siri and Alexa, powered by AI, have transformed how we interact with our devices, enabling hands-free control and providing instant information.

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Machine learning by Maliha A. M.

Machine learning is a type of Artificial Intelligence that allows computers to learn and improve their performance on a task. It can be supervised, unsupervised or reinforcement learning. Its like teaching a child to recognise different car types: we show them on the road or by pictures, and they will learn to identify the different cars due to their common and differing characteristics

This has transformed various aspects of society from healthcare, finance, transportation, education etc.

Drawback: Bias is a common drawback when training a machine learning model. This can lead to unfair outcomes. For example, a facial recognition system based on mostly white faces will struggle with brown or darker skin toned individuals.

Advantage: Repetitive tasks can be automated with machine learning and this frees humans to handle more complex tasks. For example, chatbots can handle the simpler customer inquiries while human support agents can focus on more complex problems.

Examples of Machine learning in our society:

1) Image recognition in our Google Photos uses facial recognition feature to identify and group photos of individuals together. I have groups for all members of my family.

2) Climate pattern prediction models. Especially nowadays in Karachi such predictive models with machine learning are being used to analyse large datasets to predict when and where it will rain and to what extent.

3) Risk management. Machine learning helps investors make informed decisions by identifying potential risks and opportunities. We used to do SWOT Analysis by hand or through research and that is something now automated.

4) Foodpanda app usage in Pakistan. This app uses machine learning to recommend restaurants based on preferences and order history.

5) Daraz app uses machine learning for personalised product recommendations and demand forecasting.

6) Easypaisa uses machine learning for fraud detection and personalised financial services.

7) Zameen.com and Pakwheels

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This introduction to machine learning provides an overview of its history, important definitions, applications and concerns within businesses today.

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Even though there are numerous uses of Machine learning in our daily lives, but I will specifically choose the use of Machine Learning in regard to advancement in the transportation system in Pakistan. With the advent of ride hailing services in Pakistan, going from one place to another, even from one city to another city has become very easy. Previously, those people without the cars had to wait for a long time or had to cover long distances waiting for bus, cabs or rickshaws. Not only the long waits, there used to be a great deal of haggling involved as there was no fixed price process back in those days.

Now, the presence of self hailing cabs has made it very easy to go the distance, and also thanks to the machine learning technology, the cars or other vehicles get assigned to the nearest driver available, plus the machine estimation of the fare has made it convenient to pay the reasonable amount for your ride.

There is a drawback to these ride hailing services, people have become more lazy as they dont have to walk from one place to another looking for the transportation.

Plus in the event of an absence of internet and mobile availability, it becomes impossible to get a ride.

Mehreen.

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Machine learning uses data or algorithm to perform specific tasks. It has made our lives far easier, from booking a doctor's appoinent from the comfort of our home to going cashless without any fear of being robbed. Yet, it sometimes can be a threat to our privacy as it can be used to hack our personal data.

By Saima Wali

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Machine learning has revolutionized society by enhancing efficiency in various sectors, from healthcare to finance, through predictive analytics and automation. Advantages include improved decision-making and personalized services. However, it also poses risks such as job displacement, privacy concerns, and biased algorithms. Balancing innovation with ethical considerations is crucial for maximum benefits while considering drawbacks. Privacy issues arise from the extensive data collection required for training algorithms, and biases in machine learning models can perpetuate and even exacerbate existing inequalities. Ensuring data security, addressing algorithmic biases, and preparing for workforce shifts are essential to harness the benefits of machine learning while minimizing its negative impacts.

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AYESHA

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MACHINE LEARNING