#LearningML

[learnandgrowcommunity]

Session 9 : Aiming for Generalization | Overcoming Underfit and Overfit Models

Welcome to Session 9, where we unravel the intricacies of machine learning, focusing on the critical theme of generalization. Join us as we address the challenges posed by underfitting and overfitting models, providing actionable insights to enhance your machine learning endeavours.

Subscribe to "Learn And Grow Community" YouTube : https://www.youtube.com/@LearnAndGrowCommunity LinkedIn Group : https://linkedin.com/company/LearnAndGrowCommunity

[education30and40blog]

Inteligencia Artificial - Base necesaria para educación

loadYouTubePlayer('yt_video_gpvnPOt_TJI_gn53UHdB3xgWXqCz');

Debido al interés y al impacto que la Inteligencia Artificial está teniendo, veo NECESARIO este vídeo divulgativo para dar la base necesaria para educación, para ambos educadores y alumnado. Quedaros con las ideas claves del final del vídeo, por favor. 0:00 Introducción 0:30 Datos-Modelo-Resultado 1:18 Ejemplo analógico - Datos 2:22 Ejemplo analógico - Modelo 2:40 Ejemplo analógico - Resultado 3:56 Razones para usar LearninML 4:40 Tutorial LearningML - Datos 5:11 Tutorial LearningML - Modelo 5:18 Tutorial LearningML - Resultados 6:18 LearningML avanzado - Cambio de Modelo 7:10 LearningML - Guardar datos y modelo 7:29 Engañando la IA 9:02 Confusión de la IA 9:43 Ideas clave - Datos y sesgos 10:49 Ideas clave - Modelos. 11:10 Ideas clave - Resultados 11:36 Reflexión sobre IA generativa Infografía completa para descargar en: https://gmedranotic.wordpress.com/2023/01/08/inteligencia-artificial/ Link learningml: https://web.learningml.org/en/home/ Versión2: https://v2.learningml.org/

[luismiglesias]

Propuesta didáctica: Inteligencia artificial con LearningML. Modelo numérico. Botánicos en la escuela; clasificación de iris

Propuesta didáctica: Inteligencia artificial con LearningML. Modelo numérico. Botánicos en la escuela; clasificación de iris

En esta entrada comparto una nueva propuesta didáctica para introducir la Inteligencia Artificial (IA) en el aula. En ella planteo un escenario de aprendizaje automático basado en un modelo numérico implementado con la herramienta LearningML. Se trata de una propuesta con enfoque STEM, que desarrolla Competencias Específicas de las materias Matemáticas y Biología y en el trabajo por ámbitos,…

[myblogontech]

What is machine learning(ML) And Types of Machine Learning? || LN-TECHINFO

Visitor link : lntechinfo.in

What is machine learning?

"Machine Learning is a technique of computer programming in which a computer program is designed in such a way that the program can learn new things by itself and can take any decision by itself if needed." This technique of computer field is called machine learning.

Since machine learning only means learning machine or learning machine. In this, any application or software is developed in such a way that it can learn new things without interfering in its program and can predict or output the information related to that data when the time comes. is.

Definition of Machine Learning - Machine Learning, by definition, is an application that provides the ability to automate learning and improve itself without explicitly programming the system.

How does machine learning work?

A model is created by giving data to the Machine Learning (ML) Algorithm and when an input arrives in this algorithm, it provides the output according to the model created.

To understand this, you can take the example of YouTube, because if you search for things related to a topic or watch videos, then videos will appear on your YouTube homepage accordingly. In a way YouTube shows you the video on your homepage according to your preference.

Now it will not do any Employee of YouTube because it has crores of users and for this, how many Employee will be required. Machine learning is used to perform such a task that customizes your homepage and suggests videos according to your own Searches and Activity.

Read more blog :https://lntechinfo.blogspot.com

Types of Machine Learning?

1. Supervised Learning

This is the most common part of machine learning in which the output of the program is determined. It works completely on the guidance of the programmer like a teacher teaches a child. First a model of Algorithm is prepared in it and then a Dataset is created.

And from this Dataset, machine prediction or Decision takes. For example, we have made a program in which it is said that Mohan is 5 years old, Sohan is 10 years old, Ramesh is 15 years old. So if we ask this machine who is 15 years old, then the machine will immediately tell that it is 15 years old based on its dataset. Therefore, the output is accurate.

2. Unsupervised Learning

In this algorithm of machine learning, the Dataset is not fully labeled so that the output is not fully confirmed. This algorithm is used to extract hidden data from large dataset.

In Unsupervised Learning, the machine itself keeps searching for new patterns and relationships from the data. And it keeps making changes in its dataset. In this, very little information is given to learn the machine and it keeps learning a lot from the same data.

3. Reinforcement Learning

These algorithms are quite different and they are being used the most in today's advanced technology. These are Self Dependent Algorithm in a way which is capable of taking completely different types of decision itself. Such programs make many mistakes and keep improving their programs with their mistakes and experience.

Reinforcement Learning is quite complex, which can also modify the software created when needed. An example of this can be considered as Auto Driving Cars which always goes to the new area and always sees and understands different things.

Therefore its database also keeps changing. For example, if there is a machine based on machine learning, while fighting a board on the road while running, then it will give a feedback in its algorithm that the board has to be saved. Meaning the machine learns a whole new thing in it and applies it according to the Situation.

Our blog :https://lntechinfo.blogspot.com/2020/09/what-is-machine-learningml-and-types-of.html

How to Learn Machine Learning?

If you are a computer student or want to pursue a career in the field of computers, then you must have got a question about how machine learning can be learned and what is the career potential in it. Before learning this, it is very important to have basic knowledge of computer which you can learn by doing a small computer course.

Apart from taking a computer course, you can also do an online course by yourself or you can take help of YouTube and Google. After this, you will have to read Programming Language because from this you will create the program or software of Machine Learning.

You can start Programming Language with C Language because it is easier than other language and is also very helpful in making machine learning programs. And then after knowing the basics of these Programming Language, you should take an online or offline course.

In this, Google also conducts free course of machine learning in which you can learn online machine learning in free. After this, you can also do a Paid Course so that you can know about it deeply. Click here to enroll Machine Learning Course in Google - Google Machine Learning Course

Is there a lot of scope in machine learning?

Machine Learning - It is anticipated that there will be a time of machine learning and artificial intelligence in the world of computers and the Internet. Almost every type of devices will have its application so that the user gets better service. In the field of Computer Science, a lot of special attention is being given to machine learning because the demand for this technology is gradually increasing.

Big tech companies like Apple, Google, Microsoft are working on this at a very large level in which the name of Automatic Car of Google comes first. Apart from this, Tesla and Apple are also running their projects on such vehicles. There is a lot less expert in this field than its demand and the applications of machine learning are constantly increasing.

Where machine learning is being used?

Large companies like Google and Apple are using it on a large scale so that they can easily understand their huge user-base. You must have known that these companies have crores of users and it is difficult to give service to every user according to their choice, because it requires very large staff.

Using Machine Learning Algorithm, the big data can be easily solved and important information related to them can be derived. Google search engine also uses ML Algorithm because Google has millions of website indexes and which website is important for the user and which is not, it is difficult to check manually.

Google's machine learning is very advanced, which can find thousands of websites in less than a second and find the right website. Apart from this, e-commerce companies also use machine learning so that they can show and sell products to their customers according to their preference.

Contact us :https://lntechinfo.in/contacts/

[eurekakinginc]

"[D] Any class recommendation for a student?"- Detail: Hi, I am currently a master student at GaTech. I was a game developer after I graduate from Pitt in Computer engineering. I am completely new to this realm. So, could you guys recommend some classes? There are just too many of them. And I only have eight classes left to finish.Here is the course list. Classes that are in bold text are what I selected.​Algorithm(Pick one or more):Computability, Algorithms, and ComplexityComputational Complexity TheoryDesign and Analysis of AlgorithmsGraph AlgorithmsApproximation AlgorithmsRandomized AlgorithmsComputational Science and Engineering AlgorithmsCore(Pick one or more):Machine LearningComputational Data Analysis: Learning, Mining, and ComputationElective(Pick three or more):Big Data Systems & AnalysisComputer Vision Markov Chain Monte CarloSpectral AlgorithmsMachine Learning TheoryPattern RecognitionBehavioral Imaging Deep Learning  Machine Learning for TradingNatural LanguageWeb Search and Text MiningData and Visual AnalyticsComputational StatisticsBayesian MethodsStochastic OptimizationSpecial Topics(Pick Less than four):Interactive Robot LearningML with Limited SupervisionMath Foundations Mach Learning. Caption by inexistentme. Posted By: www.eurekaking.com

[luismiglesias]

Propuesta didáctica: Inteligencia artificial con LearningML. Modelo numérico. Matemáticas; puntos, coordenadas y cuadrantes

Propuesta didáctica: Inteligencia artificial con LearningML. Modelo numérico. Matemáticas; puntos, coordenadas y cuadrantes

En esta entrada comparto una propuesta didáctica para introducir la Inteligencia Artificial (IA) en el aula de Matemáticas. En ella planteo un escenario de aprendizaje automático basado en un modelo numérico implementado con la herramienta LearningML. Machine Learning is the subfield of computer science that gives ‘computers the ability to learn without being explicitly programmed’. Arthur…

[eurekakinginc]

Posted by Ajaygawde via /r/artificial. Join Discussion: https://ift.tt/2NkskN5. Curated by: www.eurekaking.com