Video yükleniyor...

Video Yüklenemedi

Bu video yüklenirken bir sorun oluştu. Bu geçici bir ağ sorunundan kaynaklanıyor olabilir veya video kullanılamıyor olabilir.

Ana Sayfaya Dön

Oracle offering free certificate training in A.I and Machine learning

𝗘𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝗣𝗹𝘂𝗴𝘀🔌🔌

67,303 subscribers

57,585 görüntüleme • 1 yıl önce •via X (Twitter)

Bilim & Teknoloji Haberler & Politika Eğitim

Anya Rossi• Live Now

Private livecam show

9 Yorum

Baba Voss Tich profil fotoğrafı

Baba Voss Tich1 yıl önce

Since nobody wants to Google these days....

𝗘𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝗣𝗹𝘂𝗴𝘀🔌🔌 profil fotoğrafı

𝗘𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝗣𝗹𝘂𝗴𝘀🔌🔌1 yıl önce

🤣🤣🤣

Kamohelo profil fotoğrafı

Kamohelo1 yıl önce

Where's the link?

Sarcastic_Badass😎 profil fotoğrafı

Sarcastic_Badass😎1 yıl önce

Yeah it's free but to collect that certificate you will have to pay up

Zozo🇿🇦 profil fotoğrafı

Zozo🇿🇦1 yıl önce

@TJMUNGONI

Uncle Knights profil fotoğrafı

Uncle Knights1 yıl önce

Thank you for this, im already flying through the course

LambChip profil fotoğrafı

LambChip1 yıl önce

We hear you, my sista. Thank you

Zamo Mbuyisa profil fotoğrafı

Zamo Mbuyisa1 yıl önce

Link please

Sarcastic_Badass😎 profil fotoğrafı

Sarcastic_Badass😎1 yıl önce

They love plugging people with stuff they haven't tried before showing people

Benzer Videolar

New Graduate Certificate and Masters Degree in Machine Learning and Artificial Intelligence for Engineering at University of Washington!!! 🧠💪🧠

New Graduate Certificate and Masters Degree in Machine Learning and Artificial Intelligence for Engineering at University of Washington!!! 🧠💪🧠

Steven Brunton

12,846 görüntüleme • 1 yıl önce

AWS offering 100% free certification vouchers for Foundational and Associate certifications. These vouchers cover the following certifications: Cloud Practitioner, AI Practitioner, Solutions Architect, SysOps Administrator, Developer, Data Engineer, and Machine Learning Engineer.

AWS offering 100% free certification vouchers for Foundational and Associate certifications. These vouchers cover the following certifications: Cloud Practitioner, AI Practitioner, Solutions Architect, SysOps Administrator, Developer, Data Engineer, and Machine Learning Engineer.

Hawa

189,721 görüntüleme • 1 yıl önce

Lean Applied Machine Learning for FREE by Cornell University - 80 Lectures - Lecture Notes - Self-paced Learning - 100% FREE Course and Notes link 🧵👇

Lean Applied Machine Learning for FREE by Cornell University - 80 Lectures - Lecture Notes - Self-paced Learning - 100% FREE Course and Notes link 🧵👇

Python Space

121,264 görüntüleme • 3 yıl önce

Free Machine Learning with Python Course from IBM Topic covered: - Supervised Learning - Unsupervised Learning - Dimensionality Reduction & Collaborative Filtering 100% FREE Course link 🧵👇

Free Machine Learning with Python Course from IBM Topic covered: - Supervised Learning - Unsupervised Learning - Dimensionality Reduction & Collaborative Filtering 100% FREE Course link 🧵👇

Afiz ⚡️

59,880 görüntüleme • 3 yıl önce

Trump: Oracle will be building a $14 billion worth of cloud computing and A.I. infrastructure in the kingdom. Why isn't he doing that here, David?

Trump: Oracle will be building a $14 billion worth of cloud computing and A.I. infrastructure in the kingdom. Why isn't he doing that here, David?

Acyn

21,957 görüntüleme • 3 ay önce

Jim Simons explaining Machine Learning in Finance:

Jim Simons explaining Machine Learning in Finance:

ₕₐₘₚₜₒₙ

29,476 görüntüleme • 5 ay önce

“Each Factory And Each Machine Is For Multiple Industries” Kevin Czinger Discusses Revolutionary A.I. Development

“Each Factory And Each Machine Is For Multiple Industries” Kevin Czinger Discusses Revolutionary A.I. Development

Bannon’s WarRoom

19,696 görüntüleme • 1 yıl önce

KL divergence is a fundamental concept used in machine learning, from optimization a neural net to RL training of LLMs. but here is what it actually means:

KL divergence is a fundamental concept used in machine learning, from optimization a neural net to RL training of LLMs. but here is what it actually means:

ℏεsam

20,381 görüntüleme • 6 ay önce

2. Sololearn / Sololearn → Sololearn provides AI-powered web design and app development training, as well as free data analysis tools. In addition, you may earn credentials while learning.

2. Sololearn / Sololearn → Sololearn provides AI-powered web design and app development training, as well as free data analysis tools. In addition, you may earn credentials while learning.

Mujeeb Ahmed

27,461 görüntüleme • 1 yıl önce

As AI advances towards mainstream adoption, security and privacy in machine learning models must be prioritized Our founder Dawn Song shares why security in machine learning systems is crucial as we begin to use AI for everyday purposes

As AI advances towards mainstream adoption, security and privacy in machine learning models must be prioritized Our founder Dawn Song shares why security in machine learning systems is crucial as we begin to use AI for everyday purposes

Oasis Labs

20,270 görüntüleme • 3 yıl önce

Machine Learning (ML) explained like you are 5 In 30 seconds

Machine Learning (ML) explained like you are 5 In 30 seconds

Sharav Arora

46,509 görüntüleme • 9 gün önce

Artificial Intelligence and machine learning, data scientists and analysts top list of future jobs in demand by 2025. Branji #HigherEducationExpoAndCareerFair

Artificial Intelligence and machine learning, data scientists and analysts top list of future jobs in demand by 2025. Branji #HigherEducationExpoAndCareerFair

NTV Kenya

76,906 görüntüleme • 2 yıl önce

Dave Allen offering advice and training Johnny Fisher earlier today. They fought each other in May 🤝

Dave Allen offering advice and training Johnny Fisher earlier today. They fought each other in May 🤝

Boxing Kingdom

89,907 görüntüleme • 1 yıl önce

Still after all these years I’m experimenting with pattens. Many of which are the result of my constant training with A.I. to better understand what I am capable of. Machine Inspired phrases # 10023

Still after all these years I’m experimenting with pattens. Many of which are the result of my constant training with A.I. to better understand what I am capable of. Machine Inspired phrases # 10023

Harry Yeff

11,101 görüntüleme • 3 yıl önce

.const believes Bittensor will outpace centralized machine learning labs, becoming the go-to hub for efficient problem-solving in data collection, benchmarks, and AI training. As competitors emerge, $TAO aims to remain the Schelling point for digital commodity systems.

.const believes Bittensor will outpace centralized machine learning labs, becoming the go-to hub for efficient problem-solving in data collection, benchmarks, and AI training. As competitors emerge, $TAO aims to remain the Schelling point for digital commodity systems.

Grayscale

102,100 görüntüleme • 1 yıl önce

A unique first of its kind CM Skills Development prog. 4000 students today started training in market demand driven courses like Graphic Design (UI- UX Design),Data Analytics ,Cyber Security,Game Development, AI and Machine Learning & Cloud Computing. #PioneeringITSolutions

A unique first of its kind CM Skills Development prog. 4000 students today started training in market demand driven courses like Graphic Design (UI- UX Design),Data Analytics ,Cyber Security,Game Development, AI and Machine Learning & Cloud Computing. #PioneeringITSolutions

Maryam Nawaz Sharif

85,758 görüntüleme • 2 yıl önce

Building a machine learning model isn’t just about finding the right algorithm—it’s about the right process. In Machine Learning in Production, Andrew Ng breaks down the iterative loop of model development: training, error analysis, refining hyperparameters, and improving data. Getting to a high test set accuracy is one thing, but aligning your model with real-world business metrics? That’s where the real challenge begins. Learn how to bridge the gap between models and impact:

Building a machine learning model isn’t just about finding the right algorithm—it’s about the right process. In Machine Learning in Production, Andrew Ng breaks down the iterative loop of model development: training, error analysis, refining hyperparameters, and improving data. Getting to a high test set accuracy is one thing, but aligning your model with real-world business metrics? That’s where the real challenge begins. Learn how to bridge the gap between models and impact:

DeepLearning.AI

45,248 görüntüleme • 1 yıl önce

Meet the AI agents that search, reason, and sift through all your content to get you answers grounded in your team’s shared context. Hear more from Dropbox machine learning engineer Jongmin Baek—along with machine learning engineer Marta Mendez—on Working Smarter.

Meet the AI agents that search, reason, and sift through all your content to get you answers grounded in your team’s shared context. Hear more from Dropbox machine learning engineer Jongmin Baek—along with machine learning engineer Marta Mendez—on Working Smarter.

Dropbox

20,018 görüntüleme • 18 gün önce

if you're struggling on where to start learning ML, here’s a playlist of 30 youtube videos to learn machine learning fundamentals from scratch "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):

if you're struggling on where to start learning ML, here’s a playlist of 30 youtube videos to learn machine learning fundamentals from scratch "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):

ℏεsam

108,861 görüntüleme • 1 yıl önce

a playlist of 30 youtube videos to learn machine learning fundamentals from scratch if you're struggling on where to start learning ML, this list goes this "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):

a playlist of 30 youtube videos to learn machine learning fundamentals from scratch if you're struggling on where to start learning ML, this list goes this "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):

ℏεsam

117,570 görüntüleme • 1 yıl önce