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... 1:1:10
Machine Learning Fundamentals in 30 Minutes | AI and Machine Learning Tutorial | Simplilearn

Simplilearn

4 days ago 4K views

This video outlines the fundamental concepts of machine learning, including its definition as a branch of AI that enables computers to learn from data and make predictions without explicit programming. It highlights the importance of understanding data inputs and outputs for effective model training and discusses various machine learning types: supervised, unsupervised, and reinforcement learning, with examples of their applications like recommendation systems and self-driving cars. The session emphasizes the necessity of having practical experience and knowledge in AI tools and technologies to successfully transition into a career in this field.
17:43 - 18:10

Unsupervised learning discovers hidden structures within unlabeled data for insights.

Simplilearn Machine Learning Education
... 7:47
AI VS ML Engineer What Do They Do?

Exaltitude

5 days ago 2K views

AI focuses on creating machines that can replicate human behavior and learning. It encompasses various applications such as virtual assistants, recommendation systems, and more. Machine learning, a subset of AI, emphasizes teaching machines to learn from data. The differences between AI engineers and ML engineers lie in their approaches—AI engineers prioritize product delivery while ML engineers concentrate on model training and deployment. The demand for AI engineers is rising rapidly, with many companies seeking talent to develop practical AI applications, reflecting a significant shift in job market dynamics geared towards innovative AI solutions.
03:58 - 04:23

AI Engineers leverage existing tools for rapid deployment without model training.

Datacamp Machine Learning Education
... 53:32
Simplifying Mathematics Behind AI

Data Science Dojo

5 days ago 254 views

AI fundamentally relies on pattern recognition, extracting relationships from raw data to guide predictions. Math is crucial for training models and understanding AI behaviors, with linear algebra aiding representation and calculus optimizing equations. Concepts like regression reveal significant patterns, facilitating decision-making based on data insights. For instance, handling properties' size vs price illustrates turning patterns into equations for predicting future values. This journey into the mathematical underpinnings of AI enhances comprehension and application across varying scenarios in machine learning.
04:50 - 05:05

Pattern representation through linear regression predicts outcomes based on data.

Machine Learning Education
... 38:56
Ctrl + Alt + AI - Ep1- Open Source AI Models - with Pritish Yuvraj (Sr ML Engineer, Meta)

Aishwarya Srinivasan

14 days ago 2K views

AI has become transformative across various fields, emphasizing the importance of accessibility and creativity through technology. By examining the evolution of personal computing, the democratization of tools, and the significance of open-source initiatives, it becomes clear that machine learning presents both opportunities and challenges. The video features insights into transitioning from software engineering to machine learning, the critical role of large language models, and recommendations for leveraging AI beneficially. As advancements continue, the focus should remain on personalizing AI to serve individuals effectively while maintaining ethical considerations and addressing potential bottlenecks.
10:01 - 10:13

Current learning curves in AI are facilitated by improved accessibility and tools.

Meta Machine Learning Education
... 6:3
AI vs ML vs DL vs Generative AI

codebasics

19 days ago 4K views

Artificial intelligence encompasses solving problems requiring human intelligence, such as driving or creative writing. Machine learning is a subset of AI where algorithms like linear regression derive patterns from input-output pairs. Deep learning, a further subdomain, utilizes neural networks for complex tasks involving unstructured data like images and audio. The concept of generative AI, using deep learning, focuses on producing new content such as text and videos. Algorithms and models trained on historical data enable predictions in various applications, including real estate pricing and fraud detection.
04:45 - 04:55

Generative AI involves deep learning architectures for text and video creation.

Machine Learning Education
... 21:13
why ai neural networks will change trading forever and how to build yours in minutes!

QuantProgram

1 month ago 3K views

Machine learning and AI influence trading and investment decisions, particularly using neural networks to generate advanced strategies. Neural networks function similarly to human brains, identifying patterns through errors and adjustments in weight. Methods such as feedforward and recurrent neural networks are explained, with a focus on their applications in stock price prediction and strategy optimization. The discussion also covers the importance of hyperparameter tuning, challenges in computational demand, and the potential for improved returns compared to traditional S&P 500 investments. Applications of AI extend to portfolio rebalancing and enhancing existing strategies.
06:09 - 07:52

Machine learning models help optimize and rebalance investment portfolios for better returns.

Neural Network Architectures Finance
... 27:5
Donny Greenberg, Runhouse | theCUBE + NYSE Wired: Media Week - Cyber & AI Innovators Summit

SiliconANGLE theCUBE

1 month ago 1K views

Machine learning is experiencing a resurgence as infrastructure becomes competitive and mature. Recent advancements in AI applications highlight a pivotal shift in how engineers leverage new technologies. A notable uptick in talent has emerged as business models evolve from zero-interest rates, leading to increased investments in AI. The fundamental challenges in integrating machine learning with existing systems persist, emphasizing the need for robust infrastructure to support high-velocity work in AI development and deployment. Establishing an optimal system mindset is crucial for successfully navigating these challenges in the evolving AI landscape.
02:05 - 02:11

A vacuum for reference architecture for Enterprise AI infra has been noted.

Runhouse Cybersecurity Digital Media
... 4:16
Understanding the building blocks of artificial intelligence

evolvAI

1 month ago 2K views

Artificial intelligence (AI) teaches computers to think and learn like humans using data, algorithms, machine learning, and neural networks. The process starts with data, just as humans learn through experience. Algorithms guide how AI uses this data, while machine learning allows AI to improve through experiences, categorized mainly into supervised, unsupervised, and reinforcement learning. Neural networks simulate the human brain to process complex information like facial recognition or autonomous driving, ultimately making AI smarter. Understanding these foundations simplifies the complexity of AI technology and its applications.
02:16 - 02:29

Machine learning enables AI to learn and improve from various learning types.

Machine Learning Education
... 4:16
Understanding the building blocks of artificial intelligence

evolvAI

1 month ago 2K views

Artificial intelligence (AI) teaches computers to think and learn like humans using data, algorithms, machine learning, and neural networks. The process starts with data, just as humans learn through experience. Algorithms guide how AI uses this data, while machine learning allows AI to improve through experiences, categorized mainly into supervised, unsupervised, and reinforcement learning. Neural networks simulate the human brain to process complex information like facial recognition or autonomous driving, ultimately making AI smarter. Understanding these foundations simplifies the complexity of AI technology and its applications.
02:16 - 02:29

Machine learning enables AI to learn and improve from various learning types.

Machine Learning Education