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... 4:53
AI vs Machine Learning vs Data Science (What’s the Difference?)

365 Data Science

1+ months ago 2K views

Artificial Intelligence (AI) focuses on creating intelligent machines, drawing from various disciplines like mathematics and psychology. AI powers innovations such as virtual assistants and autonomous vehicles. Machine Learning (ML), a subfield of AI, uses data for predicting outcomes by recognizing patterns. Data Science incorporates AI and ML while also utilizing traditional methods like data visualization and statistical analysis to derive insights from data. Understanding the distinctions and interrelationships between AI, ML, and Data Science is essential for navigating today's technology landscape.
02:19 - 04:10

Machine learning identifies patterns to predict outcomes, crucial for AI functionality.

Machine Learning Education
... 13:11
AI & Machine Learning Transforming Businesses. Artificial Intelligence and Machine Learning software

Travel Hotels Vacations - Since 2019

1+ months ago 3K views

Artificial intelligence and machine learning are transforming business operations by enhancing efficiency, decision-making, and customer-centric strategies. Companies are adopting AI-driven analytics, automation, and intelligent systems that streamline processes and improve accuracy. The evolving demand for advanced solutions reflects the significant value these technologies deliver, enabling businesses to maintain a competitive edge. AI tools provide actionable insights from large data sets, optimize customer interactions through chatbots, and automate repetitive tasks. As AI continues to advance, its integration into various industries is becoming necessary for improved operations and customer satisfaction.
02:57 - 03:23

AI chatbots provide automated customer support, enhancing service efficiency.

Machine Learning Research & Innovations
... 8:15
Maximize AI Potential with an Ensemble of AI Models

IBM Technology

1+ months ago 2K views

AI's integration in business is evolving rapidly, necessitating an adaptable approach to leveraging an ensemble of AI models tailored to specific situations. Traditional AI, based on machine learning and deep learning, excels with structured data while large language models (LLMs) handle unstructured data with higher accuracy but greater resource demands. By understanding their characteristics, businesses can intelligently switch between models for optimal predictions and efficiency. Practical applications are illustrated through examples in fraud analysis and insurance claims, emphasizing the importance of accuracy and speed in decision-making processes.
03:10 - 04:20

Differences in AI models impact their efficiency and application.

IBM Machine Learning Education
... 42:13
How Is Artificial Intelligence Transforming Stock Trading? - EP 357 - Nirav Shah

Asia Tech Podcast Official

1+ months ago 20K views

Human behavior influenced by subtle patterns can be better recognized by AI, opening avenues in predictive analytics for investment decision-making within a data-rich environment. This requires unraveling complex strategies tied to alternative data and the challenges in identifying alpha in crowded markets. The conversation explores how AI and machine learning are reshaping investment processes and investment strategies, emphasizing the journey from engineering to quantitative trading. The focus lies on innovative methodologies and tools in finance while remaining cautious of hidden biases and noise in data.
22:34 - 22:39

Multiple brokers are leveraged to ensure competitive algorithmic trading.

Dr Tech Predictive Analytics Finance
... 10:12
AI Software Revolution Transforming Industries. Machine Learning Software, AI-powered analytics

Computer Science Quad

1+ months ago 3K views

Artificial intelligence and machine learning software are transforming industries through automation and enhanced decision-making. These technologies enable organizations to process large amounts of data, identify trends, and improve customer experiences. AI-powered analytics provide valuable insights, allowing businesses to optimize operations and respond quickly to market needs. Additionally, AI applications in customer support, finance, healthcare, and marketing enhance efficiency, accuracy, and innovation while reducing operational costs. By leveraging AI tools, companies gain a competitive advantage in the digital landscape, leading to improved performance and growth opportunities.
03:09 - 03:31

AI-driven automation tools streamline repetitive tasks, increasing productivity with less human input.

Machine Learning Research & Innovations
... 9:47
How I Would Learn AI as a Software Engineer/Programmer (If I Could Start Over in 2025)

Atef Ataya

1+ months ago 1K views

Learning AI effectively begins with mastering Python and basic math to ensure understanding of underlying principles. Focusing on foundational concepts like regression and classification is crucial before tackling deep learning. Utilizing libraries like Scikit-learn can ease the learning curve through practical projects. Building applications with tools featuring real-world projects showcases acquired skills. Continuous engagement with community and current research helps in staying updated. Ultimately, a structured, hands-on approach, starting from basics to advanced frameworks like large language models, is essential for success in the rapidly evolving AI landscape.
03:17 - 03:47

Interactive, hands-on learning through platforms like DataCamp enriches AI education.

Machine Learning Education
... 1:31:22
Yoshua Bengio’s AI Masterclass: The Future of Machine Learning & Deep Learning

LunarTech

1+ months ago 2K views

Deep learning is a crucial aspect of machine learning, which allows computers to learn from data and represent information at various levels of abstraction. This approach, inspired by brain functions and neural networks, has advanced significantly in applications such as speech recognition and computer vision. The quest to understand intelligence's underlying principles continues, drawing on insights from neuroscience and other disciplines. Current challenges include understanding the credit assignment problem and the importance of generalization in AI. This journey involves evaluating the technology's implications for the future and addressing ethical considerations.
06:51 - 07:08

Recent advances in AI applications include significant improvements in speech and image recognition.

LunarTech Machine Learning Education
... 11:48
#ONPASSIVE |UPDATE FOR FOUNDERS : ARTIFICIAL INTELLIGENCE | AI VS ML |FULL INFO & LATEST UPDATE

Shanti Ghosh Videos

1+ months ago 1K views

Artificial intelligence (AI) is a top branch of computer science allowing machines to think, while machine learning (ML) is a subset that enables machines to learn from data using statistical techniques. AI encompasses various branches like robotics and natural language processing, leading to applications such as self-driving cars and virtual assistants. The video explains the three types of ML: supervised, unsupervised, and reinforcement learning, and illustrates their applications in everyday scenarios, such as recommendations by Amazon and YouTube. The discussion emphasizes the need to understand these technologies as they shape the future of various domains.
04:43 - 04:59

Three types of machine learning: supervised, unsupervised, and reinforcement learning.

ONPASSIVE Machine Learning AI Startups
... 11:48
#ONPASSIVE |UPDATE FOR FOUNDERS : ARTIFICIAL INTELLIGENCE | AI VS ML |FULL INFO & LATEST UPDATE

Shanti Ghosh Videos

1+ months ago 1K views

Artificial intelligence (AI) is a top branch of computer science allowing machines to think, while machine learning (ML) is a subset that enables machines to learn from data using statistical techniques. AI encompasses various branches like robotics and natural language processing, leading to applications such as self-driving cars and virtual assistants. The video explains the three types of ML: supervised, unsupervised, and reinforcement learning, and illustrates their applications in everyday scenarios, such as recommendations by Amazon and YouTube. The discussion emphasizes the need to understand these technologies as they shape the future of various domains.
04:43 - 04:59

Three types of machine learning: supervised, unsupervised, and reinforcement learning.

ONPASSIVE Machine Learning AI Startups