Thank you for the informative video. These days a lot of people are using data mining to infiltrate companies and finding out what sort of projects they are working on and more...
Thanks, A question though : at 01:15 3 Disciplines of Data Mining : Statistics AI Machine Learning --- Please explain the diffrence between AI and ML as i don't get the subtility here. Thanks again --------- ChatGPT : In the context of data mining, the distinctions between AI and ML are often nuanced but significant: 1. **Artificial Intelligence (AI)**: - AI encompasses a broader set of techniques and approaches aimed at creating machines that can perform tasks requiring human-like intelligence. - In data mining, AI techniques may include not only machine learning but also other methods such as expert systems, natural language processing, computer vision, and robotics. - AI algorithms can range from rule-based systems to deep learning neural networks, and they are designed to mimic human cognitive functions like reasoning, problem-solving, and perception. - In data mining, AI may involve tasks such as automated decision-making, pattern recognition, anomaly detection, and predictive modeling. 2. **Machine Learning (ML)**: - ML is a subset of AI focused specifically on the development of algorithms and statistical models that enable computers to learn from and make predictions or decisions based on data. - ML algorithms learn from labeled or unlabeled data to identify patterns, relationships, and insights without being explicitly programmed for specific tasks. - In data mining, ML techniques are widely used for tasks such as classification, regression, clustering, association rule mining, and recommendation systems. - ML algorithms can be categorized into supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning, each suited to different types of data and objectives. In summary, while AI encompasses a broader range of techniques and capabilities aimed at mimicking human intelligence, ML is a specific subset of AI focused on developing algorithms that learn from data to perform tasks without explicit programming instructions. In the context of data mining, ML is a key component, but AI may involve a broader set of techniques beyond just machine learning algorithms.
Thank you so much, this is the perfect video to help me for my data science presentation😁
Welcome 😄
Thank you for the informative video. These days a lot of people are using data mining to infiltrate companies and finding out what sort of projects they are working on and more...
Thanks,
A question though :
at 01:15
3 Disciplines of Data Mining :
Statistics
AI
Machine Learning
---
Please explain the diffrence between AI and ML as i don't get the subtility here.
Thanks again
---------
ChatGPT :
In the context of data mining, the distinctions between AI and ML are often nuanced but significant:
1. **Artificial Intelligence (AI)**:
- AI encompasses a broader set of techniques and approaches aimed at creating machines that can perform tasks requiring human-like intelligence.
- In data mining, AI techniques may include not only machine learning but also other methods such as expert systems, natural language processing, computer vision, and robotics.
- AI algorithms can range from rule-based systems to deep learning neural networks, and they are designed to mimic human cognitive functions like reasoning, problem-solving, and perception.
- In data mining, AI may involve tasks such as automated decision-making, pattern recognition, anomaly detection, and predictive modeling.
2. **Machine Learning (ML)**:
- ML is a subset of AI focused specifically on the development of algorithms and statistical models that enable computers to learn from and make predictions or decisions based on data.
- ML algorithms learn from labeled or unlabeled data to identify patterns, relationships, and insights without being explicitly programmed for specific tasks.
- In data mining, ML techniques are widely used for tasks such as classification, regression, clustering, association rule mining, and recommendation systems.
- ML algorithms can be categorized into supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning, each suited to different types of data and objectives.
In summary, while AI encompasses a broader range of techniques and capabilities aimed at mimicking human intelligence, ML is a specific subset of AI focused on developing algorithms that learn from data to perform tasks without explicit programming instructions. In the context of data mining, ML is a key component, but AI may involve a broader set of techniques beyond just machine learning algorithms.
Thank you very much...
That was a very good explanation....
Thank you 😀 Glad it was helpful!
Data means it's the collect of raw information
Well explained 👍👏👏
Thank you for your support. On that note, here is another video you might like
th-cam.com/video/pzvT6Z_b6MA/w-d-xo.html
Thank you 😊
Thank you!!
Welcome!
welcome☺
Thank you
welcome☺
Once I learn the skill of Data Mining... how do I generate money for performing this services ?
Well useful
Thanks a lot
thanks
T Sing