What is Data Mining

When we think of mining, it sounds manual, tedious, and unfruitful — after all, hacking away at rock walls for hours on end hoping to find gold sounds like a lot of work for a very small reward.

Data Mining, however, is quite the opposite — without doing much work at all, you can reap rewarding results. That’s because we have modern solutions which do it for us. These softwares can sift through terabytes of data within minutes, giving us valuable insights on patterns, journeys, and relationships in the data.

So, let’s dive into what Data Mining is, how we do it, and what its examples look like.
0:00 Intro
0:41 What is Data Mining?
2:02 What are the steps in Data Mining
4:20 Data Mining example
Some of the sources we used in our video:

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[ocfuzxm]

Thank you so much for making this vedio and the editing is really awesome ❤

[ronakjoshi]

Thank you so much, this is the perfect video to help me for my data science presentation😁

[yomo]

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...

[sajolsajol]

Thank you very much...
That was a very good explanation....

[nessim.liamani]

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.

[mingo]

Once I learn the skill of Data Mining... how do I generate money for performing this services ?