What are data science ML and AI
Artificial Intelligence vs. Machine Learning vs. Deep Learning
1 | Artificial Intelligence
Artificial intelligence is an umbrella term and describes the rough approach of using machines to imitate intelligent human behavior in order to solve problems.
This approach is by no means new: the term "Artificial Intelligence" was first introduced in 1956 at the Dartmouth Conference in a workshop. However, AI only became really relevant in the last few years, especially from 2015. This is due, on the one hand, to the computing power, which has improved significantly in recent years, and, on the other hand, to the availability of data (e.g. images and videos on the Internet ), which has also increased significantly. We will clarify in a moment why exactly these two points are so important.
A simple, general example for the use of AI is the spam filter in your e-mail inbox: A person would manually sort out certain e-mails based on characteristics (sender address, certain words in the subject line, etc.). This process can also be "artificially" mapped: by writing a program that takes on exactly this task and sorting out the e-mails for us based on the characteristics. Intelligent, human behavior was imitated here - so it is AI.
However, this AI would be very limited in its capabilities. What happens if the senders of the spam emails change their sender address or the words in the subject line? Then the spam filter would no longer work.
So you need technology that learns by itself and adapts accordingly. This technology is already in use - Machine learning.
2 |Machine learning
Machine learning is a technology that is used to achieve artificial intelligence.
First, data (e.g. images, videos, audio files, statistics, etc.) are collected and fed into the program - this is called input. This data is then passed on to the program, which uses a more complex algorithm to analyze the data and make predictions and make decisions. The special feature is that machine learning programs learn without human intervention.
To come back to our example with the spam filter: First you would show the system a lot of different non-spam emails. Then it would show him a lot of different spam emails. The system analyzes these emails, finds differences and similarities (Data mining) and forms its own rules based on which it classifies an email as spam or not spam. A machine learning system learns and becomes more intelligent with more data. The general science that deals with data, statistics, algorithms, and machine learning is called Data science.
Further everyday examples for the use of machine learning are:
- The face recognition function of your smartphone
- The delivery of the results from your Google search
- Weather forecast
- The series and product suggestions on Netflix, Youtube and Amazon
In general, the following rule applies to machine learning: the more data the algorithm receives, the more precise the result.
In recent years, more and more data volumes have become available, the computing power has been increased by the further development of so-called GPUs (Graphics processing units) optimized and the algorithms further refined. This further development of machine learning is called Deep learning designated.
3 |Deep learning
Deep learning is the further development of machine learning. The technology makes use of so-called neural networks (also known as artificial neural networks).
These Artificial Neural Networks (ANN) are originally inspired by how the human brain works:
When the brain receives information, it tries to decipher it by categorizing the information according to characteristics. If it receives new information, it is compared with the information already available in order to interpret this information. This is a very rough description. In reality, these are highly complex processes that take place within fractions of a second. Although attempts were initially made to imitate this artificially, ANNs now make use of complicated algorithms that have little in common with the human brain. These deep learning algorithms are far more complex than those used in “traditional” machine learning.
In order to achieve accurate results, deep learning requires enormous amounts of data and thus extreme computing power, which we are only just able to achieve with today's technology. So the future will be exciting.
Which programming language should I learn for this?
The programming languages most commonly used in Artificial Intelligence, Machine Learning and Deep Learning are:
- C ++
Siraj Raval, one of the most famous YouTubers in the field of machine learning, has published an interesting video on this topic:
Do you want to learn Python in 30 days? ›Here you can find our complete Python course.
(Proofread by Markov Solutions)
- How did Kilimanjaro come about
- Which is the best touring bike
- Why are there bumps on my fingers
- What are some examples of purebred genotypes
- How do I stay away from smoking
- What is the work of supercomputers
- What diseases did Irrfan Khan suffer from
- Shopify is a web host
- What words express disgust in Lithuanian?
- Makes online rummy real money
- Alan Watts was really enlightened
- How do I check the earlier punctuality of the trains
- What do you prefer to meditation
- How can we ridicule AWS services
- Women's problem with me
- Is something really important in this life
- What does it take to be disciplined
- What are the uses of Fibonacci numbers
- Is Soluto dead
- Are vidyamandir classes good or bad
- Why should people think polygamy is moral?
- Can statins cause abdominal pain
- Is there a Bitcoin address wallet for India
- Which MNC has the best work culture