Comprehensive Guide to Artificial Intelligence with Python (Translate.p1)

Tram Ho

Source: Edureka

Artificial intelligence with Python:

Artificial intelligence has been around for more than half a century and its advances are growing at an exponential rate. The demand for AI is at its highest and you want to learn about AI, you’ve found the right place. This article on Artificial Intelligence with Python will help you understand all the concepts of Ai with real implementations in Python.

For in-depth knowledge of Artificial Intelligence, you can apply directly to the Machine Learning Engineer Master Program by Edureka with 24/7 support and lifetime access.

Topics covered by the article:

  1. Why Is Python Best For AI?
  2. Demand For AI
  3. What Is Artificial Intelligence?
  4. Types Of Artificial Intelligence
  5. Machine Learning Basics
  6. Types Of Machine Learning
  7. Types Of Problems Solved By Using Machine Learning
  8. Machine Learning Process
  9. Machine Learning With Python
  10. Limitations Of Machine Learning
  11. Why Deep Learning?
  12. How Deep Learning Works?
  13. What Is Deep Learning?
  14. Deep Learning Use Case
  15. Perceptrons
  16. Multilayer Perceptrons
  17. Deep Learning With Python
  18. Introduction To Natural Language Processing (NLP)
  19. NLP Applications
  20. Terminologies In NLP

(Because the content is quite long, so in this article I will share the first 4 topics, the remaining topics I will put in the next article.)

1. Why Is Python Best For AI?

A lot of people have asked me, “Which programming language is best for AI?” or “Why choose Python for AI?”

Although the language has a common purpose, Python has found its way into the most complex technologies like AI, Machine Learning, Deep Learning, etc.

Why is python so popular in all these areas?

Below is a list of the reasons why Python is the language choice for every Developer, Data Scientist, Machine Learning Engineer, …

  • Less Code: Deploying Who to tons of algorithms. Python support for pre-defined packages, we cannot code algorithms. And to make things easier, Python provides testing in English as you write your method code to reduce the code checking burden.
  • Prebuilt Libraries: Python has 100 built-in libraries to implement various Machine Learning and Deep Learning algorithms. So every time you want to run an algorithm on a data set, all you have to do is install and download the necessary packages with a single command. Examples of pre-built libraries include numPy, Keras, Tensolflow, Pytorch, etc.
  • Ease of learning: Python uses a very simple syntax that can be used to perform simple calculations like, adding two strings to complex processes like building a Machine Learning model.
  • Platform Independent: Python can run on multiple platforms including Windows, MacOS, Linux, etc.While transferring code from one platform to another, you can use packages like Pylnstaller that will handle any extra issues. belong.
  • Massive Community Support: Python has a huge user community that’s always helpful when we have coding problems. In addition to a large fan base, Python has many communities, groups, and forums where developers post their bugs and help each other.

If you want to learn intensive Python programming, read these blogs by following these links:

  1. Python Tutorial – A Complete Guide to Learn Python Programming
  2. Python Programming Language – Headstart With Python Basics
  3. A Beginners Guide To Python Functions
  4. Python for Data Science

Since this blog is all about Artificial Intelligence with Python, I will introduce you to the most effective and popular AI-based Python Libraries.

  1. Tensoflow: Developed by Google, this library is commonly used in Machine Learning algorithms and performing heavy calculations related to Neural Networks.
  2. Scikit-Learn: Scikit-Learn is a Python library associated with NumPy and SciPy. It is considered one of the best libraries for working with complex data.
  3. NumPy: NumPy is a Python library used specifically for calculating scientific and mathematical data
  4. Theano: Theano is a library of computational functions and efficient calculation of mathematical expressions related to multidimensional arrays.
  5. Keras: This library simplifies the implementation of neural networks. It also has the best functions for computing models, evaluating data sets, graph displays and more.
  6. NLTK: NLTK or Natural Language ToolKit is an open source Python library built specifically for Natural Language Processing, Text Analysis, Text Extraction, …

In addition to the libraries mentioned above, refer to Top 10 Python Libraries You Must Know In 2019 for better understanding.

Now that you know the important Python libraries used to deploy AI techniques, let’s focus on Artificial Intelligence. In the next section, I will cover all the basic concepts of AI.

2. Demand for AI

Ever since the advent of AI in the 1950s, we have witnessed exponential growth in its potential. But if AI has been here for more than half a century, why has it suddenly achieved such great importance? Why are we talking about Artificial Intelligence now?

The main reasons for the widespread popularity of AI are:

More computing power : Deploying AI power requires a lot of computing power because building AI models involves heavy computing and the use of complex neural networks. The invention of the GPU has made this possible. Finally we can perform high-level calculations and perform complex algorithms.

Data Generation: (Create data) Over the years, we have created a huge amount of data. That data needs to be analyzed and processed using Machine Learning algorithms and other AI techniques.

More Effective Algorithms: (More efficient algorithms) Over the past decade, we have successfully managed to develop modern algorithms related to the deployment of Deep Neural Networks.

Broad Investment: ( Broad Investment ) When technology giants like Tesla, Netflix and Facebook started investing in Artificial Intelligence, it became more popular leading to an increase in demand for systems based on on AI.

The development of artificial intelligence is exponential, it is also adding to the economy at a fast pace. So, this is the right time for you to enter the field of Artificial Intelligence.

3. What Is Artificial Intelligence?

The term Artificial Intelligence was first coined decades ago in 1956 by John McCarthy at the Dartmouth conference. He defines AI as:

“The science and engineering of making intelligent machines.”

Means: Science and engineering of intelligent machines .

In other words, AI is science that helps machines think and make decisions like humans .

In the recent past, AI has been able to do this by creating machines and robots that have been used in many areas including healthcare, robots, marketing, business analytics and more. again.

Now let’s discuss the different stages of Artificial Intelligence.

4. Types Of Artificial Intelligence

AI is structured in three stages of evolution:

  1. Artificial Narrow Intelligence ( Narrow Artificial Intelligence )
  2. Artificial General Intelligence ( Artificial Intelligence General )
  3. Artificial Super Intelligence ( Artificial Super Intelligence )

Artificial Narrow Intelligence

Often referred to as weak AI, narrow Artificial Intelligence only applies AI to specific tasks.

Existing AI-based systems that require the use of “artificial intelligence” are actually acting as a weak AI. Alexa is a good example of narrow intelligence. It operates within a limited predefined function range. Alexa has no real intelligence or self awareness.

Search engines Google, Sophia, self-driving cars and even the famous AlphaGo, belong to the weak AI group.

Artificial General Intelligence

Often referred to as powerful AI, Artificial Intelligence involves machines that possess the ability to perform any intellectual task that humans can.

You see, machines don’t possess human-like capabilities, they have a powerful processing unit that can perform high-level calculations but they don’t have the ability to think and reason like humans.

Many experts suspect that AGI will never be possible, and many people question whether it will be desirable.

Stephen Hawking, for example warns: “Strong AI would take off on its own, and re-design itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn’t compete and would be superseded. ” (AI Strong AI will take off and redesign itself at an ever increasing rate. Humans, limited by progress biochemistry is slow, uncompetitive and will be replaced.)

Artificial Super Intelligence

Super artificial intelligence is a term that refers to how long a computer’s ability will surpass a human.

ASI is now considered a hypothetical situation as depicted in movies and science fiction books, where machines have taken over the world. However, technology masterminds like Elon Musk believe that ASI will take over the world by 2040!

AI vs ML vs DL (Artificial Intelligence vs Machine Learning vs Deep Learning)

People tend to think that Artificial Intelligence, Machine Learning, and Deep Learning are similar because they have common applications. For example, Siri is an application of AI, Machine learning and Deep learning.

So how are these technologies related?

  • AI is a science that helps machines imitate human behavior.
  • ML is a subset of Artificial Intelligence (AI) focused on making machines make decisions by providing them with data.
  • DL is a subset of Machine Learning that uses the concept of neural networks to solve complex problems.

To synthesize AI, Machine Learning and Deep Learning are interconnected fields. Machine Learning and Deep learning support Artificial Intelligence by providing a set of algorithms and neural networks to solve data-based problems.

However, AI is not restricted to just ML and DL. It covers a wide range of areas including, Natural Language Processing (NLP), object detection, computer vision, robots, expert systems, …

Comprehensive Guide to Artificial Intelligence with Python (Translate.p2)

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