PYTHON AI & MACHINE LEARNING
AI and ML uses vast quantities of data for their processing and interpretation that cannot be performed manually by the human brain due to substantially increased volumes and data strength. Enhancing market quality, lower cost of production and improved output are the added values. In analytics, AI is now being implemented to generate forecasts that can help individuals create strong plans and search for more efficient solutions. In order to do market analysis and foresee where to spend funds for bigger gains, FinTech applies AI to investment platforms. AI is used by the travel industry to provide customised feedback or to launch chatbots, as well as to boost the overall user experience. These examples illustrate that process data loads are used by AI and ML to provide a faster, more personal and accurate user experience.
PYTHON USES OF AI AND ML
BENEFITS OF PYTHON
AI-fueled businesses are, according to Deloitte's report, the latest trend in technological change aimed at improving productivity.
INDUSTRIES USING PYTHON FOR AI AND ML
1. Travel
2. Fintech
3. Transportation
4. Health
5. Insurance
6. Computer Software & Hardware
PYTHON’S STRONG SIDE
IBM's machine learning department, Jean Francois Puget, shared his view that Python is the most common language for AI and ML and based it on trend search results on indeed.com
1. VARIETY OF LIBRARIES
One of the key reasons why Python is the most common programming language used for AI is the great choice of libraries. A library is a module or a collection of modules that have been released by various sources. This requires a pre-written piece of code that allows users to access certain features or perform various actions. Python libraries contain simple objects, so developers don't always have to code them from the very beginning.
PYTHON LIBRARIES
Scikit-learn for handling basic ML algorithms like clustering, linear and logistic regressions, regression, classification, and others.
Pandas for high-level data structures and analysis. It allows merging and filtering of data, as well as gathering it from other external sources like Excel, for instance.
Keras for deep learning. It allows fast calculations and prototyping, as it uses the GPU in addition to the CPU of the computer.
TensorFlow for working with deep learning by setting up, training, and utilizing artificial neural networks with massive datasets.
Matplotlib for creating 2D plots, histograms, charts, and other forms of visualization.
NLTK for working with computational linguistics, natural language recognition, and processing.
Scikit-image for image processing.
PyBrain for neural networks, unsupervised and reinforcement learning.
Caffe for deep learning that allows switching between the CPU and the GPU and processing 60+ mln images a day using a single NVIDIA K40 GPU.
StatsModels for statistical algorithms and data exploration.
Here’s a table of сommon AI cases and technologies used
2. LOW ENTRY BARRIER
The vocabulary of Python programming is similar to the daily English language, which makes the learning process simpler. Its simple syntax helps you to work with complex structures easily, maintaining consistent relationships between elements of the system.
3. PLIANCY
Programmers have a chance to take the situation under control and choose the programming styles which they are fully comfortable with or even combine these styles to solve different types of problems in the most efficient way.
The imperative style consists of commands that describe how a computer should perform these commands. With this style, you define the sequence of computations which happen like a change of the program state.
The functional style is also called declarative because it declares what operations should be performed. It doesn’t consider the program state, compared to the imperative style, it declares statements in the form of mathematical equations.
The object-oriented style is based on two concepts: class and object, where similar objects form classes. This style is not fully supported by Python, as it can’t fully perform encapsulation, but developers can still use this style to a finite degree.
The procedural style is the most common among beginners, as it proceeds tasks in a step-by-step format. It’s often used for sequencing, iteration, modularization, and selection.
4. PLATFORM VERSATILE
Python can run on any platform, including Windows, MacOS, Linux, Unix, and twenty-one others, to improve machine learning. Developers need to incorporate many small-scale modifications to move the method from one platform to another and change certain lines of code to construct an executable type of code for the chosen platform. Packages like PyInstaller can be used by developers to prepare their code for running on various platforms. Again, on different channels, this saves time and resources for experiments and makes the overall process quick and easy.
5. DECIPHERABLE
Python is very simple to read, so any developer of Python can understand their peers' code and modify, copy or share it. There is no misunderstanding, mistakes or contradictory paradigms, and this leads to an effective exchange between AI and ML professionals of algorithms, concepts, and instruments. For better data understanding, efficient presentation and visualisation, libraries such as Matplotlib allow data scientists to create charts, histograms and plots. Different application programming interfaces often simplify the process of visualisation and make simple reports easier to produce.
6. ULLUMNI
As well as in Python communities and forums, a lot of Python documentation is available online, where programmers and machine learning developers discuss mistakes, fix issues, and help each other out. Python is becoming more and more common among data scientists as a result of the benefits mentioned above. Python, with its simplicity, large community, and tools allows developers to build architectures that are close to perfection while keeping the focus on business-driven tasks.
CONCLUSION
The world we live in is deeply influenced by computer-based intelligence or artificial intelligence, with new technologies growing steadily. For the numerous benefits that make it particularly desirable for AI and profound learning projects, brilliant programmers pick Python as their go-to programming language.
It also reduces the psychological overhead of engineers, opening up their mental assets with the aim of concentrating on strategic thought and achieving venture goals. Finally, the consistent punctuation makes it easier to work together or to switch between designers.
Although other programming dialects can also be used in AI projects, there is no escape from the front line of Python, and critical thinking should be given. For AI company, this is the reason you should consider Python.
REFERENCE
https://steelkiwi.com/blog/python-for-ai-and-machine-learning/
https://technative.io/why-use-python-for-ai-and-machine-learning/
W. https://hart-coded-profile.com
L: https://www.dhirubhai.net/in/yolande-hart