In the modern world, Artificial Intelligence (AI) and Machine Learning (ML) based solutions have been presented to the modern era through self-driving cars and recommendation systems. What is curious to notice though, is how the two terms are used interchangeably these days and represent a distinct and interrelated field. With this in mind, the AI vs ML relationship should be understood by any modern technology enthusiast and Python forms a significant pivot in the manifestation of such advanced solutions.
In this blog, we will discuss some insights about how AI and ML are connected, as well as how Python is used to produce AI-designed technologies.
What is Artificial Intelligence (AI)?
Artificial Intelligence (AI) is the term used to describe the provision of human intelligence to machines. AI is the general title for computers operating on what humans consider to be intelligent work. This comprises a vast aspect of technologies that allow machines to execute jobs that usually demand human intelligence, such as visual perception, voice recognition, decision-making, and translation of languages.
There are two broad categories of AI:
Narrow AI (weak AI): It is an AI system that is specifically designed to accomplish one particular task (like facial recognition or voice assistant).
General AI or Strong AI: A more developed concept for AI systems that is very close to human intelligence; it will be able to perform various tasks on its own without any help from humans and it is mostly still theoretical.
What is Machine Learning (ML)?
It can be said that it is a subcategory of AI in that it enables computers to make predictions or decisions without explicit programming through the explicit use of experience in the learning process. It works as Machine learning algorithms learn by employing large data sets and improving their performances as they spot patterns in these data sets.
The three main categories of ML are classified as follows:
Supervised learning: train algorithm on labelled data, which will learn how to map input to output.
Unsupervised learning: An algorithm learns patterns and underlying structure from unlabelled data.
Reinforcement Learning: This algorithm learns by experimenting by acquiring rewards or penalties due to actions pursued.
The Role of Python in AI and Machine Learning
Python has emerged as the most ideal language for AI and ML for its ease of learning, ease of reading, and availability of libraries.
Now, let’s look at a few ways through which Python supports AI and ML development:
Python Libraries for Machine Learning
Python has a number of strong libraries that make it easier to create ML algorithms:
Scikit-learn: it supports a large number of algorithms for general ML and covers topics from simple linear regression to complex operations like clustering and classification.
TensorFlow: Google’s open-source framework. The primary use is in building deep learning models or neural networks and other applications powered by AI.
Keras: A general-purpose neural network API that sits at a very high level and is built on top of TensorFlow and Theano, allowing users to test out deep learning approaches very easily.
Python Libraries for Artificial Intelligence
Python also offers a robust set of libraries for AI application development:
OpenAI Gym: A software that has been used in the design of reinforcement learning algorithms and used for comparative analysis.
Numpy and Pandas: Data manipulation and analysing libraries which are used prior to executing the data in AI and ML applications.
PyTorch: Deep learning library for applications in computer vision, natural language processing and so on that is open source.
AI and ML Applications in Real-world Scenarios
The applications of AI and ML in everyday life transform industries at breakneck speeds. Some real-world examples of places where AI and ML play a strong role include:
Healthcare: Applying the concept of AI-based results to both medical diagnosis and also medical treatment planning and discovery of drugs.
Finance: Predictive Analytics for Stock Market Prospects, Fraud Detection, and Risk Management.
Retail: AI-based recommendation engines which analyse customer behaviour and provide suggestions for products.
Transport: Driverless cars and smart AI which could be used to fine-tune routes and control traffic.
How to Learn AI and ML with Python
To learn AI and ML with Python, begin with the following steps:
Learn Python Basics: A good grasp of Python basics is very important before diving into AI and ML. This entails learning the essential items of Python, comprising data structures, functions, and loops.
Familiarise with key libraries: Familiarise with libraries like Numpy, Pandas, Matplotlib, Scikit-learn, TensorFlow, and PyTorch.
Basic ML algorithms: Students need to learn linear regression, decision trees, and k-nearest neighbours, which must be started with, followed by the more complex algorithms like neural networks and deep learning.
Practice on Projects: Design simple projects such as a recommendation engine, a chatbot, or a predictive model. Through practice and your hands-on approach, understanding will come.
Conclusion
Artificial Intelligence and Machine Learning are changing the patterns within industries, and Python is a key tool in that change. Learning Python and associated AI/ML libraries gives you the ideal leverage for building AI-driven solutions and starting a promising career in tech. The London School of Emerging Technology (LSET) has developed a course for Machine Learning with Python. LSET understands the changing rapid trend in the ML and AI fields and developed their course, considering new trends and tools. Pupils will also get the opportunity to get into internship opportunities.