Welcome to the thrilling world today of Artificial Intelligence! What was once a futuristic scientific concept, Artificial Intelligence (AI) is a rapidly evolving field with the capability to transform the world.
My name is Rahul Mehtam and in this post, I will be going over some of the fundamentals of AI and how these can be implemented.
To start, AI is a simulation of human intelligence in technology. There are several components of AI including but not limited to Machine Learning (ML) and Deep Learning (DL). Machine Learning is a subset of AI involving the development of algorithms that allow computers to learn and make decisions, much like the human mind. Deep Learning is a specialized branch of ML that uses neural networks to model complex patterns in data.
Healthcare, Finance, and Transportation are just some of the aspects of human life that AI will be able to transform in the coming years, and it is highly recommended to keep your eyes on the AI field while it makes breakthrough after breakthrough.
Now that a general overview of AI has been given, I will specifically be speaking on Agile, Python, Numpy Arrays, and Pandas, all related to AI.
Agile
Agile is a project management methodology that promotes iterative development, collaboration, and flexibility. In the context of AI projects, Agile helps teams adapt quickly to changes, experiment with new ideas, and continuously improve their models and algorithms. Key principles of Agile include Iterative Development, Collaboration, Flexibility, and Customer Feedback. Interns in the AI field are just some of the people who often work in Agile teams.
Python
Python is the primary programming language used for ML due to its simplicity and its powerful libraries. A few of the basics that will be needed to program a ML model include but are not limited to: Variables and Data Types, Control Structures, and Libraries as previously mentioned. Numpy and Pandas are two of the most powerful Python libraries in the context of AI.
Numpy
Numpy is a fundamental library for scientific computing in Python. It supports large, multi-dimensional arrays and matrices and mathematical functions to operate on these arrays. Images/Videos/Frames can be converted into numpy array format for an ML model to be able to understand.
Key features of Numpy include
- ndarray: A powerful n-dimensional array object.
- Vectorization: Enabling operations on entire arrays without the need for explicit loops, leading to more efficient code.
- Broadcasting: Allowing arithmetic operations on arrays of different shapes.
Here is an example of how a numpy array can be created and manipulated.
![Manipulated]()
Pandas
Pandas is another essential library in the AI toolkit, designed for data manipulation and analysis. It provides data structures like DataFrame and Series, which make handling structured data much simpler. Pandas are commonly used to extract features from frames and have been used in projects to detect specific parts of images/videos.
Key features of pandas include
- DataFrame: A 2-dimensional labeled data structure with columns of potentially different types.
- Series: A 1-dimensional labeled array capable of holding any data type.
- Data Operations: Tools for data manipulation, such as merging, reshaping, selecting, and cleaning data.
Here is an example of using pandas to import and manipulate data.
![Manipulate data]()
Conclusion
By embracing Agile practices, mastering Python basics, and taking advantage of powerful libraries such as numpy and pandas, anybody can be well-prepared to tackle complex AI challenges and contribute to innovative solutions. Don't miss out on the future of AI, begin learning more about it now or else you'll wish you did!