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Getting Started w/ Hands-On AI experience

When you embark upon a journey, it's always smart to pick your tools appropriately. This is especially important for those new to the world of data science and artificial intelligence which is filled with different programming languages and frameworks. This can be a daunting situation, especially for those who are just starting. The Python programming language has surfaced as the go-to choice for data scientists and AI-enthusiasts. A 2019 Github analysis found it to be the most popular tool. But be it as it may, the process required to set up your environment can be daunting and discouraging, especially for newcomers. Even if getting started with Python is beginner-friendly and allows you to get ready and learn the basics for the language with ease, when it comes to artificial intelligence and machine learning related projects, there are some considerations to be taken into account. 

Some easier approaches include setting up Jupyter notebooks or anaconda, generally used tools in the AI landscape. It isn't impossible to do it, but we should strive to make these kinds of tools more accessible for all in a technical sense. Jupyter notebook is an application for many programming languages and allows people to share documents with code. Anaconda is an open-source data science platform for Python and R programming which “simplifies package management and deployment.” We should strive to make these kinds of tools more accessible for all.

This is what Google Colab achieves. A tool created by Google that allows you to get started with Python for machine learning without having to deal with package management, virtual environments, etc. Not only does it let you do this, but it also helps you share your code and collaborate with friends and colleagues while harnessing Google's computing power. It comes with examples and easily inserted code samples. Moreover, it enables you to access data stored in Google Drive and some other Google services. It allows you to perform tasks such as data plotting or model training using Google’s cloud computing resources while displaying everything in an appropriate manner. 

Recently, I have worked on projects involving the use of Python libraries such as Scikit-learn and even TensorFlow. Using Colab made my experience much more smooth and allowed me to worry about what mattered. 

If you want to get started in the world of data science either as a discipline of its own or to aid you in any research project or new development, you will not only need to have the right tools, but you will also need to know how to use them. Here are two courses that are helpful. If you’re looking for a more hands-on approach and a project-based curriculum covering different areas, FreeCodeCamp offers new courses on data analysis or scientific computing . If on the other hand, you prefer a more structured course, Stanford professor Andrew Ng has a course on Machine Learning. Another resource to check is Kaggle which, besides offering a notebook environment similar to Google’s, also hosts over 19,000 datasets that are free to use. Kaggle also offers micro-courses and competitions you can take part in. These tools and resources will help you get started with an interactive approach. 


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