Published on 27 July 2023 7 min read

Use Anaconda distribution for Python and R
Learn to use Jupyter notebooks
Get familiar with NumPy, Pandas, and Matplotlib
Practice Python through Kaggle competitions
Learn Python libraries for AI

Deciding to dive into Artificial Intelligence (AI) development can feel quite daunting, especially when you're just getting started. One of the first questions that may pop up is, "Should I start learning Python for AI development?" The answer is a resounding yes! Python's versatility and simplicity make it a great choice for AI programming. This blog post will guide you through five practical Python tips to kickstart your journey in AI development.

Use Anaconda distribution for Python and R

Let's start with a tip that'll simplify your programming life significantly. Anaconda is a free and open-source distribution of Python and R programming languages that is widely used in data science, machine learning, and AI. It's like a Swiss army knife for programming and can be a game changer when you start learning Python for AI development.

Why Anaconda?

So, why should you choose Anaconda? Here are a few reasons:

Pre-packaged libraries: Anaconda comes with over 1,500 Python/R data science packages. This means you won't have to install each library individually, saving you a lot of time and potential headaches.
Environment management: When working on different projects, you might need different versions of Python and its libraries. Anaconda makes it easy for you to create isolated environments for each project, so there's no need to worry about version conflicts.
Easy to use: Anaconda comes with a desktop graphical user interface, Anaconda Navigator, which makes it easier to launch applications and manage packages and environments.

Getting started with Anaconda

Now that you know why Anaconda is a good choice, let's move on to how you can get started with it:

Download and install: Visit the official Anaconda website and download the installer for your operating system. Follow the installation instructions, and you're good to go!
Launch Anaconda Navigator: After installation, you can launch the Anaconda Navigator from your start menu or application list. From here, you can start exploring the pre-packaged tools and libraries.
Create your first environment: To create a new environment, click on the 'Environments' tab on the left, then click on 'Create'. Give your environment a name, choose the Python version you need, and start building.

There you have it. With Anaconda in your toolkit, you're one step closer to mastering Python for AI development.

Learn to use Jupyter notebooks

With Anaconda in your toolkit, it's now time to dive into the world of Jupyter notebooks. Jupyter notebooks are an open-source web application that allows you to create and share documents containing live code, equations, visualizations, and narrative text. If you're wondering whether you should start learning Python for AI development, Jupyter notebooks are a compelling reason to say yes.

Why Jupyter notebooks?

There are several reasons why Jupyter notebooks are a fantastic tool for Python programming:

Interactive programming: With Jupyter notebooks, you can write your code in cells and run them individually. This makes it easy to test and debug specific parts of your code without having to run the whole script every time.
Data visualization: Notebooks support various libraries such as Matplotlib, Plotly, and Bokeh for data visualization. You can create beautiful graphs right in your notebook.
Documentation: Jupyter notebooks allow you to add comments and explanations between code cells, making it perfect for documentation and presentation of your AI projects.

Getting started with Jupyter notebooks

Now let's see how you can get your hands dirty with Jupyter notebooks:

Install Jupyter: If you've installed Anaconda, you already have Jupyter installed. If not, you can install it using pip, Python's package manager.
Launch Jupyter: You can launch Jupyter from Anaconda Navigator or by typing 'jupyter notebook' in your terminal or command prompt. This will open Jupyter in your web browser.
Create your first notebook: To create a new notebook, click on 'New' and select 'Python'. You can now start writing your Python code in the notebook.

Getting the hang of Jupyter notebooks will take your Python skills to the next level and put you on the right path for AI development.

Get familiar with NumPy, Pandas, and Matplotlib

As your Python journey continues, you'll want to get familiar with a trio of tools that are fundamental to AI development: NumPy, Pandas, and Matplotlib. These libraries are not only powerful, but they also work well together, making your Python learning journey smoother and more enjoyable.

Why NumPy, Pandas, and Matplotlib?

Each of these libraries plays a unique role in Python programming, particularly in AI:

NumPy: Short for 'Numerical Python', NumPy is all about mathematical computing. It's the go-to library for handling large, multi-dimensional arrays and matrices, and it offers a large library of high-level mathematical functions to operate on these arrays.
Pandas: Pandas is designed for data manipulation and analysis. It provides data structures for efficiently storing large datasets and tools for data wrangling and analysis. If you're working with machine learning, you'll find Pandas handy for preprocessing your data.
Matplotlib: Data visualization is a big part of AI development, and that's where Matplotlib comes in. This Python library helps you create static, animated, and interactive visualizations in Python.

Getting started with NumPy, Pandas, and Matplotlib

Here are a few steps to get you started with these libraries:

Install the libraries: If you have Anaconda, you already have these libraries installed. Otherwise, you can install them using pip.
Start playing with the libraries: Open a Jupyter notebook and start experimenting with these libraries. Try creating arrays with NumPy, dataframes with Pandas, and simple plots with Matplotlib.
Explore online resources: There are plenty of online tutorials and documentation to help you learn these libraries. The official documentation for each library is a great place to start.

By getting comfortable with NumPy, Pandas, and Matplotlib, you're building a strong foundation for your Python journey—especially if you're considering diving into AI development.

Practice Python through Kaggle competitions

Looking to put your Python skills to the test and gain some real-world experience? Kaggle competitions are a great place to do just that. Kaggle is a platform where data scientists and AI enthusiasts compete to solve complex data science problems using machine learning and AI.

What's the deal with Kaggle?

Imagine a place where you can learn, grow and challenge yourself, all while helping to solve some of the world's most intricate data problems. That's Kaggle! It's a community that encourages learning through doing, which is excellent if you're wondering, "Should I start learning Python for AI development?"

Competitions: Kaggle hosts competitions where you can apply your Python and AI skills. Competitions range from beginner level to expert, so there's something for everyone.
Learning Resources: Kaggle also provides a wealth of learning resources. You can learn Python, machine learning, and other data science topics through their free courses.
Community: One of the best parts of Kaggle is its community. You can learn a lot just by reading through the discussion forums and notebooks shared by other Kagglers.

How to get the most out of Kaggle competitions

Participating in Kaggle competitions can be a lot of fun, but it's also a great way to learn. Here are some tips to help you get started:

Start with the 'Getting Started' competitions: These are designed for beginners and come with a tutorial to help you get started.
Read the forums and notebooks: Kaggle competitions have forums where participants discuss the competition and share ideas. You can also find notebooks where participants share their code and explain their approach.
Don't be afraid to ask questions: If you're stuck or don't understand something, ask! The Kaggle community is incredibly supportive and always ready to help.

So, if you've been asking yourself, "Should I start learning Python for AI development?"—Kaggle competitions offer a resounding 'Yes!'. It's a wonderful platform to apply what you've learned and gain real-world experience in Python and AI.

Learn Python libraries for AI

When it comes to AI development, Python reigns supreme, and for good reason. Its vast selection of libraries specifically designed for AI and machine learning make it the go-to choice for many developers. So yes, if you're still wondering, "Should I start learning Python for AI development?"—the answer is a resounding yes.

A brief look at Python libraries for AI

There are several Python libraries that have been specifically designed for AI and machine learning. They make it easier to implement complex algorithms without having to code everything from scratch. Here are a few:

TensorFlow: Developed by Google, TensorFlow is one of the most popular libraries for machine learning and AI. Its flexible architecture allows you to deploy computation on one or more CPUs or GPUs, making it a good choice for both research and production.
Keras: Built on top of TensorFlow, Keras is a user-friendly neural network library. It's designed to enable fast experimentation with deep neural networks.
PyTorch: Developed by Facebook's AI Research lab, PyTorch is a library for machine learning that offers strong GPU acceleration. It's known for its flexibility and efficiency.

How to go about learning these libraries?

Learning these libraries might seem like a big task, but don't worry—there are ways to make it manageable:

Pick one library to start with: Trying to learn all the libraries at once could be overwhelming. Start with one—TensorFlow or Keras is a good choice for beginners—and get comfortable with it before moving on to the others.
Use online resources: There are plenty of online tutorials and courses that can help you learn these libraries. Some of these resources are even provided by the companies that developed the libraries, so they're high quality and up to date.
Practice, practice, practice: The best way to learn is by doing. Once you've got the basics down, find a project or a problem to solve. This will help you understand how the library functions in a real-world scenario.

In summary, Python's extensive range of AI libraries makes it an excellent choice for AI development. So, the next time someone asks, "Should I start learning Python for AI development?"—you know what to tell them!

If you're excited to dive deeper into AI development after reading this blog post, we recommend checking out Ansh Mehra's workshop, 'Midjourney AI: Beginners Crash Course.' This workshop is designed to help beginners navigate the world of AI development, providing essential tips and insights to build a strong foundation in Python and AI.