Learn Artificial Intelligence: A Complete Roadmap
If you're driven by the desire to explore the vast field of artificial intelligence (AI), you've come to the right place. This article provides you with a comprehensive roadmap, drawn from ten years' experience as a freelance data scientist. Whether you're a novice or looking to hone your skills, this roadmap is intended as a compass for navigating the ever-changing world of AI.
Why Learn AI?
The field of artificial intelligence is developing rapidly. There are more and more AI applications in everyday life. There is great job potential in this field. Most companies need expertise in artificial intelligence to maximize their production.
AI is a booming sector, with an estimated market prospect of $20,000 billion by 2030. This potential makes it one of the most promising opportunities to explore. The AI revolution is still in its infancy, facilitated by the growing availability of pre-trained models from open AI.
Code vs. low/no-code approach
Before diving into the world of AI, it's essential to determine your preference between a code-centric approach or a low/no-code approach. Both paths present opportunities, but it's crucial to understand the pros and cons of each.
The approach suggested here is distinguished by its practical orientation. It avoids the extremes often encountered online, between superficial discussions of low/no-code tools and deep dives into the complex mathematics of AI. Rather than focusing on these two extremes, the emphasis is on learning by doing, reverse-engineering existing experiments, applying them, and filling identified gaps.
The Seven-Step Technical Roadmap
Project design is one of the first stages in the life cycle of an initiative, and involves planning ideas, processes, resources, and expected results in seven simple steps.
1. Configure Your Environment: Set up a working environment adapted to your needs, using AI-relevant tools.
2. Learn Python and Key Libraries: Python is the language of choice in
AI. Developers can quickly import and use libraries, essentially collections of
modules with pre-written code, to create any functionality. Python addresses
the need for regressive and continuous data processing in artificial
intelligence by enabling data access, manipulation, and transformation.
Familiarize yourself with its basics and explore the key libraries:
- Using SciPy and NumPy in scientific computing;
- For data exploration, use BeautifulSoup and Scrapy ;
- For data exploration and visualization, use Pandas, Matplotlib,
Seaborn and Plotly;
- Use Scikit-learn, PyCaret, Keras, PyTorch, and TensorFlow for machine
learning.
3. Learn the basics of Git and GitHub: Version management is crucial in
software development. Understand the basics of Git and use GitHub to
collaborate.
What is Git?
Linux founder Linus Torvalds created Git versioning software in 2005.
Versioning software allows you to keep a record of changes made to a project so that in the event of a problem, you can easily identify the changes made and
revert to a previous version.
These days, versioning software is almost indispensable, making
project management much easier and teamwork much more productive.
Git is the undisputed leader in version control software, so any developer must know how to use it.
A repository is a term used to describe the centralized copy of all
project files and their versions in version control systems. Git also refers to
this as a "repository" or simply a "repository".
GitHub is an Internet platform that lets you host repositories, including Git repositories. It is the world's largest host of Git repositories.
Many repositories on GitHub are open source, allowing anyone to download code and suggest new features to further develop the repositories.
4. Work on Projects and Build a Portfolio: The best way to learn is to practice. Get involved in real-life projects and build a portfolio to showcase your skills.
5. Explore Specific Areas of AI: Immerse yourself in specific areas such as machine learning, natural language processing, or computer vision.
6. Network with Other AI Professionals: Build relationships with other AI enthusiasts and professionals. Exchanges and collaborations can be enriching.
7. Keep abreast of the latest AI trends: AI is evolving rapidly. Keep abreast of the latest advances by following blogs, conferences, and publications.
Resources for each step
Each step of this roadmap should be accompanied by specific resources to help you move forward. These resources include online tutorials, courses, and practical tools.
It's impossible to complete a project without resources. The planning, availability, and optimization of resources are considered essential to the success of any project. Therefore, you must carefully seek out all the resources you need to complete your project to avoid running out, which could seriously affect the project's success.
Conclusion
This roadmap is a starting point, and there is no single method for learning AI. The key is to find an approach that suits you, and to commit to it with perseverance. For those hesitating to become coders, start with low/no-code tools.
A few additional tips: explore the many online resources dedicated to AI, feel free to experiment with different approaches, and remember that there's no substitute for hard work and dedication.
In the end, this roadmap offers a practical guide for those aspiring to plunge into the exciting world of AI, whether to develop applications or become experts in data science and machine learning.