A Simple and Efficient Roadmap to Learn AI |

Ever since Chad GPT emerged, it has started quite a revolution worldwide because no one was expecting this level of advancement to happen in the AI industry. It's becoming very clear that AI will soon change many aspects of our lives, from how we consume goods and services to how we learn and communicate. But perhaps most importantly, it will reshape the way we work, especially in the tech industry.

While I believe that AI won't replace software engineers anytime soon, I think that learning AI fundamentals is recommended for anyone working in the field. Not only for personal growth and taking advantage of the new opportunities it represents but also to prepare for the shift in the job market needs in the years to come. So if you are looking for a simple and efficient roadmap to learn AI, this article is for you.

Step 1: Learn Programming with Python

The first step is to learn programming with Python. Python is widely used in AI development, and depending on how you like to learn, there is a huge number of resources out there that you can use. There are textbooks, for example, but this is honestly not my preferred option. What I recommend is choosing one online course and following it from beginning to end.

Here are a few great resources: * Khan Academy: Offers a great course on Python. * learnpython.org: This website offers a free, interactive way to learn Python. * freeCodeCamp: Provides a full course on YouTube that has more than 40 million views.

Don't try to go through all of them; following only one course would be sufficient. Keep in mind, we are not aiming for perfection here. You just need to learn enough that would help you to start writing programs.

Step 2: Build AI Products with APIs and Prompt Engineering

Now that you have a good knowledge of Python, you can actually start building AI products already. OpenAI, the company that created Chad GPT, offers powerful APIs that you can use for free to build applications on top of them. Basically, you can leverage Chad GPT, which is already a powerful tool, to build services like an AI assistant, automate sending emails to customers, or summarize articles, for example.

To do that, you would need to learn more about something called Prompt Engineering.

In prompt engineering, the goal is to build prompts in such a way that they guide a language model like Chad GPT to produce desired outputs or behaviors. It basically teaches you how to effectively communicate with Chad GPT so it does exactly what you want it to do. This involves the right choice of words, the right context provided, and any additional constraints or instructions you want to give to the model.

A great, 1-hour-long course that can help you learn this is by deeplearning.ai, called Chad GPT Prompt Engineering for Developers.

Step 3: Dive into Machine Learning

Now, at this stage, you know how to build simple AI products, and perhaps you are now interested in learning what happens under the hood. If that's the case, the next step would be to dive into machine learning.

Machine learning is basically teaching a computer to learn from examples and experiences, much like how we learn from practice ourselves. Instead of giving the computer specific instructions on what to do, we give it a lot of data and let it figure out patterns and make decisions on its own.

Machine learning is built on two pillars: Programming and Mathematics. We've talked about programming, especially in Python, so let's talk next about mathematics.

The Mathematics Foundation

For math, you need to have a basic understanding of: * Linear Algebra * Calculus * Probability and Statistics

Most of the basics you would have learned already in high school or college, so you don't need to know the details of every algorithm. You just need to know enough of the basics for understanding the big picture or the big concepts. Technically, you would be using libraries most of the time when implementing AI projects, so understanding just the basics would be fine.

If you think you remember enough of the concepts that you learned in high school or university, then just skip this step and go to the next one. But if not, here are some great courses: * Coursera: Mathematics for Machine Learning and Data Science * Stanford University: Introduction to Statistics

Core Machine Learning

Now that we have the basics covered, let's dive into what we're here for: machine learning. The best machine learning course out there is Machine Learning Specialization by Andrew Ng, which you can find on Coursera as well. The course is divided into three classes: 1. Supervised Machine Learning 2. Unsupervised Learning 3. Advanced Learning Algorithms

Supervised learning is like teaching with answers. You give the computer examples along with their correct answers, and with enough data, it learns to predict or classify new, similar data on its own. Like your email inbox, for example; it knows how to detect spam emails because it was taught how they look.

In unsupervised learning, on the other side, you give the computer data without explicit instructions, and it finds patterns or structures in the data on its own, like for example, grouping similar news articles together based on their content without you telling the computer what categories to look for.

Once you have a good understanding of the concepts, a good place to find project examples is Kaggle. And once you feel confident enough to build your own projects, you can check the competition section of the website. The biggest issue with machine learning is that you need to have access to a huge amount of data, and Kaggle is a great place for that.

Step 4: Deeper into Deep Learning

By now, you should have learned the foundations of machine learning. The next step is diving deeper into a subset of the field called Deep Learning.

In simple terms, deep learning is a way for computers to learn and understand things by looking for patterns in lots of pieces of data. Examples of its applications are: * Computer Vision: where computers learn to recognize objects in images, for example. * Natural Language Processing (NLP): which focuses on the interaction between computers and humans through natural language. It involves tasks such as text understanding, text generation, language translation, sentiment analysis, and more. The most famous example here that I guess you have recognized is Chad GPT.

The most important concept here is Large Language Models (LLMs). Two resources I would recommend you check out are: * An "Introduction to LLMs" article by Andrej Karpathy, who's an expert in the field. * For getting deeper into it, I recommend again the course by Andrew Ng that you can find on Coursera as well.