How To Learn Generative AI [and become an expert]

Is Generative AI Difficult to Learn?

Generative AI is a type of AI that can generate human-like content in response to a given prompt. It helps us write fast, generate realistic images, create quality music, do faster research, translate languages, provide 24/7 customer service, and whatnot. (Read this post to learn more about generative AI applications.)

When you start to learn GenAI, you may find topics like neural networks, machine learning algorithms, or large language models somewhat difficult. But here’s the thing: like any new skill, it starts out challenging but becomes easier as you give it some time. 

The good thing is you don’t have to be a coding expert to begin. You can simply start from the basics, build your foundation, and keep learning advanced concepts. Within 4-5 months, you can be an expert. You may need the right resources and mentorship to do it easily and quickly. But as you stay consistent and curious, you will find generative AI isn’t as difficult as it seems.

How to Learn Generative AI from Scratch?

Here are the most important topics you should study to master this technology. If you want more detailed topics, you can check the comprehensive generative AI syllabus.

1. Learn Programming Basics

Yes, you need to know programming. In fact, you will need a strong foundation in programming, especially in Python. So, start with the basic topics like Python data types, control flow, loops, and functions, which are essential for writing AI code. Understanding important libraries such as Pandas and Numpy for data manipulation will also help you handle data more effectively. You can watch the Python tutorial by Ivy Professional School on YouTube to develop your Python skills.

2. Get an Introduction to Generative AI

Once you know the programming basics, the next step is to understand what generative AI is and what its applications are. You also need to learn about different generative models and explore OpenAI APIs, like GPT and DALL-E. At this point, you can try to generate text with OpenAI API using Python functions.

3. Study the Machine Learning Fundamentals

You can’t learn generative AI without understanding the basics of machine learning. You will need to learn models like decision trees, linear models, and k-nearest neighbors (k-NN). You should study how classification and regression models work and learn to train them on different datasets. You can also learn about deep learning and understand architectures like Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), etc.

4. Explore Text and Image Generation

Text and image generation are two important topics in generative AI. Learn how models like GPT and tools like DALL-E generate creative text and images, respectively. You can try building apps like an AI Chatbot using Python, deploy it on a web platform, and integrate it with OpenAI’s API for real-time interactions. You can also build social media automation tools by implementing LangChain components and deploying them on a cloud platform. Next, you can learn how to build an image generation app integrating the DALL-E API. 

5. Learn about Voice Recognition and Generation

Voice-based AI is another important area to study. Voice recognition systems analyze and process human speech, while voice generation systems like Whisper API produce natural-sounding speech. You should learn the basics of how these systems work, from feature extraction to text-to-speech algorithms. You can also integrate the Whisper API to create applications like voice assistants that can listen and speak just like humans. You should also know how to deploy the app on cloud platforms and test, monitor, and optimize its performance.

6. Master Multimodal GenAI

Multimodal generative AI has the capability to deal with different types of data, like text, images, audio, and video. To study this advanced topic, you should start with techniques like Early Fusion, Late Fusion, and Hybrid Fusion, which merge data from multiple sources to improve AI performance. You will also need to learn about progressive GANs, StyleGAN, and vision-and-language transformers (VLT) to handle complex multimodal tasks. 

7. Practice Hands-On Projects

The best way to learn generative AI is to work on projects and get practical experience. You can implement the theoretical knowledge to build interesting generative AI apps like chatbots, content generation tools, voice assistants, etc. You can use APIs like OpenAI or DALL-E for tasks like text generation, image creation, or voice synthesis. The more projects you will work on, the more confident you will be. This will also build a solid portfolio to showcase your skills and expertise to employers and get a job at top MNCs.

Now, you can watch this one-hour video where I explain in detail how you can make a career in generative AI: