How to Learn Generative AI from Scratch in 2024?

This blog is your key to unlock the path to the buzzing domain of Generative AI. You will learn about all the skills you need to hone to learn Genrative AI from scratch and add it to your bag of professional skills through certifications from companies like Google and Microsoft. So, dive in!

Generative AI has witnessed a recent surge in its popularity among the AI enthusiasts. Its ascent is not just marked by innovation but by statistics that highlight its exponential rise in the technological realm as mentioned by Navveen Balani on LinkedIn.

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The global Generative AI market as per Market.us, currently valued at an impressive $13.71 billion, is projected to surge to an estimated $22.12 billion by 2025, further catapulting to an astounding $110.8 billion by 2030. North America commands a lion's share, owning 41% of the Generative AI market, while usage statistics across generations showcase an intriguing trend. Salesforce has revealed that 29% of Gen Z, 28% of Gen X, and 27% of Millennials are actively utilizing Generative AI tools in their professional environments, with an overwhelming 65% of users falling within the Millennial or Gen Z demographic. Remarkably, the enthusiasm to learn Generative AI is not merely confined to its adoption but also to the users' confidence in mastering its complexities. Nearly 6 in 10 users express their belief in progressing towards mastering this transformative technology. What's more intriguing is the purpose behind its adoption: a staggering 75% of Generative AI users are leveraging its capabilities to automate tasks at work, harnessing its potential to streamline processes and enhance productivity. Moreover, this technology is gradually becoming a cornerstone of decision-making, with 52% of Gen Z users placing trust in Generative AI to aid them in crucial choices.

As the demand and utilization surge, more than two-thirds of respondents affirm their companies' intentions to leverage Generative AI, with a significant portion already having it on their organizational agenda. This suggests that following a Generative AI learning roadmap and adding it to your skillset is likely to prove beneficial in the future. So, if you have made up your mind and are on the lookout for a comprehensive resource that lists all you need to know to learn about Generative AI and it in your resume skill set, read this blog until the end as we take you on a journey from a beginner to an expert in Generative AI.

Why learn Generative AI from scratch?

Learning about Generative AI from the ground level offers various compelling reasons beyond acquiring a new skill. This knowledge empowers informed decision-making, whether implementing it within an organization or shaping one's career path. Understanding these basics will serve as a guiding light in navigating the vast landscape of artificial intelligence. Here's a list of few more reasons to help you affirm how delving into Generative AI proves to be exceptionally rewarding:

• Learning from scratch builds foundational mathematical skills, enabling a deeper dive into algorithms. This understanding allows for comprehensive learning about how algorithms operate and perform. These skills form the bedrock for comprehending intricate algorithms like GANs, autoencoders, and probabilistic models, crucial for effective Generative AI model development.

• Starting from scratch emphasizes grasping the fundamentals of a programming language progressively. This step-by-step learning approach cultivates a solid understanding of implementing diverse algorithms. It provides insights into the functioning of generative models facilitating efficient and optimized model building.

• When you start from scratch, you choose how fast or slow you want to learn. This means you can take your time to understand each part before moving ahead. You're in control, learning at a pace that suits you best.

• The landscape of Generative AI is in perpetual motion, marked by continuous advancements. Starting from scratch equips learners with the agility to keep pace with these innovations. It ensures readiness to adapt to the latest developments, staying ahead of the curve and remaining relevant in an ever-evolving technological sphere.

• Here is another exciting reason to learn Generative AI from scratch by Carlos Hernandez Vaquero.

Thus, the journey of learning Generative AI from scratch not only facilitates a profound understanding of the technology but also nurtures practical skills, opening doors to new career prospects, leading to personal growth, and enabling informed decision-making in leveraging Generative AI's potential. After having explored the reasons to invest their time and effort into comprehensively learning Generative AI, it is time to unlock the Generative AI learning roadmap.

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Learning Generative AI Roadmap 2024

Mastering Generative AI from scratch involves a systematic and iterative process. Each step builds upon the other, promoting an overall understanding and practical expertise in this dynamic field. The following step-by-step guide will pave the way for a comprehensive understanding and proficiency in Generative AI:

Step 1: Understanding the Basics of Machine Learning

To start your Generative AI journey, it’s imperative to establish a strong foundation in machine learning. Begin by comprehending fundamental concepts and techniques that machine learning algorithms are built on. Explore supervised and unsupervised learning, delve into regression and classification models, and grasp the nuances of training and testing data sets. Resources like online courses, textbooks, and interactive projects on machine learning can aid in this foundational understanding.

Step 2: Mastering Python Programming

Python is one the most popular languages among the members of the AI and machine learning community. Acquiring proficiency in Python programming is crucial for implementing machine learning models and diving into Generative AI. So, familiarize yourself with Python's syntax, data structures, libraries (such as NumPy, Pandas, and TensorFlow), and their applications in machine learning. Interactive coding platforms and project-based learning can accelerate the mastery of Python for AI applications.

Step 3: Exploring Data Science and Deep Learning

Next, immerse yourself in the realms of data science and deep learning. Gain insights into data preprocessing, feature engineering, and visualization techniques essential for handling and understanding complex datasets. Delve deeper into neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other deep learning architectures like Transformers, GNNs, etc. Understand their applications in image generation, natural language processing, and sequential data analysis—a crucial precursor to understanding Generative AI models.

Step 4: Introduction to Generative AI

Now that the groundwork is laid, acquaint yourself with the foundational concepts of Generative AI. Explore its diverse applications, including image generation, text-to-image synthesis, style transfer, and more. Grasp the underlying principles of Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and other prominent Generative AI models. Dive into research papers, online lectures, and specialized courses to gain a comprehensive understanding of Generative AI’s capabilities and implications.

Step 5: Engage in Hands-on Projects and Challenges

Theory without practical application can only go so far. Apply your knowledge by engaging in hands-on projects and challenges. Create Generative AI models to generate art, synthesize images, or produce music. Experiment with existing datasets and tweak models to understand their behavior and limitations. Platforms like GitHub, ProjectPro, Kaggle, and open-source repositories offer a wealth of datasets and project ideas to implement and showcase your skills.

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Step 6: Networking and Collaborating with the Generative AI Community

An often overlooked yet invaluable aspect of learning Generative AI is community engagement. Join online forums, participate in discussion groups, and actively engage with the Generative AI community. Seek guidance from peers, share insights, and collaborate on projects. Community engagement not only enhances learning through collective knowledge but also keeps you updated on the latest trends, techniques, and advancements in Generative AI.

By diligently following this roadmap, from grasping foundational concepts to engaging in hands-on projects and community collaboration, you'll position yourself to navigate the complexities of Generative AI and unlock its creative potentials in diverse applications. The next step in learning about Generative AI from scratch has to be aiming for a certification that allows you to showcase it on your resume. So, proceed to the next sections for a few freely available resources and popular certifications that will uplift the skill section in your resume.

Top Generative AI Courses

We will now discuss a list of courses that you can study in case you want to learn Generative AI in a more guided manner or want to quickly recap to what you have learned so far on your own.

1. Generative AI Learning Path by Google

Google Cloud's Introduction to Generative AI Learning Path is a free introductory course encompassing fundamental concepts, Large Language Models (LLMs), and responsible AI. Subscribers gain access to live training sessions and practical labs.

2. LinkedIn

LinkedIn Learning, presents a diverse array of Generative AI courses, each targeting distinct aspects of the field. Courses such as "Generative AI: Working with Large Language Models" delve into essential facets of large language models, while "GPT-4 Foundations" focuses on constructing AI-powered apps using GPT-4 technology. Other courses cover topics like conversational AI fundamentals, prompt engineering, and cloud computing concepts in Generative AI. 

3. Learn Prompting

Learn Prompting is an open-source curriculum guiding users through ChatGPT and related tools, offering over 60 content modules catering to different skill levels. It also sponsors a prompt hacking competition to enhance AI safety and education.

4. Towards AI

Towards AI is a platform with over 2,000 contributing writers and 270,000 followers, aiming to democratize AI education. It offers accessible news, tutorials, newsletters, and articles on trending AI topics, with free access to most content.

These courses, available across various platforms, cater to different skill levels and preferences, offering a diverse range of learning experiences in Generative AI, from introductory insights to specialized practical applications.

5. Class Central

Class Central offers over 2,700 free courses and 1,900 paid courses in Generative AI, covering 

tools like ChatGPT, Midjourney, and Stable Diffusion. It's an ideal starting point for understanding AI capabilities within enterprises and offers longer programs for a comprehensive grasp of Generative AI..

Generative AI Learning Path Google Certification

Google, as a frontrunner in technology, provides professionals with unparalleled learning opportunities. On Google Cloud Skills Boost, two robust learning paths in AI are offered: "Introduction to Generative AI" and "Generative AI for Developers." 

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Introduction to Generative AI Path

This Generative AI learning path by Google comprises introductory, non-technical courses suited for sales, marketing, HR, and operations roles.

• Introduction to Generative AI: An introductory microlearning course explaining Generative AI, its applications, and differences from traditional ML methods.

• Introduction to Large Language Models: Covers large language models (LLM) exploration and prompt tuning for enhanced LLM performance.

• Introduction to Responsible AI: Focuses on explaining responsible AI, its significance, and Google's implementation of responsible AI principles.

• Generative AI Fundamentals: Awards a skill badge upon completion of introductory courses, validating foundational knowledge in generative AI.

• Responsible AI: Applying AI Principles with Google Cloud: Emphasizes the importance of responsible AI practices within enterprise AI and ML domains.

Generative AI for Developers Path

This Generative AI learning path by Google caters to software developers and engineers, integrating technical hands-on labs and courses.

• Introduction to Image Generation: Introduces diffusion models for image generation, drawing inspiration from physics-based thermodynamics.

• Attention Mechanism: Explores the attention mechanism's role in neural networks for focused input sequence processing.

• Encoder-Decoder Architecture: Synopsis on the encoder-decoder architecture for sequence-to-sequence tasks like translation and summarization.

• Transformer Models and BERT Model: Introduction to Transformer architecture and Bidirectional Encoder Representations from Transformers (BERT) model.

• Create Image Captioning Models: Teaches creation and training of image captioning models using deep learning.

• Introduction to Generative AI Studio: Explores Generative AI Studio on Vertex AI for prototyping and customizing generative AI models.

• Generative AI Explorer - Vertex AI: Collection of labs illustrating Generative AI's use on Google Cloud via the Vertex AI PaLM API family.

• Explore and Evaluate Models using Model Garden: Lab session exploring and evaluating AI models through Model Garden.

• Prompt Design using PaLM: Focuses on prompt design essentials, interactions with PaLM for desired responses, and best practices.

These comprehensive paths cater to diverse skill sets, offering a spectrum of courses for various roles and technical proficiencies, ensuring a well-rounded understanding of Generative AI.

Generative AI Learning Path Microsoft Certification

Microsoft stands as a pioneering force in the technology industry, offering a myriad of solutions through its Azure platform. Microsoft Azure provides a robust ecosystem for AI-related services and certifications. One such fundamental certification is the 'Exam AI-900: Microsoft Azure AI Fundamentals.' This exam evaluates foundational knowledge in Azure AI, covering diverse AI workloads and considerations, Azure AI services, machine learning concepts, and responsible AI practices within Azure.

Aligned with this certification, Microsoft offers a learning path specifically tailored to Generative AI within Azure. This learning path, 'Microsoft Azure AI Fundamentals: Generative AI,' is designed to provide an understanding of generative AI's core concepts and applications within Azure. The path comprises three modules, catering to various roles like AI Engineers, Developers, Solution Architects, and Students.

The modules within this learning path include:

1. Fundamentals of Generative AI: Explores the role of large language models (LLMs) in enabling AI applications to generate original content based on natural language input. It covers the Azure OpenAI Service and how generative AI facilitates the creation of AI-powered copilots.

2. Fundamentals of Azure OpenAI Service: Delve into the relationship between AI, Responsible AI, and text, code, and image generation. Understand the usage of Azure OpenAI to build solutions leveraging AI models within Azure.

3. Fundamentals of Responsible Generative AI: Highlights the importance of implementing generative AI responsibly to mitigate the risks associated with content generation, ensuring ethical AI practices.

These modules encompass diverse aspects of generative AI within Azure, preparing individuals for the Exam AI-900 by instilling a foundational understanding of generative AI's capabilities, applications, and responsible use. Prior familiarity with Azure and the Azure portal is recommended as a prerequisite for this learning path.

Learn Generative AI with ProjectPro!

Preparing for certifications independently can be a daunting and time-consuming task. The struggle to gather relevant resources and structure study materials often leads to frustration. Enter ProjectPro—your savior from all the unnecessary hassle. Our platform eradicates the hassle by offering a curated repository of solved projects in Data Science and Big Data. Delve into Generative AI through our tailored projects that encompass GANs, autoencoders, and probabilistic models. With detailed solutions presented in video excerpts by industry experts, understanding how Generative AI is implemented becomes easily accessible. ProjectPro simplifies your learning Generative AI roadmap by offering hands-on projects and comprehensive learning in Generative AI.

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FAQs

1) Who can learn generative AI?

Anyone with an interest in AI, regardless of background, can learn Generative AI. It's recently become popular among developers, data scientists, engineers, and enthusiasts keen on exploring creative AI technologies.

2) How to learn generative AI for Beginners?

Beginners can start with online courses, books, or tutorials focusing on AI fundamentals, Python programming, and specific Generative AI concepts. Hands-on projects and beginner-friendly courses help grasp the basics effectively.

3) Where to learn Generative AI?

Platforms like LinkedIn, TowardsAI, and Class Central offer courses on Generative AI. Additionally, Google Cloud and Microsoft provide learning paths. OpenAI's documentation and AI community forums are valuable resources for learning Generative AI techniques.

About the Author

Manika

Manika Nagpal is a versatile professional with a strong background in both Physics and Data Science. As a Senior Analyst at ProjectPro, she leverages her expertise in data science and writing to create engaging and insightful blogs that help businesses and individuals stay up-to-date with the

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