20 FREE Courses for getting started with Generative AI, LLMs, and More.

1. Introduction to Generative AI (Google)

What You'll Learn:

- Basics of Generative AI

- Applications and real-world use cases

- Differences between Generative AI and traditional ML methods

Why It Matters:

Understanding Generative AI is crucial for grasping how AI can create new content, from images to text, and its potential to innovate across industries.

2. Introduction to Image Generation (Google)

What You'll Learn:

- Techniques for training image generation models

- Deployment strategies for these models

- Hands-on projects to apply your knowledge

Why It Matters:

Image generation is a key application of AI, with uses in design, entertainment, and more. Mastering this skill opens doors to creative and technical opportunities.

3. Introduction to Large Language Models (LLMs) (Google)

What You'll Learn:

- Overview of LLMs

- How LLMs work and their various functions

- Applications and impact of LLMs in different fields

Why It Matters:

LLMs are at the forefront of natural language processing, powering everything from chatbots to content generation. This course provides the foundation needed to work with these powerful models.

4. Encoder-Decoder Architecture (Google)

What You'll Learn:

- Detailed workings of the encoder-decoder framework

- Its role in AI applications like translation and summarization

- Practical implementations of this architecture

Why It Matters:

Understanding the encoder-decoder architecture is essential for developing models that handle complex tasks involving sequential data.

5. Transformer and BERT Models (Google)

What You'll Learn:

- The architecture of Transformers and BERT

- Their impact on natural language understanding and generation

- How to apply these models to real-world problems

Why It Matters:

Transformers and BERT are foundational to modern NLP technologies, making this knowledge crucial for anyone working with AI language models.

6. Attention Mechanism (Google)

What You'll Learn:

- How attention mechanisms work

- Their advantages in improving model performance

- Applications in tasks like machine translation and summarization

Why It Matters:

The attention mechanism is a key innovation that improves the effectiveness of models by allowing them to focus on relevant parts of the data.

7. Generative AI Explained (Nvidia)

What You'll Learn:

- How Generative AI functions

- Its various applications and potential

- Challenges and opportunities in the field

Why It Matters:

Generative AI is transforming how we create and interact with digital content, making this course essential for understanding its broad implications.

8. Augment Your LLM Using Retrieval Augmented Generation (Nvidia)

What You'll Learn:

- Basics of RAG and its retrieval process

- Components and applications of RAG

- How to integrate RAG with LLMs for improved performance

Why It Matters:

RAG improves the efficiency and effectiveness of LLMs by combining retrieval and generation, making it a valuable technique for advanced AI applications.

9. Artificial Intelligence for Beginners (Microsoft)

What You'll Learn:

-Explore AI approaches, from symbolic reasoning (GOFAI) to deep learning

-Build and understand neural networks using TensorFlow and PyTorch

-Discover AI techniques beyond deep learning, including Genetic Algorithms and Multi-Agent Systems

Why It Matters:

This course provides a comprehensive foundation in AI, equipping you with the skills to understand and build intelligent systems using modern frameworks.

10. Accelerating Data Science Workflows (Nvidia)

What You'll Learn:

- Advantages of unified CPU and GPU workflows

- Techniques for GPU-accelerated data processing

- How to achieve faster processing times

Why It Matters:

This course helps streamline data science workflows, making it easier to leverage GPU power for faster and more efficient data processing.

11. Mastering Recommender Systems (Nvidia)

What You'll Learn:

- Techniques for building recommendation systems

- Two-stage models, candidate generation, feature engineering, and ensembling

- Best practices and strategies for improving recommendations

Why It Matters:

Recommendation systems are vital for personalizing user experiences in e-commerce. Mastering these techniques can enhance user engagement and drive business success.

12. Building RAG Agents with LLMs (Nvidia)

What You'll Learn:

- Scalable deployment strategies for LLMs

- LangChain paradigms for dialog management

- Advanced models and production steps for building RAG agents

Why It Matters:

This course equips you with the knowledge to deploy LLMs effectively, bridging the gap between advanced AI models and practical applications.

13. Vector Search and Embeddings (Google)

What You'll Learn:

-Embeddings and vector search fundamentals

-Integration with Google Vertex AI

-Real-world applications of vector-based search

Why It Matters:

This course bridges advanced AI techniques with practical search applications, enabling you to create intelligent systems for recommendation engines, personalized search, and beyond.

14. Inspect Rich Documents with Gemini Multimodality and Multimodal RAG

What You'll Learn:

Basics of multimodal AI and data processing

Using Gemini tools for multimodal insights

Video description generation and multimedia retrieval

Why It Matters:

In a multimedia-driven world, understanding and leveraging multimodal data is crucial. This course equips you to innovate in areas like video analysis, multimedia insights, and more.

15. Introduction to AI with Python (Harvard)

What You'll Learn:

1. Understand core AI concepts, including search algorithms, optimization, and machine learning
2. Build AI models using Python and popular libraries like TensorFlow and PyTorch
3. Apply AI to real-world problems, including natural language processing and game playing

Why It Matters:

Mastering AI fundamentals with Python prepares you to build intelligent applications and advance in the rapidly growing field of artificial intelligence.

16. LLMOps: Deploy & Manage Large Language Models

What You'll Learn:

1. Understand the full lifecycle of deploying and maintaining large language models
2. Implement best practices for scaling, monitoring, and optimizing LLMs in production
3. Explore real-world case studies on operationalizing AI effectively

Why It Matters:

Managing LLMs efficiently ensures AI applications remain scalable, cost-effective, and high-performing in real-world use cases.

17. Fundamentals of Deep Learning (Nvidia)

What You'll Learn:

-Basics of neural networks and activation functions

-Training models for image and text tasks

-Working with pre-trained models for practical applications

Why It Matters:

Deep learning powers modern AI systems, from virtual assistants to autonomous vehicles. This course builds your expertise to apply deep learning effectively in various domains.

18. Accelerating Data Engineering Pipelines

What You'll Learn:

-GPU-accelerated data processing techniques

-Designing efficient data pipelines

-Practical applications of GPU acceleration in data engineering

Why It Matters:

As data volumes grow, traditional processing methods fall short. This course equips you to streamline workflows, enabling faster insights and AI-readiness.

19. 5-Day Generative AI Intensive Course (Kaggle)

What You'll Learn:

-Fundamentals of generative AI and model architectures

-Techniques for generating creative content

-Hands-on projects using TensorFlow, PyTorch, and APIs

Why It Matters:

Generative AI is transforming industries with scalable creativity. This course prepares you to lead in one of the fastest-growing areas of AI innovation.

20. AI Applications & Prompt Engineering (edX)

What You'll Learn:

1. Craft effective AI prompts to enhance model performance
2. Develop AI-driven applications with real-world use cases
3. Optimize AI interactions for better accuracy and efficiency

Why It Matters:

AI is reshaping industries, and mastering prompt engineering gives you a competitive edge in automation, decision-making, and content generation.

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First, go to the course you want to take and click 'Enroll for free', then 'Audit the course'.

Note: You'll need to create an account to take courses, but won't need to pay anything.