5 AI Frameworks You MUST Learn in 2026
44sThe list format is highly shareable and the promise of future-proof skills sparks curiosity and FOMO.
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This video provides a concise overview of the most important AI frameworks to learn in 2026, covering essential tools for machine learning, deep learning, and AI agent development. It outlines seven key frameworks, from scikit-learn for beginners to advanced tools like LangGraph and CrewAI for building AI agents, and emphasizes a structured learning path.
Chapters
[00:02]
Scikit-learn for ML Basics
Scikit-learn is essential for understanding the basics of machine classification and model training.
[00:16]
PyTorch for Deep Learning
PyTorch is a crucial deep learning framework, especially for working with neural networks and AI models.
[00:28]
TensorFlow and Keras
Keras is the high-level API of TensorFlow, offering a beginner-friendly way to build deep learning models.
[00:41]
Hugging Face Transformers
This framework is vital for working with LLMs and multimodal AI models, including text, vision, audio, and video.
[00:41]
LangChain and LangGraph
LangChain helps build AI applications, while LangGraph is for creating reliable AI agents with memory, tool use, and multi-step workflows.
[00:57]
LlamaIndex for RAG
LlamaIndex is essential for building RAG apps that connect AI with PDFs, APIs, SQL, and business data.
[01:14]
CrewAI and Agent SDKs
Explore CrewAI, semantic kernel, or OpenAI agent SDK for building AI agents, but don't try to learn everything at once.
The video recommends a structured learning path: start with machine learning basics, then deep learning, then LLMs, and finally Agentic AI. It encourages viewers to focus on one framework at a time and engage with the community.
Mentioned in this Video
Tutorial Checklist
1
00:02
Learn scikit-learn for machine learning basics, including classification and model training.
3
00:28
Learn TensorFlow and Keras for a beginner-friendly approach to building deep learning models.
7
01:14
Explore CrewAI or OpenAI agent SDK for building AI agents, but don't try to learn everything at once.
Study Flashcards (7)
What is the first framework to learn for machine learning basics?
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What is the first framework to learn for machine learning basics?
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scikit-learn
00:02
Which framework is described as one of the most important for deep learning, especially for neural networks?
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Which framework is described as one of the most important for deep learning, especially for neural networks?
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PyTorch
00:16
What is the high-level API of TensorFlow?
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What is the high-level API of TensorFlow?
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Keras
00:28
Which framework is essential for working with LLMs and multimodal AI models?
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Which framework is essential for working with LLMs and multimodal AI models?
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Hugging Face Transformers
00:41
What is the difference between LangChain and LangGraph?
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What is the difference between LangChain and LangGraph?
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LangChain helps build AI applications, while LangGraph is for building reliable AI agents with memory, tool use, and multi-step workflows.
00:41
Which framework is a must-know for building RAG apps that connect AI with PDFs, APIs, and SQL?
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Which framework is a must-know for building RAG apps that connect AI with PDFs, APIs, and SQL?
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LlamaIndex
00:57
What is the recommended learning path for AI frameworks?
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What is the recommended learning path for AI frameworks?
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Start with machine learning basics, then deep learning, then LLMs, and finally Agentic AI.
01:14
💡 Key Takeaways
🔧
Scikit-learn as Foundation
Establishes the core ML framework that underpins all subsequent learning.
00:02
📊
PyTorch's Dominance
Highlights PyTorch's critical role in modern deep learning and AI model development.
00:16
💡
Hugging Face for Multimodal
Emphasizes the importance of Hugging Face for handling diverse AI data types.
00:41
🔧
LlamaIndex for RAG
Shows the practical application of RAG in connecting AI to business data.
00:57
⚖️
Structured Learning Path
Provides a clear, sequential approach to mastering AI frameworks, avoiding overwhelm.
01:14Full Transcript
[00:02] 2026, then [music] these are the frameworks you should know. First is scikit-learn. This is where you're going to understand the basics of machine classification, and model training. Second one is learn PyTorch. PyTorch is
[00:16] one of the most important [music] deep learning frameworks, especially if you want to work with neural networks and AI models. [music] Third is learn TensorFlow and Keras. Keras is the high-level API of TensorFlow, [music]
[00:28] and it is useful when you want beginner-friendly way to build deep learning model. Fourth is Hugging Face Transformers. [music] And this is very important for working with LLMs, text, visions, audio, video, [music] and
[00:41] multimodal AI models. Fifth is LangChain and LangGraph. LangChain helps you build AI applications, while LangGraph is useful for building reliable AI agents with memory, tool use, and multi-step workflows. Sixth is learn LlamaIndex. If
[00:57] you want to build RAG apps that connect AI with PDF, APIs, SQL, and business data, then LlamaIndex is a must-know framework. And finally, explore CrewAI, which is a semantic kernel or OpenAI agent SDK if you want to build AI agent
[01:14] don't try to learn everything at the first go. Start with machine learning basics, then go into deep learning, then learn about LLMs, and then explore Agentic AI. So, guys, these are the smartest AI
[01:27] framework for 2026. Let us know in the comment section below Let us know in the comment section below which one are you learning right now.
