Roadmap to Building AI Agents
1. Master Python Programming – Build a solid foundation in Python, the primary language for AI development.
2. Understand RESTful APIs – Learn how to send and receive data via APIs, a crucial part of building interactive agents.
3. Dive into Large Language Models (LLMs) – Get a grip on how LLMs work and how they power intelligent behavior.
4. Get Hands-On with the OpenAI API – Familiarize yourself with GPT models and tools like function calling and assistants.
5. Explore Vector Databases – Understand how to store and search high-dimensional data efficiently.
6. Work with Embeddings – Learn how to generate and query embeddings for context-aware responses.
7. Implement Caching and Persistent Memory – Use databases to maintain memory across interactions.
8. Build APIs with Flask or FastAPI – Serve your agents as web services using these Python frameworks.
9. Learn Prompt Engineering – Master techniques to guide and control LLM responses.
10. Study Retrieval-Augmented Generation (RAG) – Learn how to combine external knowledge with LLMs.
11. Explore Agentic Frameworks – Use tools like LangChain and LangGraph to structure your agents.
12. Integrate External Tools – Learn to connect agents to real-world tools and APIs (like using MCP).
13. Deploy with Docker – Containerize your agents for consistent and scalable deployment.
14. Control Agent Behavior – Learn how to set limits and boundaries to ensure reliable outputs.
15. Implement Safety and Guardrails – Build in mechanisms to ensure ethical and safe agent behavior.
React ❤️ for more
1. Master Python Programming – Build a solid foundation in Python, the primary language for AI development.
2. Understand RESTful APIs – Learn how to send and receive data via APIs, a crucial part of building interactive agents.
3. Dive into Large Language Models (LLMs) – Get a grip on how LLMs work and how they power intelligent behavior.
4. Get Hands-On with the OpenAI API – Familiarize yourself with GPT models and tools like function calling and assistants.
5. Explore Vector Databases – Understand how to store and search high-dimensional data efficiently.
6. Work with Embeddings – Learn how to generate and query embeddings for context-aware responses.
7. Implement Caching and Persistent Memory – Use databases to maintain memory across interactions.
8. Build APIs with Flask or FastAPI – Serve your agents as web services using these Python frameworks.
9. Learn Prompt Engineering – Master techniques to guide and control LLM responses.
10. Study Retrieval-Augmented Generation (RAG) – Learn how to combine external knowledge with LLMs.
11. Explore Agentic Frameworks – Use tools like LangChain and LangGraph to structure your agents.
12. Integrate External Tools – Learn to connect agents to real-world tools and APIs (like using MCP).
13. Deploy with Docker – Containerize your agents for consistent and scalable deployment.
14. Control Agent Behavior – Learn how to set limits and boundaries to ensure reliable outputs.
15. Implement Safety and Guardrails – Build in mechanisms to ensure ethical and safe agent behavior.
React ❤️ for more
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LLM Cheatsheet
Introduction to LLMs
- LLMs (Large Language Models) are AI systems that generate text by predicting the next word.
- Prompts are the instructions or text you give to an LLM.
- Personas allow LLMs to take on specific roles or tones.
- Learning types:
- Zero-shot (no examples given)
- One-shot (one example)
- Few-shot (a few examples)
Transformers
- The core architecture behind LLMs, using self-attention to process input sequences.
- Encoder: Understands input.
- Decoder: Generates output.
- Embeddings: Converts words into vectors.
Types of LLMs
- Encoder-only: Great for understanding (like BERT).
- Decoder-only: Best for generating text (like GPT).
- Encoder-decoder: Useful for tasks like translation and summarization (like T5).
Configuration Settings
- Decoding strategies:
- Greedy: Always picks the most likely next word.
- Beam search: Considers multiple possible sequences.
- Random sampling: Adds creativity by picking among top choices.
- Temperature: Controls randomness (higher value = more creative output).
- Top-k and Top-p: Restrict choices to the most likely words.
LLM Instruction Fine-Tuning & Evaluation
- Instruction fine-tuning: Trains LLMs to follow specific instructions.
- Task-specific fine-tuning: Focuses on a single task.
- Multi-task fine-tuning: Trains on multiple tasks for broader skills.
Model Evaluation
- Evaluating LLMs is hard-metrics like BLEU and ROUGE are common, but human judgment is often needed.
Join our WhatsApp Channel: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
Introduction to LLMs
- LLMs (Large Language Models) are AI systems that generate text by predicting the next word.
- Prompts are the instructions or text you give to an LLM.
- Personas allow LLMs to take on specific roles or tones.
- Learning types:
- Zero-shot (no examples given)
- One-shot (one example)
- Few-shot (a few examples)
Transformers
- The core architecture behind LLMs, using self-attention to process input sequences.
- Encoder: Understands input.
- Decoder: Generates output.
- Embeddings: Converts words into vectors.
Types of LLMs
- Encoder-only: Great for understanding (like BERT).
- Decoder-only: Best for generating text (like GPT).
- Encoder-decoder: Useful for tasks like translation and summarization (like T5).
Configuration Settings
- Decoding strategies:
- Greedy: Always picks the most likely next word.
- Beam search: Considers multiple possible sequences.
- Random sampling: Adds creativity by picking among top choices.
- Temperature: Controls randomness (higher value = more creative output).
- Top-k and Top-p: Restrict choices to the most likely words.
LLM Instruction Fine-Tuning & Evaluation
- Instruction fine-tuning: Trains LLMs to follow specific instructions.
- Task-specific fine-tuning: Focuses on a single task.
- Multi-task fine-tuning: Trains on multiple tasks for broader skills.
Model Evaluation
- Evaluating LLMs is hard-metrics like BLEU and ROUGE are common, but human judgment is often needed.
Join our WhatsApp Channel: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
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9 advanced coding project ideas to level up your skills:
🛒 E-commerce Website — manage products, cart, payments
🧠 AI Chatbot — integrate NLP and machine learning
🗃️ File Organizer — automate file sorting using noscripts
📊 Data Dashboard — build interactive charts with real-time data
📚 Blog Platform — full-stack project with user authentication
📍 Location Tracker App — use maps and geolocation APIs
🏦 Budgeting App — analyze income/expenses and generate reports
📝 Markdown Editor — real-time preview and formatting
🔍 Job Tracker — store, filter, and search job applications
Coding Projects: https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
ENJOY LEARNING 👍👍
🛒 E-commerce Website — manage products, cart, payments
🧠 AI Chatbot — integrate NLP and machine learning
🗃️ File Organizer — automate file sorting using noscripts
📊 Data Dashboard — build interactive charts with real-time data
📚 Blog Platform — full-stack project with user authentication
📍 Location Tracker App — use maps and geolocation APIs
🏦 Budgeting App — analyze income/expenses and generate reports
📝 Markdown Editor — real-time preview and formatting
🔍 Job Tracker — store, filter, and search job applications
Coding Projects: https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
ENJOY LEARNING 👍👍
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🖥 Large Language Model Course
The popular free LLM course has just been updated.
This is a step-by-step guide with useful resources and notebooks for both beginners and those who already have an ml-base.
The course is divided into 3 parts:
1️⃣ LLM Fundamentals : The block provides fundamental knowledge of mathematics, Python and neural networks.
2️⃣ LLM Scientist : This block focuses on the internal structure of LLMs and their creation using the latest technologies and frameworks.
3️⃣ The LLM Engineer : Here you will learn how to write applications in a hands-on way and how to deploy them.
⭐️ 41.4k stars on Github
📌 https://github.com/mlabonne/llm-course
#llm #course #opensource #ml
The popular free LLM course has just been updated.
This is a step-by-step guide with useful resources and notebooks for both beginners and those who already have an ml-base.
The course is divided into 3 parts:
1️⃣ LLM Fundamentals : The block provides fundamental knowledge of mathematics, Python and neural networks.
2️⃣ LLM Scientist : This block focuses on the internal structure of LLMs and their creation using the latest technologies and frameworks.
3️⃣ The LLM Engineer : Here you will learn how to write applications in a hands-on way and how to deploy them.
⭐️ 41.4k stars on Github
📌 https://github.com/mlabonne/llm-course
#llm #course #opensource #ml
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Are you looking to become a machine learning engineer? 🤖
The algorithm brought you to the right place! 🚀
I created a free and comprehensive roadmap. Let’s go through this thread and explore what you need to know to become an expert machine learning engineer:
📚 Math & Statistics
Just like most other data roles, machine learning engineering starts with strong foundations from math, especially in linear algebra, probability, and statistics. Here’s what you need to focus on:
- Basic probability concepts 🎲
- Inferential statistics 📊
- Regression analysis 📈
- Experimental design & A/B testing 🔍
- Bayesian statistics 🔢
- Calculus 🧮
- Linear algebra 🔠
🐍 Python
You can choose Python, R, Julia, or any other language, but Python is the most versatile and flexible language for machine learning.
- Variables, data types, and basic operations ✏️
- Control flow statements (e.g., if-else, loops) 🔄
- Functions and modules 🔧
- Error handling and exceptions ❌
- Basic data structures (e.g., lists, dictionaries, tuples) 🗂️
- Object-oriented programming concepts 🧱
- Basic work with APIs 🌐
- Detailed data structures and algorithmic thinking 🧠
🧪 Machine Learning Prerequisites
- Exploratory Data Analysis (EDA) with NumPy and Pandas 🔍
- Data visualization techniques to visualize variables 📉
- Feature extraction & engineering 🛠️
- Encoding data (different types) 🔐
⚙️ Machine Learning Fundamentals
Use the scikit-learn library along with other Python libraries for:
- Supervised Learning: Linear Regression, K-Nearest Neighbors, Decision Trees 📊
- Unsupervised Learning: K-Means Clustering, Principal Component Analysis, Hierarchical Clustering 🧠
- Reinforcement Learning: Q-Learning, Deep Q Network, Policy Gradients 🕹️
Solve two types of problems:
- Regression 📈
- Classification 🧩
🧠 Neural Networks
Neural networks are like computer brains that learn from examples 🧠, made up of layers of "neurons" that handle data. They learn without explicit instructions.
Types of Neural Networks:
- Feedforward Neural Networks: Simplest form, with straight connections and no loops 🔄
- Convolutional Neural Networks (CNNs): Great for images, learning visual patterns 🖼️
- Recurrent Neural Networks (RNNs): Good for sequences like text or time series 📚
In Python, use TensorFlow and Keras, as well as PyTorch for more complex neural network systems.
🕸️ Deep Learning
Deep learning is a subset of machine learning that can learn unsupervised from data that is unstructured or unlabeled.
- CNNs 🖼️
- RNNs 📝
- LSTMs ⏳
🚀 Machine Learning Project Deployment
Machine learning engineers should dive into MLOps and project deployment.
Here are the must-have skills:
- Version Control for Data and Models 🗃️
- Automated Testing and Continuous Integration (CI) 🔄
- Continuous Delivery and Deployment (CD) 🚚
- Monitoring and Logging 🖥️
- Experiment Tracking and Management 🧪
- Feature Stores 🗂️
- Data Pipeline and Workflow Orchestration 🛠️
- Infrastructure as Code (IaC) 🏗️
- Model Serving and APIs 🌐
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
The algorithm brought you to the right place! 🚀
I created a free and comprehensive roadmap. Let’s go through this thread and explore what you need to know to become an expert machine learning engineer:
📚 Math & Statistics
Just like most other data roles, machine learning engineering starts with strong foundations from math, especially in linear algebra, probability, and statistics. Here’s what you need to focus on:
- Basic probability concepts 🎲
- Inferential statistics 📊
- Regression analysis 📈
- Experimental design & A/B testing 🔍
- Bayesian statistics 🔢
- Calculus 🧮
- Linear algebra 🔠
🐍 Python
You can choose Python, R, Julia, or any other language, but Python is the most versatile and flexible language for machine learning.
- Variables, data types, and basic operations ✏️
- Control flow statements (e.g., if-else, loops) 🔄
- Functions and modules 🔧
- Error handling and exceptions ❌
- Basic data structures (e.g., lists, dictionaries, tuples) 🗂️
- Object-oriented programming concepts 🧱
- Basic work with APIs 🌐
- Detailed data structures and algorithmic thinking 🧠
🧪 Machine Learning Prerequisites
- Exploratory Data Analysis (EDA) with NumPy and Pandas 🔍
- Data visualization techniques to visualize variables 📉
- Feature extraction & engineering 🛠️
- Encoding data (different types) 🔐
⚙️ Machine Learning Fundamentals
Use the scikit-learn library along with other Python libraries for:
- Supervised Learning: Linear Regression, K-Nearest Neighbors, Decision Trees 📊
- Unsupervised Learning: K-Means Clustering, Principal Component Analysis, Hierarchical Clustering 🧠
- Reinforcement Learning: Q-Learning, Deep Q Network, Policy Gradients 🕹️
Solve two types of problems:
- Regression 📈
- Classification 🧩
🧠 Neural Networks
Neural networks are like computer brains that learn from examples 🧠, made up of layers of "neurons" that handle data. They learn without explicit instructions.
Types of Neural Networks:
- Feedforward Neural Networks: Simplest form, with straight connections and no loops 🔄
- Convolutional Neural Networks (CNNs): Great for images, learning visual patterns 🖼️
- Recurrent Neural Networks (RNNs): Good for sequences like text or time series 📚
In Python, use TensorFlow and Keras, as well as PyTorch for more complex neural network systems.
🕸️ Deep Learning
Deep learning is a subset of machine learning that can learn unsupervised from data that is unstructured or unlabeled.
- CNNs 🖼️
- RNNs 📝
- LSTMs ⏳
🚀 Machine Learning Project Deployment
Machine learning engineers should dive into MLOps and project deployment.
Here are the must-have skills:
- Version Control for Data and Models 🗃️
- Automated Testing and Continuous Integration (CI) 🔄
- Continuous Delivery and Deployment (CD) 🚚
- Monitoring and Logging 🖥️
- Experiment Tracking and Management 🧪
- Feature Stores 🗂️
- Data Pipeline and Workflow Orchestration 🛠️
- Infrastructure as Code (IaC) 🏗️
- Model Serving and APIs 🌐
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
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5 Easy Projects to Build as a Beginner
(No AI degree needed. Just curiosity & coffee.)
❯ 1. Calculator App
• Learn logic building
• Try it in Python, JavaScript or C++
• Bonus: Add GUI using Tkinter or HTML/CSS
❯ 2. Quiz App (with Score Tracker)
• Build a fun MCQ quiz
• Use basic conditions, loops, and arrays
• Add a timer for extra challenge!
❯ 3. Rock, Paper, Scissors Game
• Classic game using random choice
• Great to practice conditions and user input
• Optional: Add a scoreboard
❯ 4. Currency Converter
• Convert from USD to INR, EUR, etc.
• Use basic math or try fetching live rates via API
• Build a mini web app for it!
❯ 5. To-Do List App
• Create, read, update, delete tasks
• Perfect for learning arrays and functions
• Bonus: Add local storage (in JS) or file saving (in Python)
React with ❤️ for the source code
Python Projects: https://whatsapp.com/channel/0029Vau5fZECsU9HJFLacm2a
Coding Projects: https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
ENJOY LEARNING 👍👍
(No AI degree needed. Just curiosity & coffee.)
❯ 1. Calculator App
• Learn logic building
• Try it in Python, JavaScript or C++
• Bonus: Add GUI using Tkinter or HTML/CSS
❯ 2. Quiz App (with Score Tracker)
• Build a fun MCQ quiz
• Use basic conditions, loops, and arrays
• Add a timer for extra challenge!
❯ 3. Rock, Paper, Scissors Game
• Classic game using random choice
• Great to practice conditions and user input
• Optional: Add a scoreboard
❯ 4. Currency Converter
• Convert from USD to INR, EUR, etc.
• Use basic math or try fetching live rates via API
• Build a mini web app for it!
❯ 5. To-Do List App
• Create, read, update, delete tasks
• Perfect for learning arrays and functions
• Bonus: Add local storage (in JS) or file saving (in Python)
React with ❤️ for the source code
Python Projects: https://whatsapp.com/channel/0029Vau5fZECsU9HJFLacm2a
Coding Projects: https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
ENJOY LEARNING 👍👍
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