Collections·1yComprehensive Course on Building AI Agents
Gain a thorough understanding of building AI agents through this in-depth guide. Learn about essential concepts, practical workflows, memory mechanisms, agentic flows, and safety guardrails. Explore design patterns, agentic frameworks, and multi-agent systems while optimizing AI agents for production environments. Develop key skills like prompt engineering to create responsive AI agents.
Daily Dose of Data Science | Avi Chawla | Substack·1yAI Agent Crash Course—Part 1
In this crash course, learn about AI agents and their implementation. It covers the fundamentals, memory for agents, agentic flows, guardrails, implementing agentic design patterns, and optimizing agents for production. The aim is to build autonomous systems that can reason, plan, take actions, and correct themselves, going beyond the capabilities of standalone generative models.
Daily Dose of Data Science | Avi Chawla | Substack·1y16 Techniques to Build Real-world RAG Systems
Scaling a prototype RAG system for real-world use presents significant challenges, such as performance bottlenecks and inefficient retrieval. This guide offers 16 practical techniques to help developers overcome these issues across five key pillars. It also highlights five agentic AI design patterns, including reflection, tool use, ReAct, planning, and multi-agent patterns, which enable LLMs to refine outputs, gather information, and subdivide tasks more effectively.
Daily Dose of Data Science | Avi Chawla | Substack·1yOpen-source Python Development Landscape
Explore the essential tools for various stages of Python development, including dependency and package managers, monitoring and profiling, virtual environments, linters and style checkers, type checkers, logging, testing, debugging, code refactoring, and code security. These tools are crucial for improving development workflow and code quality.
freeCodeCamp·1yLearn Linear Algebra for Machine Learning
Linear algebra is a crucial component of machine learning, offering a mathematical foundation for understanding models and algorithms. A new course by Tatev Aslanyan from Lunar Tech on the freeCodeCamp.org YouTube channel covers essential concepts such as vectors, matrices, transformations, and more. This course is suitable for beginners, data scientists, and AI practitioners looking to strengthen their knowledge of linear algebra in machine learning.
Machine Learning Mastery·1yNext-Level Data Science (7-Day Mini-Course)
Understand the evolution of data science from statistical analysis to the use of machine learning models. This 7-day mini-course is designed for developers with basic Python and machine learning knowledge, focusing on practical applications and explaining data through machine learning models. Participants will learn how to handle data, perform linear regression, interpret factors, select features, and utilize decision trees and random forests to build accurate models.
ByteGrad·1yPython Tutorial - All 25 Concepts You Need To Know
Python is a versatile language used in various domains such as machine learning, data science, automation, and web development. This guide explains important Python concepts including installation, using code editors like Visual Studio Code, and basic syntax like variables, data types, strings, lists, dictionaries, loops, and functions. Advanced topics like object-oriented programming, type checking with mypy, and using third-party packages are also covered. Additionally, the guide recommends DataCamp for mastering Python through interactive lessons and projects.
Machine Learning Mastery·1yThe 2025 Machine Learning Toolbox: Top Libraries and Tools for Practitioners
2024 marked a pivotal year for machine learning (ML) and artificial intelligence (AI) as they became mainstream, with the introduction of large language models (LLM) such as ChatGPT leading the way. In 2025, trends like generative AI and model explainability will continue shaping the ML landscape. Key libraries and tools for ML practitioners include LangChain for LLM application development, JAX for high-performance numerical computation, Fastai for deep learning, InterpretML for model explainability, and TokenSHAP for token-level analysis in LLMs. Embracing these tools can help practitioners build robust and transparent ML solutions.