Learning plan cover

Ai for engineers

LEARNING PLAN

Ai for engineers

This learning plan is designed for engineers who want to transition into AI and machine learning roles or enhance their existing software engineering skills with practical AI capabilities. It bridges the gap between theoretical AI concepts and real-world implementation, making it ideal for software developers, data engineers, and technical professionals who need to build production-grade AI systems rather than just understanding AI at a surface level.

ByCommunity User
2 h 13 m
11 coursesUpdated 5 days ago
Artificial IntelligenceArtificial Intelligence and Generative AI for BeginnersAI Explained: From Zero to Understanding11 sources

How This Learning Plan Was Made

This plan was crafted by BeFreed’s proprietary AI to help you learn Ai for engineers with ease. It is curated from in-depth research on the topic and structured around the most effective learning journeys proven by BeFreed users.

Each episode delivers bite-sized, high-impact lessons distilled from world-class sources — bestselling books, research papers, and expert insights. Together, they form a sophisticated yet accessible path to mastering Ai for engineers.

What You'll Learn

  • Build a solid conceptual foundation in AI and machine learning principles, understanding how machines learn and make intelligent decisions
  • Develop practical machine learning engineering skills using Python and industry-standard tools to build, train, and evaluate real ML systems
  • Master deep learning architectures and neural networks by building networks from scratch and understanding the underlying mathematics
  • Learn to deploy and scale AI systems in production environments using MLOps practices and production-ready engineering patterns
  • Gain end-to-end AI engineering capabilities from theory to production deployment that distinguish professional implementations from prototypes

Build a strong conceptual foundation by exploring what AI really is, how machines learn, and the core principles that power modern artificial intelligence systems.

Artificial Intelligence cover
Artificial Intelligence

A captivating exploration of AI's potential and limitations, demystifying the hype and addressing crucial questions about machine intelligence.

9 m
Artificial Intelligence and Generative AI for Beginners cover
Artificial Intelligence and Generative AI for Beginners

Comprehensive guide to AI and generative AI for all skill levels.

9 m
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AI 101: The Fundamentals of Artificial Intelligence - NtivaArtificial Intelligence (AI) Basics for Beginners | Learning TreeAI for Beginners: Artificial Intelligence Explained in Simple TermsUnderstanding Artificial Intelligence
6 sources
AI Explained: From Zero to Understanding

Strip away the jargon and discover what artificial intelligence really is. Learn how AI already powers your daily life and get practical tips for using these powerful tools responsibly.

9 m

Master practical ML skills by building real systems with Python, exploring supervised and unsupervised learning, and understanding how to train and evaluate models using industry-standard tools.

Hands-On Machine Learning with Scikit-Learn and TensorFlow cover
Hands-On Machine Learning with Scikit-Learn and TensorFlow

Step-by-step guide to machine learning with Python

8 m
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source 1source 2source 3source 4
6 sources
AI Revolution: How Machines Learn & Transform Industries

Dive deep into artificial intelligence fundamentals - from neural networks mimicking brain function to reinforcement learning discovering winning strategies. Explore real industry transformations and practical steps for thriving in the AI-powered future.

22 m

Dive into the architecture of neural networks, understand how deep learning powers modern AI breakthroughs, and build your own networks from scratch to grasp the underlying mathematics.

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Make Your Own Neural Network

Step-by-step guide to building neural networks for beginners.

8 m
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source 1Fundamentals of Artificial Neural Networks and Deep LearningNeural Networks and Deep Learning: A Comprehensive Introductionsource 4
6 sources
Neural Networks: From Brain Biology to AI Revolution

Discover how artificial neural networks mimic human brains to power everything from face recognition to self-driving cars. Learn the elegant math behind machine intelligence and explore real-world applications transforming our daily lives.

11 m
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Make Your Own Neural NetworkHands-On Machine Learning with Scikit-Learn and TensorFlowPython CookbookChatGPT For Dummies
8 sources
Deep Learning: From Bicycles to Rocket Ships

Explore the evolution of neural networks from their 1965 origins through the AI winter to today's revolution, as we unpack how these brain-inspired systems are transforming technology across industries.

17 m

Learn to deploy, scale, and maintain AI systems in production environments, covering MLOps practices, system design considerations, and real-world engineering challenges that separate prototypes from production-ready solutions.

Keras Reinforcement Learning Projects cover
Keras Reinforcement Learning Projects

Build self-learning AI agents with 9 reinforcement learning projects using Keras.

9 m
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The 25 Most Common AI Engineers Interview QuestionsCommon AI Engineer Interview Questions & Answers (2025)AI Engineer Interview Questions: The Ultimate Preparation ...source 4
6 sources
AI Engineer Interview Mastery Guide

Jackson and Lena break down everything AI engineers need to ace technical interviews, from ML fundamentals and deep learning architectures to system design and real-world case studies.

8 m
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source 1RAG Architecture: Complete Guide to Retrieval-Augmented ...Reference Architecture : Retrieval Augmented Generation (RAG)Optimizing Chunking, Embedding, and Vectorization for Retrieval ...
6 sources
RAG & Vector Databases: Cloud Architecture Deep Dive

Master RAG implementation from chunking strategies to Kubernetes deployment patterns. Explore vector database selection, optimization techniques, and production-ready cloud architectures for enterprise AI systems.

23 m

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