Master the basics of Python syntax, data structures, control flow, and functions. This is your core tool.

• Python Official Tutorial (High Value)
• Codecademy: Learn Python 3 (Interactive, Practical)
• Corey Schafer: Python Tutorials (Excellent for deeper dives)

Understand the core concepts of Linear Algebra, Calculus, Probability, and Statistics. Focus on intuition behind algorithms.

• 3Blue1Brown: Essence of Linear Algebra (Visual, High Value)
• StatQuest with Josh Starmer: Statistics Fundamentals (Clear, Intuitive)
• Khan Academy: Linear Algebra & Probability (Comprehensive Basics)

Learn to work with numerical data and tabular datasets efficiently.

• Kaggle Learn: Pandas (Hands-on, Practical)
• NumPy Official Beginner's Guide (Foundational)

Understand core ML concepts like supervised/unsupervised learning, regression, classification, and basic algorithms.

• Stanford University (Andrew Ng): Machine Learning (Classic, High Value)
• Kaggle Learn: Intro to Machine Learning (Practical, Code-focused)
• Scikit-learn User Guide (Official Docs, Practical)

Apply your knowledge to a simple, complete project from data loading to model evaluation.

• Kaggle: Titanic Survival Prediction Tutorial (Popular Starter Project)
• Towards Data Science: House Price Prediction (Detailed Walkthrough)

Explore ensemble methods, dimensionality reduction, and more complex algorithms.

• DeepLearning.AI: Machine Learning Engineering for Production (MLOps) Specialization (Practical, Production-focused)
• Towards Data Science: Ensemble Learning Explained (Clear Explanations)

Learn how to create and select effective features to boost model performance.

• Kaggle Learn: Feature Engineering (Hands-on)
• Towards Data Science: Feature Selection Techniques (Practical Guide)

Deepen your understanding of metrics, hyperparameter optimization, and explaining model predictions.

• Scikit-learn: Model Evaluation (Official Reference)
• Interpretable Machine Learning by Christoph Molnar (Comprehensive Book)

Start thinking about how to deploy and manage ML models in a production environment.

• MLOps Community (Industry Insights)
• Towards Data Science: Deploy ML with Flask (Practical Guide)

Apply advanced techniques to more complex datasets and problems.

• Kaggle: Tabular Playground Series (Good for practice)
• DataCamp: Data Science Case Studies (Business Applications)

Design robust, scalable ML systems for large-scale data and complex applications.

• Designing Machine Learning Systems by Chip Huyen (Industry Standard)
• Google Cloud Vertex AI Documentation (Cloud ML Platform)
• AWS SageMaker Documentation (Cloud ML Platform)

Master CI/CD for ML, model monitoring, data governance, and infrastructure automation.

• MLOps Community (Comprehensive MLOps Resource)
• Databricks MLOps Guide (Industry Perspective)

Understand and implement principles of fairness, transparency, and accountability in AI systems.

• Google Responsible AI Practices (Industry Guidelines)
• IBM AI Ethics Blog (Research & Perspectives)

Deepen expertise in areas like Time Series Analysis, Recommender Systems, or Reinforcement Learning.

• University of Alberta: Reinforcement Learning Specialization (Comprehensive)
• Kaggle Learn: Recommender Systems (Practical)

Contribute to the community, mentor junior colleagues, and lead technical initiatives.

• Open Source Guide: How to Contribute (General Guidance)
• LinkedIn: How to be an Effective Mentor (Career Development)

Ensure a solid grasp of Python, data manipulation, and basic ML concepts before diving into Deep Learning.

• (Refer to ML Fresher Roadmap Steps 1-3)

Understand what neural networks are, how they learn (backpropagation), and key components like activation functions.

• 3Blue1Brown: But what is a neural network? (Highly Visual, Intuitive)
• DeepLearning.AI: Deep Learning Specialization (Course 1) (Foundational, Andrew Ng)

Learn to build simple neural networks using a popular framework.

• TensorFlow Quickstart for Beginners (Official Guide)
• PyTorch Tutorials: Learn the Basics (Official Guide)
• Kaggle Learn: Intro to Deep Learning (Hands-on, Keras/TF)

Implement a basic feedforward neural network for classification or regression.

• Towards Data Science: Build Your First NN (Step-by-step tutorial)

Master image processing tasks like classification, object detection, and segmentation using CNNs.

• fast.ai: Practical Deep Learning for Coders (Top-down, Practical)
• Stanford CS231n: Convolutional Neural Networks for Visual Recognition (Lectures, High Value)

Learn about sequence modeling, natural language processing, and the Transformer architecture.

• DeepLearning.AI: Natural Language Processing Specialization (Comprehensive)
• The Illustrated Transformer by Jay Alammar (Visual, Intuitive)

Leverage powerful pre-trained models to accelerate your DL projects and achieve state-of-the-art results.

• TensorFlow: Transfer Learning Tutorial (Practical Example)
• PyTorch: Transfer Learning Tutorial (Practical Example)

Deepen your skills in TensorFlow/PyTorch, including custom layers, training loops, and distributed training basics.

• TensorFlow: Custom Layers & Models (Advanced Usage)
• PyTorch: Distributed Training Tutorial (Scalability)

Work on projects that involve more complex DL applications, including initial generative models.

• Kaggle: Getting Started with NLP (fast.ai) (Practical NLP)
• TensorFlow: DCGAN Tutorial (Basic Generative Model)

Explore the cutting-edge of generative AI, including their architectures and training complexities.

• Lilian Weng: Understanding Diffusion Models (In-depth Blog)
• AssemblyAI: Best LLM Tools & Frameworks (Practical Overview)
• "Attention Is All You Need" Paper (Foundational Transformer Paper)

Learn techniques to make large DL models run faster and consume less memory, especially for deployment.

• TensorFlow Model Optimization Toolkit (Official Guide)
• PyTorch Quantization Tutorial (Practical Optimization)

Address bias, fairness, privacy, and safety concerns in complex deep learning models.

• Accenture: Responsible AI (Business Perspective)
• Distill.pub: Attacking ML with Adversarial Examples (Visual Explanation)

Actively read, understand, and implement cutting-edge research papers. Contribute to open-source DL projects.

• arXiv: Computation and Language (Latest NLP/GenAI Papers)
• Yannic Kilcher's YouTube Channel (Research Paper Breakdowns)

Deepen expertise in areas like Reinforcement Learning, Multi-modal AI, or Graph Neural Networks.

• University of Alberta: Reinforcement Learning Specialization (Comprehensive)
• Lilian Weng: LLM Powered Autonomous Agents (Advanced Topic)

Grasp what Generative AI is, the difference between discriminative and generative models, and common applications.

• Google: Introduction to Generative AI (High-level Overview)
• NVIDIA: What is Generative AI? (Clear Explanation)

Learn the basics of the Transformer architecture, which is the foundation of most modern LLMs.

• The Illustrated Transformer (Essential Reading, Visual)
• Hugging Face NLP Course: Introduction to Transformers (Practical)

Learn how to write effective prompts to guide LLMs and get desired outputs.

• DeepLearning.AI: ChatGPT Prompt Engineering for Developers (Practical, High Value)
• Prompting Guide (Comprehensive Resource)

Use a simple API (like OpenAI's or Hugging Face's) to build a basic application, such as a text summarizer or a simple chatbot.

• OpenAI API Quickstart (Official Guide)
• freeCodeCamp: Build a Python App with the OpenAI API (Video Tutorial)

Learn how to adapt pre-trained models to specific tasks and datasets for improved performance.

• Hugging Face: Fine-tuning a Pre-trained Model (Official Guide)
• DeepLearning.AI: Finetuning Large Language Models (Short Course)

Understand how to combine LLMs with external knowledge bases to reduce hallucinations and provide more accurate, up-to-date information.

• Pinecone: Retrieval-Augmented Generation (RAG) (In-depth Guide)
• LangChain: Question Answering over Documents (Practical Example)

Explore models like DALL-E, Midjourney, and Stable Diffusion. Understand the basics of diffusion models.

• Lilian Weng: What are Diffusion Models? (Excellent Technical Blog)
• DeepLearning.AI: Generative AI with LLMs (includes multimodal) (Comprehensive Course)

Learn to use frameworks that simplify the development of complex LLM applications.

• LangChain Documentation (Official Guide)
• LlamaIndex Documentation (Official Guide for RAG)

Explore the architecture of autonomous agents that can reason, plan, and execute tasks using LLMs as their core engine.

• Lilian Weng: LLM-Powered Autonomous Agents (Seminal Blog Post)
• LangGraph: Building Stateful, Multi-actor Applications (Advanced Framework)

Master the challenges of deploying, monitoring, and scaling large generative models efficiently and cost-effectively.

• MLOps Community: LLMOps (Community Resources)
• Databricks: What is LLMOps? (Industry Perspective)

Dive deep into the research and techniques for making powerful AI models safer, more controllable, and aligned with human values.

• Anthropic Research (Leading AI Safety Research)
• OpenAI Research on Process Supervision (Advanced Alignment Technique)

Read, implement, and contribute to the latest research in generative modeling, from new architectures to novel training methods.

• arXiv: Computation and Language (Latest Papers)
• Hugging Face Transformers Documentation (Contribute to the Ecosystem)