AI Mastery Learning Path

In recent times, I've had the pleasure of engaging with many individuals who are enthusiastically venturing into the fascinating domains of AI, ML and GenAI as part of their ongoing learning endeavors. Personal motivation is the biggest factor and the more you learn, more hungry you will feel about acquiring knowledge and explore applications. I suggest this valuable concept from Japanese martial arts known as "Shuhari” for AI learning endeavors. This concept provides a structured approach to learning and mastery: Shu (守) - Grasp the Fundamentals: - Begin at the Shu stage, where your focus is on acquiring a strong understanding of the basics. - Just as martial arts students learn by emulating their master's precise movements, in AI and ML, this involves immersing yourself in the foundational principles, algorithms, and tools (understanding of mathematics, including linear algebra, calculus, and statistics, is essential for comprehending the underlying principles of AI algorithms). - This is the phase for building a robust knowledge base and skill set. Ha (破) - Explore and Integrate: - Transitioning to the Ha stage signifies a broader exploration. Here, it's about experimentation and learning from multiple sources, akin to martial artists who incorporate various styles into their practice. - Experiment with different AI and ML approaches, blend insights from diverse experts, and integrate these learnings into your AI and ML practice. Your personal strength will be what you bring to the table at this level - domain-specific knowledge in applying AI effectively in real-world scenarios. - This phase encourages adaptability and synthesis. Ri (離) - Innovate and Apply Creatively: - The Ri stage represents the zenith of mastery. At this point, you should aim to become a problem solver in the AI and ML domain. - Like martial artists who develop their unique styles, you'll apply your knowledge creatively across a range of industry domains. Innovate by creating novel algorithms, new approaches, and pushing the boundaries of what's possible. - This is the stage where you could truly begin to lead in the field. And on a personal note, I've been on this path for six years straight, and I genuinely believe this investment is worth it for personal transformation and staying relevant in this dynamic field. AI can benefit from all, and AI can benefit all. #AI #GenAI #MachineLearning #ShuhariMastery

"How to master AI?" This blueprint changed everything for me. It got me from zero AI knowledge to law tech founder in 18 months You can do this too. Here's the exact path I took: Months 1-3: Foundation → Started with ChatGPT for basic research tasks  → Read "The AI Advantage" and "Human + Machine"  → Took a 6 week AI course at MIT → Joined 3 AI newsletters (not 30) Months 4-6: Application  → Tested AI on real legal work (contract review, brief drafting)  → Documented what worked, what didn't  → Started following AI ethicists and legal tech leaders - some of my favorite podcasts: AI Daily Brief Legal Innovation Spotlight AI and the Future of Law Law Next Months 7-9: Community  → Joined AI meetups in my city  → Connected with other non-technical AI adopters  → Started sharing lessons learned on LinkedIn Months 10-12: Specialization  → Focused specifically on AI for legal practice  → Reached out to small firm lawyers for feedback  → Built first simple AI workflow for discovery responses Months 13-18: Authority  → Launched Law Tech AI with one clear solution  → Got first CLE speaking opportunity  → Started consulting with firms on AI adoption No computer science degree required.  No coding bootcamp. Just curiosity and consistency. The legal profession needs AI guides who think like lawyers, not programmers. Are you ready to start your AI journey?

Excited to share this essential roadmap for anyone serious about thriving in the AI era! Whether you're a beginner or looking to deepen your expertise, mastering these foundational AI concepts will set you up for long-term success: 🔹 AI Foundations • Understand AI basics, its various types, and real-world applications. 🔹 Programming & Math for AI • Build strong fundamentals in Python, linear algebra, probability, calculus, and statistics. 🔹 Machine Learning (ML) • Learn supervised, unsupervised, and semi-supervised approaches, including regression, classification, clustering, and core algorithms. 🔹 Deep Learning (DL) • Explore advanced neural networks: CNNs, RNNs, LSTMs, autoencoders, and backpropagation. 🔹 Large Language Models (LLMs) • Dive into transformers, BERT, GPT, tokenization, and attention mechanisms powering tools like ChatGPT. 🔹 Prompt Engineering • Master zero-shot/few-shot prompting, chain-of-thought, and instruction tuning to get the best from LLMs. 🔹 Retrieval-Augmented Generation (RAG) • Combine LLMs with external knowledge sources using vector databases and advanced pipelines. 🔹 Vector Databases • Learn to store and retrieve high-dimensional vectors (FAISS, Pinecone, Weaviate, ChromaDB, Milvus). 🔹 AI Agents & Agentic AI • Automate complex workflows with tools and agent architectures (AutoGen, CrewAI). 🔹 Computer Vision • Enable machines to “see” with image classification, object detection, YOLO, and OpenCV. 🔹 Natural Language Processing (NLP) • Let machines understand and generate language with NER, POS tagging, sentiment analysis, and summarization. 🔹 Model Deployment & Serving • Deploy models into production with robust monitoring, logging, and A/B testing. 🔹 MLOps & Scalability • Scale production AI systems with efficient pipelines and best practices. 🔹 Real-World Projects & Use Cases • Apply your skills to impactful projects across diverse industries.    If you're starting out or aiming to future-proof your tech career, focusing on these concepts will help you unlock new opportunities in AI. Ready to level up?

6 books. 6 courses. 6 months. Ship real AI without the wait or the tuition: My friend, Sairam Sundaresan spent over 14 years in AI research and engineering. Read hundreds of papers. Tested countless courses. Shipped AI products to production. Here's the truth: You can get 90% of the practical AI knowledge you need without spending $120K on a degree. So, he put together a focused 6-month roadmap for you. And I wanted to share it with you. Each month, you'll read 1 book and complete 1 course. Just 60 minutes/day. Here’s your roadmap: 1/ Month 1: Python: 📘 Book: Effective Python ✅ Automate repetitive tasks ✅ Write clean, idiomatic code ✅ Build scripts that save hours 🎥 Course: Intro to Python (Harvard) ✅ Core Python skills ✅ Solve real coding problems ✅ Build confidence through practice 2/ Month 2: Practical Deep Learning: 📘 Book: Hands on ML ✅ Learn core ML (not just DL) ✅ Build neural nets ✅ Go from beginner to builder 🎥 Course: FastAI's Practical Deep Learning ✅ Train real models fast ✅ Solve real-world tasks ✅ Minimal code, big results 3/ Month 3: ML System Design: 📘 Book: Designing ML Systems ✅ Collecting data and choosing metrics ✅ Monitor and address issues ✅ Developing responsible systems 🎥 Course: ML in Production (Coursera) ✅ Project scoping & data needs ✅ Modeling strategies ✅ Deployment requirements 4/ Month 4: MLOps & Deployment: 📘 Book: ML Design Patterns ✅ Build reproducible pipelines ✅ Apply proven design patterns ✅ Work back from the problem 🎥 Course: Made with ML ✅ Learn MLOps first principles ✅ Easily scale ML workloads ✅ Go from dev to prod + CI/CD 5/ Month 5: AI at Scale: 📘 Book: AI Engineering ✅ Understand modern AI systems ✅ Learn to develop AI applications ✅ Explore prompting, RAG & agents 🎥 Course: Stanford MLSys Seminars ✅ Real-world AI infrastructure ✅ Proven deployment patterns ✅ Lessons from top AI teams 6/ Month 6: LLMs in Production: 📘 Book: Building LLMs for Production ✅ Design LLM systems end-to-end ✅ Build RAG pipelines reliably ✅ Monitor and maintain in prod 🎥 Course: The LLM Course ✅ Work with LLM APIs ✅ Advanced RAG & Agents ✅ Inference Optimization & Deployment That’s your 6-month roadmap to AI mastery. All it takes? Just 60 minutes/day of consistent learning. Tag someone who’s learning AI. This roadmap will save them months. Or better yet, take the 6-month challenge together. ♻️ Share with someone in your network who wants to learn more about AI. ➕ Follow me, Ashley Nicholson, for more tech insights. Content credit: Sairam Sundaresan. Give him a follow!