How to Build Core Machine Learning Skills

I spent 3+ hours in the last 2 weeks putting together this no-nonsense curriculum so you can break into AI as a software engineer in 2025. This post (plus flowchart) gives you the latest AI trends, core skills, and tool stack you’ll need. I want to see how you use this to level up. Save it, share it, and take action. ➦ 1. LLMs (Large Language Models) This is the core of almost every AI product right now. think ChatGPT, Claude, Gemini. To be valuable here, you need to: →Design great prompts (zero-shot, CoT, role-based) →Fine-tune models (LoRA, QLoRA, PEFT, this is how you adapt LLMs for your use case) →Understand embeddings for smarter search and context →Master function calling (hooking models up to tools/APIs in your stack) →Handle hallucinations (trust me, this is a must in prod) Tools: OpenAI GPT-4o, Claude, Gemini, Hugging Face Transformers, Cohere ➦ 2. RAG (Retrieval-Augmented Generation) This is the backbone of every AI assistant/chatbot that needs to answer questions with real data (not just model memory). Key skills: -Chunking & indexing docs for vector DBs -Building smart search/retrieval pipelines -Injecting context on the fly (dynamic context) -Multi-source data retrieval (APIs, files, web scraping) -Prompt engineering for grounded, truthful responses Tools: FAISS, Pinecone, LangChain, Weaviate, ChromaDB, Haystack ➦ 3. Agentic AI & AI Agents Forget single bots. The future is teams of agents coordinating to get stuff done, think automated research, scheduling, or workflows. What to learn: -Agent design (planner/executor/researcher roles) -Long-term memory (episodic, context tracking) -Multi-agent communication & messaging -Feedback loops (self-improvement, error handling) -Tool orchestration (using APIs, CRMs, plugins) Tools: CrewAI, LangGraph, AgentOps, FlowiseAI, Superagent, ReAct Framework ➦ 4. AI Engineer You need to be able to ship, not just prototype. Get good at: -Designing & orchestrating AI workflows (combine LLMs + tools + memory) -Deploying models and managing versions -Securing API access & gateway management -CI/CD for AI (test, deploy, monitor) -Cost and latency optimization in prod -Responsible AI (privacy, explainability, fairness) Tools: Docker, FastAPI, Hugging Face Hub, Vercel, LangSmith, OpenAI API, Cloudflare Workers, GitHub Copilot ➦ 5. ML Engineer Old-school but essential. AI teams always need: -Data cleaning & feature engineering -Classical ML (XGBoost, SVM, Trees) -Deep learning (TensorFlow, PyTorch) -Model evaluation & cross-validation -Hyperparameter optimization -MLOps (tracking, deployment, experiment logging) -Scaling on cloud Tools: scikit-learn, TensorFlow, PyTorch, MLflow, Vertex AI, Apache Airflow, DVC, Kubeflow

The GenAI wave is real, but most engineers still feel stuck between hype and practical skills. That’s why I created this 15-step roadmap—a clear, technically grounded path to transitioning from traditional software development to advanced AI engineering. This isn’t a list of buzzwords. It’s the architecture of skills required to build agentic AI systems, production-grade LLM apps, and scalable pipelines in 2025. Here’s what this journey actually looks like: 🔹 Foundation Phase (Steps 1–5): → Start with Python + libraries (NumPy, Pandas, etc.) → Brush up on data structures & Big-O — still essential for model efficiency → Learn basic math for AI (linear algebra, stats, calculus) → Understand the evolution of AI from rule-based to supervised to agentic systems → Dive into prompt engineering: zero-shot, CoT, and templates with LangChain 🔹 Build & Integrate (Steps 6–10): → Work with LLM APIs (OpenAI, Claude, Gemini) and use function calling → Learn RAG: embeddings, vector DBs, LangChain chains → Build agentic workflows with LangGraph, CrewAI, and AutoGen → Understand transformer internals (positional encoding, masking, BERT to LLaMA) → Master deployment with FastAPI, Docker, Flask, and Streamlit 🔹 Production-Ready (Steps 11–15): → Learn MLOps: versioning, CI/CD, tracking with MLflow & DVC → Optimize for real workloads using quantization, batching, and distillation (ONNX, Triton) → Secure AI systems against injection, abuse, and hallucination → Monitor LLM usage and performance → Architect multi-agent systems with state control and memory Too many “AI tutorials” skip the real-world complexity, including permissioning, security, memory, token limits, and agent orchestration. But that’s what actually separates a prototype from a production-grade AI app. If you’re serious about becoming an AI Engineer, this is your blueprint. And yes, you can start today. You just need a structured plan and consistency. Feel free to save, share, or tag someone on this journey.

A lot of people ask me, what’s the roadmap to become an AI engineer? It’s not just about finishing a few ML courses, it’s about mastering the fundamentals, applying them in real projects, and building the skills to ship AI systems into production. Here’s a step-by-step path that works in 2025. 📓Step 0 – Build a Strong Technical Foundation → Learn Python deeply (AI’s primary language) plus working knowledge of C++, Java, or R. → Master data structures, algorithms, and software engineering principles. → Strengthen math for AI: linear algebra, calculus, probability, and statistics. 📌 Tip: Code along with tutorials and build small utilities like dataset parsers, algorithm implementations, or math simulations. 📗Step 1 – Learn ML & AI Fundamentals → Understand core ML concepts: supervised vs. unsupervised learning, regression, classification, clustering. → Explore deep learning: CNNs, RNNs, Transformers, NLP, generative AI. → Work hands-on with PyTorch, TensorFlow, and scikit-learn. 📌 Tip: Build a neural network from scratch using only NumPy to really understand how backpropagation works. 📘Step 2 – Apply Knowledge Through Projects → Start with small, scoped projects: image classification, NLP chatbots, recommendation engines. → Use public datasets from UCI, Kaggle, or Hugging Face Datasets. → Contribute to open-source AI projects or Kaggle competitions. 📌 Starter Ideas: → Sentiment analysis with real-time inference → Retrieval-Augmented Generation (RAG) PDF summarizer → Price prediction model with explainable AI dashboards 📕Step 3 – Learn Data Engineering, MLOps & Deployment → Practice data preprocessing and visualization with pandas, NumPy, matplotlib. → Learn to deploy models on AWS Sagemaker, GCP Vertex AI, or Azure ML. → Master MLOps: CI/CD pipelines, retraining workflows, monitoring, model version control. 📌 Tip: Build an end-to-end pipeline - from raw data ingestion to a deployed model API - using Docker + FastAPI. 📙 Step 4 – Specialize in an AI Domain → Choose a focus: Computer Vision, NLP, Generative AI, Reinforcement Learning, or Robotics. → Read research papers, replicate results, and keep up with top AI conferences (NeurIPS, ICML). 📌 Tip: Maintain a “replication repo” where you implement recent papers in your chosen field. 📔Step 5 – Build a Portfolio & Network → Showcase your projects on GitHub with clear READMEs and deployment instructions. → Write technical blogs, create short videos, or speak at meetups about your projects. → Join AI communities (Slack, Discord, LinkedIn), attend hackathons, and network with peers. If you’re starting today, pick one problem you care about, find a dataset, and commit to shipping a working MVP in 3 weeks. That’s how you start building momentum ❤️ 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg