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WO🚀 How to Become an AI Engineer in 2026 👩🏻💻Save for later
Step-by-step Roadmap
🔹 PHASE 1: Foundations (0–3 Months)
Don’t skip this. Weak foundations = stuck later.
1️⃣ Programming (Must-Have)
Python: loops, functions, OOP
Libraries: NumPy, Pandas, Matplotlib / Seaborn
📌 Practice daily:
LeetCode (easy)
HackerRank (Python)
2️⃣ Math for AI (Enough, not PhD level)
Focus only on:
Linear Algebra (vectors, matrices)
Probability & Statistics
Basic Calculus (idea of gradients)
📌 Conceptual understanding is enough — no heavy theory.
🔹 PHASE 2: Machine Learning (3–6 Months)
Learn:
Supervised & Unsupervised Learning
Feature Engineering
Model Evaluation
Algorithms:
Linear & Logistic Regression
KNN
Decision Trees
Random Forest
SVM
K-Means
Tools:
Scikit-learn
📌 Project Ideas:
House price prediction
Student performance prediction
Credit risk model
🔹 PHASE 3: Deep Learning & AI (6–10 Months)
Learn:
Neural Networks & Backpropagation
CNN (Images)
RNN / LSTM (Text)
Transformers (Basics)
Frameworks:
TensorFlow or PyTorch (choose ONE)
📌 Project Ideas:
Face mask detection
Image classifier
Spam email detector
Basic chatbot
🔹 PHASE 4: Modern AI (2025–2026) 🔥
This is where the JOBS are coming from.
Learn:
Generative AI
Large Language Models (LLMs)
Prompt Engineering
RAG (Retrieval-Augmented Generation)
Fine-tuning models
Tools:
OpenAI API
Hugging Face
LangChain
Vector Databases (FAISS / Pinecone)
📌 Project Ideas:
AI PDF Chat App
Resume Analyzer
AI Study Assistant
AI Customer Support Bot
🔹 PHASE 5: MLOps & Deployment (CRITICAL)
Learn:
Git & GitHub
Docker (basics)
FastAPI / Flask
Cloud basics (AWS or GCP)
Deploy:
ML models as APIs
AI apps on the cloud
📌 Recruiters LOVE deployed projects.
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