Post by Shivam Kumar

🧭 Step-by-Step Roadmap to Become an AI Engineer 1. Learn the Fundamentals of Programming (Month 1–2) AI engineering starts with coding — usually in Python. Learn: Python (core syntax, loops, functions, OOP, libraries) Data structures & algorithms (basic understanding) Git & GitHub (version control) Resources: Python for Everybody (Coursera) YouTube: “Python full course for beginners” – FreeCodeCamp 2. Master Math for AI (Month 2–3) AI = math + data + programming. You don’t need to be a math genius, just strong in essentials. Focus on: Linear Algebra → vectors, matrices (used in neural networks) Probability & Statistics → for machine learning Calculus basics → optimization, gradients Resources: Khan Academy (free) “Mathematics for Machine Learning” by Imperial College (Coursera) 3. Learn Data Handling & Analysis (Month 3–4) Before AI, you must handle data effectively. Learn: NumPy, Pandas (data manipulation) Matplotlib, Seaborn, Plotly (data visualization) SQL (to query data) Basic EDA (Exploratory Data Analysis) Mini projects: Analyze a dataset (e.g., COVID, sales, or sports) Build a dashboard or simple report 4. Learn Machine Learning (Month 4–5) Machine Learning (ML) is the foundation of AI. Learn concepts: Supervised & Unsupervised learning Regression, Classification, Clustering Decision Trees, Random Forest, SVM Model evaluation (accuracy, precision, recall) Feature engineering Libraries: Scikit-learn TensorFlow or PyTorch (for deep learning) Projects: Predict house prices Classify emails (spam/ham) Customer segmentation 5. Deep Learning & Neural Networks (Month 5–6) This is the core of AI (especially for NLP, computer vision, and generative AI). Learn: Neural Networks (ANN, CNN, RNN, LSTM) Transfer learning Image recognition (CNN) Natural Language Processing (NLP) Transformers & Large Language Models (LLMs) Tools: TensorFlow / Keras / PyTorch Hugging Face (for NLP models) Projects: Image classifier (e.g., cat vs. dog) Chatbot using Transformers Sentiment analysis using BERT 6. Learn AI Tools & Ecosystem Common tools: Jupyter Notebook, Google Colab Docker, Flask/FastAPI (for model deployment) AWS / Azure / GCP (for cloud AI) LangChain, OpenAI API (for Generative AI) 7. Build a Strong Portfolio Recruiters love projects more than certificates! Create 4–5 real projects: Image Classification NLP Chatbot Recommendation System Fraud Detection Generative AI App (e.g., Chatbot using GPT API) Upload them on GitHub, and write blog posts on LinkedIn or Medium. 8. Apply for Internships or Junior Roles Job titles to target: AI Engineer (Junior) Data Scientist Machine Learning Engineer AI Research Assistant Tip: Start freelancing or Kaggle competitions to gain visibility. 9. (Optional) Certifications Helpful but not mandatory: Google Professional ML Engineer IBM AI Engineering Professional Certificate (Coursera) DeepLearning.AI Specialization (Coursera) Harjasdeep Singhsir