What This Roadmap Offers:

1. Core Foundations
2. Build strong fundamentals in Python, Statistics, and Exploratory Data Analysis using tools like Pandas, NumPy, Matplotlib, and Seaborn.
3. Machine Learning & Deep Learning
4. Gain hands-on experience with supervised/unsupervised algorithms, feature engineering, and model evaluation using scikit-learn, TensorFlow, and PyTorch. Includes use cases such as image classification, chatbot development, and model tuning.
5. NLP and Generative AI
6. Learn modern NLP concepts—from TF-IDF and word embeddings to transformers like BERT and GPT. Dive into prompt engineering, RAG pipelines, and tools like Hugging Face, LangChain, and Llama Index.
7. Deployment & MLOps
8. Understand the production side of ML with tools like Docker, Streamlit, MLflow, and Airflow, along with exposure to cloud platforms (AWS/GCP/Azure) and big data tools like Spark.
9. Agentic AI & Advanced Ecosystems
10. Explore emerging trends such as Agentic AI (e.g., CrewAI, AutoGen, LangGraph) and multi-agent planning workflows, enabling you to stay ahead in the AI landscape.
11. Job Readiness & Resource Library
12. Includes resume guidance, mock interviews, curated learning playlists, and a portfolio/project strategy to help you land roles in data science and machine learning.

🎯 Who Is This For?

This guide is ideal for aspiring data scientists, AI enthusiasts, and career switchers seeking a modern, job-oriented learning path rooted in both foundational rigor and cutting-edge innovation.