🗺️ AI/ML Career Roadmap: Beginner to Job-Ready

A comprehensive 12-month roadmap to transition into an AI/ML career from scratch. Follow this timeline to go from beginner to job-ready.

Roadmap Overview

This roadmap is designed for complete beginners with programming experience. If you have no coding background, add 1-2 months to the timeline.

Stage	Duration	Key Focus	Skills	Expected Salary*
Beginner	0-3 months	Foundation	Python, Math Basics, ML Fundamentals	N/A (Learning)
Intermediate	3-6 months	Core Skills	Algorithms, Deep Learning, Projects	₹4-8L (Junior roles)
Advanced	6-12 months	Specialization	NLP/CV/RL, Production, Deployment	₹8-15L (Mid-level roles)
Job-Ready	12+ months	Professional	Interview Ready, Portfolio, Networking	₹10-20L+ (Senior roles)

*Salary ranges are approximate and vary by company, location, and experience

📚 Stage 1: Beginner Phase (0-3 Months)

Goal: Learn fundamentals and build first models

Time Commitment: 15-20 hours per week

Month 1: Python Foundations

Core Topics:

Python basics (variables, loops, functions)
Data structures (lists, dictionaries, tuples)
Object-oriented programming basics
File I/O and libraries

Projects:

Simple calculator with functions
Data analysis of CSV file
Small automation script

Resources:

Python.org tutorials
DataCamp (Python for Data Science)
Codecademy (Interactive learning)

Month 2: Math Foundations & NumPy/Pandas

Core Topics:

Linear algebra basics (vectors, matrices)
Basic statistics and probability
NumPy for numerical computing
Pandas for data manipulation

Projects:

Real dataset analysis with Pandas
Data cleaning and preprocessing
Statistical analysis of dataset

Month 3: ML Fundamentals

Core Topics:

What is Machine Learning? (supervised vs unsupervised)
Train-test split and cross-validation
Linear regression and logistic regression
Model evaluation (accuracy, precision, recall)

Projects:

House price prediction (regression)
Iris classification (classification)
Customer segmentation (clustering)

Tools:

Scikit-learn for ML algorithms
Matplotlib for visualization
Jupyter notebooks for experimentation

Beginner Stage Checklist:

✓ Can write Python programs without looking at references
✓ Understand NumPy arrays and Pandas dataframes
✓ Can build simple ML models with Scikit-learn
✓ Understand train-test split and model evaluation
✓ Have 3 simple projects on GitHub

🔧 Stage 2: Intermediate Phase (3-6 Months)

Goal: Master algorithms and build portfolio

Time Commitment: 20-25 hours per week

Month 4-5: Core Algorithms & Deep Learning Basics

Core Topics:

Decision trees and random forests
Support vector machines (SVM)
Neural networks fundamentals
Convolutional Neural Networks (CNN) basics
Recurrent Neural Networks (RNN/LSTM) basics

Projects (Build 2-3):

Handwritten digit recognition (CNN)
Stock price prediction (LSTM/RNN)
Text classification (NLP basics)
Object detection basics

Tools:

TensorFlow/Keras for deep learning
PyTorch as alternative
Google Colab for free GPU

Month 6: Feature Engineering & Model Optimization

Core Topics:

Feature engineering techniques
Handling imbalanced datasets
Hyperparameter tuning
Model selection and validation

Projects:

Kaggle competition participation
Real-world dataset project (get data yourself)
Open-source contribution (contribute to ML library)

Intermediate Stage Checklist:

✓ Can implement complex ML algorithms from scratch
✓ Comfortable with TensorFlow/Keras
✓ Have 5-7 portfolio projects on GitHub
✓ Participated in at least one Kaggle competition
✓ Understand why models fail and how to debug them
✓ Can write clean, production-ready code

🚀 Stage 3: Advanced Phase (6-12 Months)

Goal: Specialize and prepare for jobs

Time Commitment: 20-25 hours per week

Choose Your Specialization (Pick 1-2):

Option 1: Natural Language Processing (NLP)

Topics: Transformers, BERT, GPT, Text generation, Chatbots, Sentiment analysis

Projects: Chatbot, Text summarization, Machine translation, Q&A system

Option 2: Computer Vision (CV)

Topics: Advanced CNNs, Transfer learning, Object detection, Segmentation, Face recognition

Projects: Medical image analysis, Self-driving car basics, Face detection, Image super-resolution

Option 3: Reinforcement Learning (RL)

Topics: Q-learning, Policy gradient, DQN, Game AI, Robotics

Projects: Game AI, Robot control, Trading algorithms, Optimization problems

Production & Deployment (All should learn):

Containerization (Docker)
Cloud platforms (AWS, GCP, Azure)
Model serving (Flask, FastAPI)
Database management (PostgreSQL, MongoDB)
CI/CD pipelines

Advanced Stage Checklist:

✓ Deep expertise in one specialization
✓ Can explain your projects in detail
✓ Have deployed models in production
✓ Have 8-10 strong portfolio projects
✓ Contributing to open-source ML projects
✓ Writing technical blog posts

💼 Stage 4: Job-Ready Phase (12+ Months)

Goal: Land your first AI/ML job

Interview Preparation:

Technical interviews (coding, ML concepts, system design)
Behavioral interviews (STAR method)
Take-home projects and assignments
Presentations and explanations

Portfolio Preparation:

GitHub profile with 8-10 quality projects
Personal website or blog
Well-written README files for each project
Clear explanations of your work

Networking & Applications:

Connect on LinkedIn with AI/ML professionals
Attend meetups and conferences
Apply to 20-30 positions (focus on good fit)
Prepare for different interview styles

Common Questions in Interviews:

Technical: Explain your project, How would you approach this problem, Write code to solve this

ML Concepts: What’s overfitting, How do you evaluate models, When would you use X algorithm

System Design: How would you build X system at scale, What technologies would you use

Job-Ready Checklist:

✓ Can confidently explain all your projects
✓ Can solve coding problems efficiently
✓ Understand ML concepts deeply
✓ Have done mock interviews
✓ Have strong LinkedIn and GitHub presence
✓ Ready to negotiate salary

Essential Resources by Stage

Learning Platforms

Coursera
Udemy
DataCamp
Fast.ai
Google Colab

Practice Platforms

Kaggle
LeetCode
HackerRank
GitHub
Codewars

Documentation

TensorFlow Docs
PyTorch Docs
Scikit-learn Docs
Pandas Docs
Hugging Face

Interview Prep

ML System Design
Coding Interview
Behavioral Questions
Mock Interviews
Resume Feedback

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