🚀 90-Day AI/ML Engineer Interview Preparation Roadmap

🎯 A comprehensive 90-day roadmap to ace your AI/ML Engineer interviews

Transform from beginner to interview-ready in just 3 months!

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📋 Table of Contents

• 🎯 Overview
• 📅 90-Day Roadmap
  • 🗓️ Month 1: ML Foundation
  • 🗓️ Month 2: Deep Learning & Projects
  • 🗓️ Month 3: GenAI & Interview Prep
• 💻 DSA Practice
• 📊 Progress Tracker
• 🎯 Final Projects Portfolio
• 📚 Additional Resources
• 🤝 Contributing

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🎯 Overview

This roadmap is designed to take you from zero to hero in AI/ML engineering within 90 days. By following this structured path, you'll build both theoretical knowledge and practical skills needed to excel in AI/ML Engineer interviews.

🏆 What You'll Achieve:

• ✅ Solid foundation in Machine Learning concepts
• ✅ Deep Learning expertise with hands-on projects
• ✅ Generative AI and modern AI techniques
• ✅ Strong programming and problem-solving skills
• ✅ Portfolio of impressive projects
• ✅ Interview confidence and technical communication

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📅 90-Day Roadmap

🗓️ Month 1: ML Foundation (Theory + Coding)

📖 Core Concepts to Master

🎯 Week 1-2: Fundamentals

• Linear Regression & Logistic Regression
  • Mathematical intuition
  • Cost functions and optimization
  • Implementation from scratch
• Bias-Variance Tradeoff
  • Understanding underfitting/overfitting
  • Model complexity analysis
• Gradient Descent
  • Batch, Mini-batch, Stochastic GD
  • Learning rate optimization

🎯 Week 3-4: Advanced Topics

• Statistics & Probability
  • Distributions, Bayes' theorem
  • Hypothesis testing
  • Central limit theorem
• Model Evaluation
  • Confusion matrix, precision, recall, F1-score
  • ROC curves and AUC
  • Cross-validation techniques
• Classic Algorithms Overview
  • Decision Trees & Random Forest
  • Support Vector Machines (SVM)
  • K-Nearest Neighbors (KNN)
  • Naive Bayes

📚 Study Resources

Resource	Type	Link	Priority
Andrew Ng's ML Course	Course	Coursera	🔥 High
StatQuest YouTube	Videos	YouTube Playlist	🔥 High
Hands-on ML Book	Book	O'Reilly	⭐ Medium

🛠️ Practice & Implementation

🔥 Daily Practice Routine

Week 1-2:

# Daily routine (2-3 hours)
✅ 1 hour: Theory (videos/reading)
✅ 1 hour: Coding practice with NumPy/Pandas
✅ 30 min: Implement algorithms from scratch

Week 3-4:

# Daily routine (3-4 hours)
✅ 1 hour: Advanced theory
✅ 1.5 hours: Kaggle competitions (beginner level)
✅ 1 hour: Algorithm implementation
✅ 30 min: Data visualization with Matplotlib/Seaborn

🏗️ Month 1 Projects

• Project 1: Implement Linear Regression from scratch (no scikit-learn)
• Project 2: Build a classification model for Titanic dataset
• Project 3: Create a data preprocessing pipeline

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🗓️ Month 2: Deep Learning & Projects

🧠 Deep Learning Fundamentals

🎯 Week 5-6: Neural Networks

• Neural Network Basics
  • Perceptron and multi-layer perceptrons
  • Forward propagation
  • Backpropagation algorithm
  • Activation functions (ReLU, Sigmoid, Tanh)
• Training Deep Networks
  • Loss functions (MSE, Cross-entropy)
  • Optimizers (SGD, Adam, RMSprop)
  • Regularization (L1, L2, Dropout)
  • Batch normalization

🎯 Week 7-8: Advanced Architectures

• Convolutional Neural Networks (CNNs)
  • Convolution, pooling layers
  • CNN architectures (LeNet, AlexNet, VGG, ResNet)
  • Transfer learning
• Recurrent Neural Networks (RNNs)
  • Vanilla RNNs, LSTM, GRU
  • Sequence-to-sequence models
• Transformers & Attention
  • Self-attention mechanism
  • Transformer architecture
  • BERT and GPT overview

📚 Study Resources

Resource	Type	Link	Priority
DeepLearning.AI Specialization	Course	Coursera	🔥 High
Fast.ai Deep Learning Course	Course	Fast.ai	🔥 High
Deep Learning Book	Book	Ian Goodfellow	⭐ Medium
Papers With Code	Research	paperwithcode.com	⭐ Medium

🚀 Month 2 Projects

🎨 Project Details & Implementation

🖼️ Project 1: Image Classifier

# Objective: Build CNN for CIFAR-10 or Fashion-MNIST
Technologies: TensorFlow/PyTorch, OpenCV
Key Skills: Data preprocessing, CNN architecture, transfer learning
Timeline: Week 5-6 (10-12 hours)

📝 Project 2: Sentiment Analysis

# Objective: NLP model for movie reviews or Twitter sentiment
Technologies: NLTK, spaCy, Transformers library
Key Skills: Text preprocessing, RNN/LSTM, model evaluation
Timeline: Week 7 (8-10 hours)

🤖 Project 3: Model Deployment

# Objective: Deploy your model using Streamlit or Hugging Face Spaces
Technologies: Streamlit, Gradio, Docker
Key Skills: Web deployment, API creation, user interface
Timeline: Week 8 (6-8 hours)

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🗓️ Month 3: GenAI & Interview Prep

🤖 Generative AI Deep Dive

🎯 Week 9-10: GenAI Foundations

• Embeddings & Vector Databases
  • Word2Vec, GloVe, BERT embeddings
  • Vector similarity and search
  • Pinecone, Weaviate, Chroma
• Retrieval-Augmented Generation (RAG)
  • RAG architecture and components
  • Document chunking and indexing
  • Retrieval strategies
• Prompt Engineering
  • Effective prompt design
  • Few-shot and zero-shot learning
  • Chain-of-thought prompting

🎯 Week 11-12: Advanced GenAI

• Fine-tuning vs Instruction-tuning
  • Parameter-efficient fine-tuning (LoRA, QLoRA)
  • Instruction datasets
  • RLHF (Reinforcement Learning from Human Feedback)
• Inference Pipelines
  • Model serving and optimization
  • GenAI system architecture
  • Performance optimization

📚 Study Resources

Resource	Type	Link	Priority
DeepLearning.AI GenAI Course	Course	Coursera	🔥 High
LangChain Documentation	Docs	LangChain	🔥 High
OpenAI API Documentation	Docs	OpenAI	⭐ Medium
Hugging Face Transformers	Library	Hugging Face	⭐ Medium

🎯 Final Month Projects

🏆 Capstone Projects

💬 Project 1: RAG Chatbot

# Objective: Custom knowledge base chatbot
Technologies: LangChain, OpenAI API, Streamlit, Vector DB
Features:
  - Document upload and processing
  - Semantic search and retrieval
  - Context-aware responses
  - Chat history management
Timeline: Week 9-10 (15-20 hours)

🎨 Project 2: Text-to-Image App

# Objective: Generate images from text descriptions
Technologies: Stability AI API, Gradio, PIL
Features:
  - Multiple art styles
  - Image customization
  - Batch generation
  - Gallery showcase
Timeline: Week 11 (8-10 hours)

📄 Project 3: Resume Analyzer

# Objective: AI-powered resume analysis tool
Technologies: LLMs, PDF processing, NLP
Features:
  - Resume parsing and extraction
  - Skill gap analysis
  - Job matching recommendations
  - Interview preparation tips
Timeline: Week 12 (10-12 hours)

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💻 DSA Practice (Throughout 90 Days)

🎯 Daily Goal: 2-3 Problems | Time Limit: 20 minutes per problem

📊 DSA Study Plan

📅 Weekly Distribution

Monday    → Arrays & Strings (2 problems)
Tuesday   → Linked Lists (2 problems)
Wednesday → Trees & Graphs (2 problems)
Thursday  → Dynamic Programming (2 problems)
Friday    → Sorting & Searching (2 problems)
Saturday  → Stack & Queue (2 problems)
Sunday    → Review & Mock Interviews (3 problems)

🏆 Recommended Problem Sets

Platform	Resource	Problems	Difficulty
LeetCode	Neetcode 150	150	Easy-Medium
GeeksforGeeks	GFG DSA Sheet	450	Easy-Hard
Striver	SDE Sheet	191	Medium-Hard

🎯 Focus Areas by Month

• Month 1: Arrays, Strings, Basic Math (Easy problems)
• Month 2: Trees, Graphs, DP basics (Easy-Medium)
• Month 3: Advanced DP, System Design, Mock Interviews (Medium-Hard)

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📊 Progress Tracker

📈 Month 1 Progress

🎯 Theory & Concepts

• Linear & Logistic Regression (/7 days)
• Bias-Variance & Overfitting (/3 days)
• Gradient Descent (/3 days)
• Statistics & Probability (/5 days)
• Model Evaluation Metrics (/4 days)
• Classic ML Algorithms (/8 days)

💻 Practice & Projects

• NumPy mastery (/10 sessions)
• Pandas proficiency (/10 sessions)
• Matplotlib/Seaborn (/8 sessions)
• Kaggle competitions (/5 competitions)
• From-scratch implementations (/6 algorithms)

🏗️ Projects Completed

• Linear Regression from scratch
• Titanic classification
• Data preprocessing pipeline

📈 Month 2 Progress

🧠 Deep Learning Concepts

• Neural Network Fundamentals (/7 days)
• CNN Architecture (/6 days)
• RNN & LSTM (/5 days)
• Transfer Learning (/4 days)
• Transformers Intro (/5 days)

🚀 Projects Completed

• Image Classifier (CIFAR-10/Fashion-MNIST)
• Sentiment Analysis Model
• Model Deployment (Streamlit/HF Spaces)

📈 Month 3 Progress

🤖 GenAI Mastery

• Embeddings & Vector DBs (/5 days)
• RAG Implementation (/6 days)
• Prompt Engineering (/4 days)
• Fine-tuning Techniques (/5 days)
• Inference Optimization (/4 days)

🏆 Capstone Projects

• RAG Chatbot with Custom Knowledge Base
• Text-to-Image Generation App
• AI Resume Analyzer

📈 DSA Progress (90 Days)

📊 Overall Statistics

• Total Problems Solved: ___/180 (Target: 2 per day)
• Easy Problems: ___/60
• Medium Problems: ___/90
• Hard Problems: ___/30

🎯 Topic-wise Progress

Topic	Problems Solved	Target	Status
Arrays & Strings	___/30	30	⏳
Linked Lists	___/15	15	⏳
Trees & Graphs	___/35	35	⏳
Dynamic Programming	___/25	25	⏳
Sorting & Searching	___/20	20	⏳
Stack & Queue	___/15	15	⏳
System Design	___/10	10	⏳

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🎯 Final Projects Portfolio

By the end of 90 days, your GitHub should showcase:

🏆 Core ML Projects

1. 📊 ML Algorithms from Scratch - Implementation of core algorithms without libraries
2. 🎯 Predictive Analytics Dashboard - End-to-end ML pipeline with visualization
3. 🧹 Data Preprocessing Toolkit - Reusable data cleaning and feature engineering modules

🧠 Deep Learning Projects

1. 🖼️ Computer Vision Suite - Image classification, object detection, style transfer
2. 📝 NLP Applications - Sentiment analysis, text summarization, named entity recognition
3. 🚀 Model Deployment Pipeline - Containerized models with REST APIs

🤖 GenAI Applications

1. 💬 Intelligent Chatbot - RAG-powered conversational AI with custom knowledge base
2. 🎨 Creative AI Tools - Text-to-image generation with customizable parameters
3. 📄 Document Intelligence - AI-powered document analysis and insights

📱 Bonus Projects

• 🔍 AI-Powered Job Matcher - Resume analysis with job recommendation engine
• 📈 Stock Price Predictor - Time series forecasting with multiple models
• 🎵 Music Recommendation System - Collaborative filtering with deep learning

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📚 Additional Resources

📖 Essential Books

Book	Author	Focus	Level
Hands-On Machine Learning	Aurélien Géron	Practical ML	Beginner-Intermediate
The Elements of Statistical Learning	Hastie, Tibshirani, Friedman	Theory	Advanced
Deep Learning	Ian Goodfellow	DL Theory	Intermediate-Advanced
Pattern Recognition and ML	Christopher Bishop	Math Foundation	Advanced
Introduction to Statistical Learning	James, Witten, Hastie, Tibshirani	Statistics	Beginner-Intermediate

🎥 YouTube Channels

Channel	Focus	Best For
3Blue1Brown	Math Intuition	Neural Networks, Linear Algebra
StatQuest	Statistics	ML Concepts, Statistics
Two Minute Papers	Research	Latest AI Research
Sentdex	Python ML	Practical Implementation
AI Engineering	Engineering	Production ML Systems

🛠️ Tools & Frameworks

Programming & Data Science

# Core Libraries
numpy==1.24.3
pandas==2.0.3
matplotlib==3.7.1
seaborn==0.12.2
scikit-learn==1.3.0

# Deep Learning
tensorflow==2.13.0
torch==2.0.1
transformers==4.30.2

# GenAI & LLMs
langchain==0.0.240
openai==0.27.8
chromadb==0.4.2

# Deployment
streamlit==1.25.0
gradio==3.39.0
fastapi==0.100.1

Development Environment

• IDE: VS Code, PyCharm, Jupyter Lab
• Version Control: Git, GitHub
• Cloud Platforms: Google Colab, Kaggle Kernels, AWS SageMaker
• Containerization: Docker, Kubernetes

🎯 Interview Preparation

Technical Interview Topics

1. Machine Learning

   • Algorithm explanations and trade-offs
   • Model selection and evaluation
   • Feature engineering and selection
   • Handling imbalanced datasets

2. Deep Learning

   • Neural network architectures
   • Training optimization techniques
   • Computer vision and NLP applications
   • Model interpretability

3. System Design

   • ML system architecture
   • Model serving and scaling
   • Data pipelines and MLOps
   • A/B testing for ML models

4. Coding & DSA

   • Array and string manipulation
   • Graph and tree algorithms
   • Dynamic programming
   • ML algorithm implementation

Mock Interview Resources

• Pramp - Free peer-to-peer practice
• InterviewBit - Technical interview prep
• LeetCode Mock Interviews - Algorithm practice
• Glassdoor - Company-specific questions

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🤝 Contributing

Found this roadmap helpful? Here's how you can contribute:

🌟 Ways to Contribute

• ⭐ Star this repository if it helped you
• 🐛 Report issues or suggest improvements
• 📝 Add resources you found useful
• 🎯 Share your progress and inspire others
• 📚 Contribute projects or code examples

📝 Contribution Guidelines

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-resource)
3. Commit your changes (git commit -m 'Add amazing resource')
4. Push to the branch (git push origin feature/amazing-resource)
5. Open a Pull Request

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🚀 Ready to Start Your Journey?

Remember: Consistency beats intensity!

"The best time to plant a tree was 20 years ago. The second best time is now."

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📫 Connect & Share Your Progress

⭐ Star this repo if it helped you | 🔄 Share with aspiring AI/ML Engineers

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