100DaysOfAIEngineer

🚀 100 Days of AI Engineer

A comprehensive, project-driven roadmap to becoming an AI Engineer in 100 days

⚠️ STOP! READ THIS FIRST OR GET THE FUCK OUT ⚠️

Listen up, aspiring AI Engineer!

I didn’t spend countless hours researching, curating 150+ blog articles, creating 100 detailed daily checklists, and designing 7 production-grade projects for people who are “just browsing” or looking for “easy learning.”

🔥 THIS IS NOT A CASUAL COURSE. THIS IS A COMMITMENT. 🔥

Before you scroll down, answer these questions HONESTLY:

Can you dedicate 6-8 hours EVERY SINGLE DAY for 100 days?
Can you code, build projects, and debug errors without giving up?
Are you willing to share your learning publicly and hold yourself accountable?
Can you handle failure, frustration, and the steep learning curve?

⚡ IF YOU ANSWERED “NO” OR “MAYBE” TO ANY OF THESE - CLOSE THIS TAB NOW. ⚡

I’m serious. Leave. Go watch YouTube tutorials. Do easy Udemy courses. This isn’t for you.

💀 THE BRUTAL TRUTH ABOUT THIS CHALLENGE:

❌ No hand-holding - You’ll get stuck. You’ll debug for hours. That’s the point.
❌ No participation trophies - Checking boxes without understanding = FAILURE.
❌ No excuses - “I was busy” = You weren’t serious from the start.
✅ 100% commitment - Miss a day? START OVER. Yes, I’m that hardcore.
✅ Public accountability - Share EVERY DAY on social media or you’re not doing this right.
✅ Real projects - Build production-grade systems, not toy examples.

🎯 THE CHALLENGE - ACCEPT OR GTFO:

I WILL:

Code for 6-8 hours EVERY DAY for 100 consecutive days (NO EXCEPTIONS)
Complete ALL coding tasks, exercises, and projects for each day
Share my progress publicly EVERY SINGLE DAY using #100DaysOfAIEngineer
Push my code to GitHub daily (no matter how ugly it is)
Help others in the community when I can
NOT give up when shit gets hard (because it WILL get hard)
Build all 7 major projects from scratch
Document my learning journey publicly

I WILL NOT:

Skip a single day
Copy-paste code without understanding
Make excuses
Quit when it gets difficult
Stay silent - I will engage with the community
Half-ass any project or exercise

🔥 STILL HERE? GOOD. YOU MIGHT ACTUALLY MAKE IT.

Here’s what separates winners from wannabes:

WINNERS build in public, embrace the grind, show up every day, help others, and become job-ready AI Engineers.

WANNABES read day 1, get excited, quit by day 7, blame “lack of time,” and never build anything real.

📢 YOUR FIRST ACTION (DO THIS RIGHT NOW):

Post this on Twitter/LinkedIn/Instagram:

I'm committing to #100DaysOfAIEngineer starting TODAY.

100 days. No excuses. No skipping.
- 6-8 hours daily
- Public learning
- Real projects
- Building in public

Repo: [Your fork link]

Day 1 starts NOW. Who's with me? 🔥

#100DaysOfCode #MachineLearning #AI #LearningInPublic

Tag 3 friends who you think have the GUTS to do this with you.

🏆 WHAT YOU’LL BECOME IF YOU ACTUALLY FINISH:

After 100 days of this brutal, unforgiving, kick-your-ass curriculum, you’ll:

Build ML models from scratch in NumPy (not just using libraries like a script kiddie)
Deploy production LLM applications with RAG, vector databases, and proper MLOps
Implement neural networks, CNNs, transformers from first principles
Have a portfolio with 7 production-grade projects that’ll make recruiters drool
Understand AI/ML at a level that 90% of “AI Engineers” don’t
Be able to pass technical interviews at top tech companies
Actually DESERVE to call yourself an AI Engineer

💰 THE PAYOFF:

Entry-level AI Engineer salary: $80k-120k
Skills that companies are desperately hiring for RIGHT NOW
Portfolio that stands out from the “I completed a Coursera course” crowd
Confidence to build ANY AI system from scratch

⚔️ THE FINAL WARNING:

This curriculum was designed by someone who gives a shit about your SUCCESS, not your COMFORT.

If you’re looking for easy: This isn’t it. Leave now. If you’re looking for quick: This isn’t it. Leave now. If you’re not willing to suffer a little: This DEFINITELY isn’t it. LEAVE NOW.

BUT…

If you’re ready to transform yourself. If you’re hungry to actually BECOME an AI Engineer, not just “learn about AI.” If you’re willing to embrace the suck for 100 days to change your life.

THEN SCROLL DOWN AND START DAY 1.

📊 TRACK RECORD (Will Be Updated):

People who started: [TBD] People who quit in week 1: [TBD] People who made it to day 30: [TBD] People who finished all 100 days: [TBD] People who got hired as AI Engineers: [TBD]

Will you be in the “finished” column or the “quit” column?

🎯 READY? PROVE IT.

Click here to start: Day 1 Checklist

Fork this repo. Share your commitment. Begin.

The world doesn’t need more people who “tried” AI. It needs people who BECAME AI Engineers.

NOW GET THE FUCK TO WORK. 💪🔥

P.S. - If this notice offended you, good. You probably weren’t ready anyway. If this fired you up, PERFECT. You’re exactly who this is for. See you at Day 100. 🎯

🌐 JOIN THE CODERCOPS COMMUNITY - YOUR SUCCESS STARTS HERE

You CAN’T do this alone. And you don’t have to.

🎯 CODERCOPS Discord Server - REQUIRED for Success

Join NOW: https://discord.gg/9eFXYntYa8

Why Discord is MANDATORY:

✅ Daily accountability - Post your progress EVERY DAY
✅ Get unstuck FAST - Community answers in minutes, not hours
✅ Code reviews - Get feedback from other learners and mentors
✅ Stay motivated - See others crushing it, get inspired
✅ Network - Connect with future AI Engineers and potential employers
✅ Peer pressure (the good kind) - Your streak is PUBLIC

📢 Key Channels:

/checkin - Run it EVERY DAY and pick “100 Days of AI Engineer”. The bot logs your day in your own thread and tracks your streak
#100daysofaiengineer forum - Your log thread, plus project showcases, code reviews and detailed discussions
#roadmaps - How check-ins work, and the other CODERCOPS roadmaps

📅 What You’ll Post Daily:

Day X/100 ✅
Topic: [What you learned]
Code: [GitHub link]
Progress: [What you built]
#100DaysOfAIEngineer

⚠️ If you’re not in Discord, you’re NOT doing the challenge properly.

📱 Follow CODERCOPS - Stay Connected & Inspired

All platforms: @CODERCOPS

🐦 Twitter/X: https://twitter.com/CODERCOPS - Daily AI tips, community wins
💼 LinkedIn: https://linkedin.com/company/CODERCOPS - Professional updates, job posts
📸 Instagram: https://instagram.com/CODERCOPS - Visual progress, motivation
🎥 YouTube: https://youtube.com/@CODERCOPS - Tutorials, project walkthroughs
💻 GitHub: https://github.com/CODERCOPS - Open source projects, resources

Follow all platforms. Engage. Tag @CODERCOPS in your posts. Build in public.

🔥 First Steps RIGHT NOW:

Join Discord: https://discord.gg/9eFXYntYa8
Introduce yourself in #introductions
Follow @CODERCOPS on all platforms
Post your commitment on social media (template above)
Start Day 1 (link below), then run /checkin in Discord

Related Docs:

📘 COMMUNITY.md - How to engage in Discord
📜 COMMUNITY_GUIDELINES.md - Server rules
📊 ACCOUNTABILITY.md - Daily posting requirements

📋 Overview

This is a structured 100-day program designed to transform you from a Python developer into a skilled AI Engineer. The curriculum is project-focused, hands-on, and covers the modern AI stack used in production environments.

🎯 Daily Checklists Directory - Track your progress day by day!

Each day includes:

✅ Checklist format - Mark tasks as you complete them
✅ Learning objectives - Clear daily goals
✅ Coding tasks - Specific implementations
✅ Social media templates - Ready-to-post updates for Twitter, LinkedIn, Instagram
✅ Reflection prompts - Document your journey

Why share on social media?

📢 Accountability through public commitment
🤝 Connect with other learners (#100DaysOfAIEngineer)
💼 Build your professional brand
📊 Track your progress publicly
🎯 Stay motivated through community support

👉 Start with Day 1 Checklist

What You’ll Build

7 Major Real-World Projects
15+ Mini Projects
Production-ready ML/AI applications
Full MLOps pipeline
LLM-powered applications

Prerequisites

✅ Python programming knowledge
✅ Basic understanding of programming concepts
✅ Willingness to code daily
✅ 6-8 hours daily commitment

🎯 Learning Path

Phase 1: Foundations & Classical ML (Days 1-15)
Phase 2: Deep Learning Fundamentals (Days 16-30)
Phase 3: Computer Vision (Days 31-45)
Phase 4: Natural Language Processing (Days 46-60)
Phase 5: LLMs & Modern NLP (Days 61-75)
Phase 6: MLOps & Production (Days 76-85)
Phase 7: Capstone & Advanced Topics (Days 86-100)

📚 Phase 1: Foundations & Classical ML (Days 1-15)

Goal: Master data manipulation, classical ML algorithms, and build your first ML pipeline

Week 1: Python for AI & Data Science

Day 1-2: NumPy Mastery

Array operations, broadcasting, vectorization
Linear algebra operations (dot products, matrix multiplication)
Random sampling and statistical operations
Exercise: Implement matrix operations from scratch
Mini Project: Build a simple image filter using NumPy

Day 3-4: Pandas for Data Manipulation

DataFrames, Series, indexing
Data cleaning, handling missing values
Groupby, pivot tables, merging
Exercise: Analyze a real dataset (Kaggle dataset)
Mini Project: COVID-19 data analysis dashboard

Day 5-6: Data Visualization

Matplotlib, Seaborn, Plotly
Statistical plots, distributions
Interactive visualizations
Mini Project: Exploratory Data Analysis (EDA) on Titanic dataset

Day 7: Mathematics for ML

Linear algebra review (vectors, matrices, eigenvalues)
Calculus basics (derivatives, gradients)
Probability and statistics fundamentals
Exercise: Implement gradient descent from scratch

Week 2: Classical Machine Learning

Day 8-9: Supervised Learning - Regression

Linear regression (theory + implementation from scratch)
Polynomial regression, regularization (Ridge, Lasso)
Gradient descent optimization
Exercise: Predict house prices using linear regression
Code: Implement linear regression without sklearn

Day 10-11: Supervised Learning - Classification

Logistic regression
Decision trees and Random Forests
Support Vector Machines (SVM)
Exercise: Binary and multi-class classification problems
Mini Project: Spam email classifier

Day 12-13: Unsupervised Learning

K-Means clustering
Hierarchical clustering
Principal Component Analysis (PCA)
DBSCAN
Mini Project: Customer segmentation using clustering

Day 14: Model Evaluation & Feature Engineering

Cross-validation, train-test split
Metrics: Accuracy, Precision, Recall, F1, ROC-AUC
Feature scaling, encoding categorical variables
Handling imbalanced datasets
Exercise: Compare multiple models on a dataset

Day 15: 🎯 PROJECT 1 - End-to-End ML Pipeline

Build: Complete ML pipeline for a real-world problem
Data collection and cleaning
Feature engineering and selection
Model training, evaluation, hyperparameter tuning
Deliverable: Jupyter notebook with full pipeline
Suggested: Predict customer churn or credit card fraud detection

🧠 Phase 2: Deep Learning Fundamentals (Days 16-30)

Goal: Understand neural networks and implement deep learning models

Week 3: Neural Networks from Scratch

Day 16-17: Neural Network Fundamentals

Perceptron, activation functions
Forward propagation
Loss functions (MSE, Cross-entropy)
Exercise: Implement a perceptron from scratch

Day 18-19: Backpropagation

Chain rule and backpropagation algorithm
Gradient descent variants (SGD, Momentum, Adam)
Exercise: Implement backpropagation from scratch
Code: Build a 2-layer neural network without frameworks

Day 20-21: Introduction to PyTorch

Tensors, autograd, computational graphs
Building models with nn.Module
Training loops, optimizers
Exercise: Reimplement Day 19 network in PyTorch
Mini Project: MNIST digit classification

Day 22: Regularization & Optimization

Dropout, Batch Normalization, L1/L2 regularization
Learning rate scheduling
Early stopping
Exercise: Prevent overfitting on a deep network

Week 4: Advanced Deep Learning

Day 23-24: Convolutional Neural Networks (CNNs) - Part 1

Convolution operation, pooling
CNN architectures (LeNet, AlexNet)
Exercise: Visualize filters and feature maps
Code: Build a simple CNN for image classification

Day 25-26: CNNs - Part 2 & Transfer Learning

Modern architectures (VGG, ResNet, EfficientNet)
Transfer learning and fine-tuning
Data augmentation
Mini Project: Fine-tune ResNet on a custom dataset

Day 27-28: Recurrent Neural Networks (RNNs)

Sequence modeling, RNN architecture
LSTM and GRU
Vanishing gradient problem
Exercise: Time series prediction with LSTM
Mini Project: Stock price prediction

Day 29: Handling Real-World Data

Data preprocessing pipelines
DataLoaders and Dataset classes in PyTorch
Handling large datasets
Exercise: Build efficient data pipelines

Day 30: 🎯 PROJECT 2 - Image Classification System

Build: End-to-end image classifier with web interface
Custom dataset creation and preprocessing
Train CNN from scratch + transfer learning comparison
Model evaluation and visualization
Deliverable: Streamlit/Gradio app for image classification
Suggested: Plant disease classifier or animal species identifier

👁️ Phase 3: Computer Vision (Days 31-45)

Goal: Master computer vision techniques and build production-ready CV applications

Week 5: Advanced Computer Vision

Day 31-32: Object Detection - Part 1

R-CNN family (R-CNN, Fast R-CNN, Faster R-CNN)
YOLO (You Only Look Once) architecture
Exercise: Understand anchor boxes and IoU
Code: Implement basic object detection with YOLOv5

Day 33-34: Object Detection - Part 2

YOLOv8, DETR (Detection Transformer)
Non-maximum suppression (NMS)
mAP (mean Average Precision) metric
Mini Project: Real-time object detection with webcam

Day 35-36: Semantic Segmentation

U-Net architecture
Mask R-CNN
DeepLab
Exercise: Image segmentation on medical images
Mini Project: Background removal tool

Day 37: Instance Segmentation & Pose Estimation

Instance segmentation with Mask R-CNN
Human pose estimation (OpenPose, MediaPipe)
Mini Project: Pose-based fitness rep counter

Week 6: Advanced CV Techniques

Day 38-39: Generative Models - Part 1

Autoencoders and Variational Autoencoders (VAE)
Image denoising and reconstruction
Exercise: Build an autoencoder for MNIST
Mini Project: Image denoising application

Day 40-41: Generative Models - Part 2 (GANs)

Generative Adversarial Networks (GANs)
DCGAN, StyleGAN basics
GAN training challenges
Mini Project: Generate synthetic images

Day 42-43: Modern CV Techniques

Vision Transformers (ViT)
CLIP (Contrastive Language-Image Pre-training)
Image captioning
Exercise: Use CLIP for zero-shot classification
Mini Project: Image search engine using CLIP

Day 44: Model Optimization for CV

Model quantization and pruning
ONNX export
TensorRT optimization
Exercise: Optimize a model for edge deployment

Day 45: 🎯 PROJECT 3 - Smart Surveillance System

Build: Real-time object detection and tracking system
Multiple object tracking (MOT)
Alert system for specific objects/behaviors
Performance optimization for real-time processing
Deliverable: Real-time video analysis application
Suggested: People counter, vehicle detection, or safety monitoring

💬 Phase 4: Natural Language Processing (Days 46-60)

Goal: Master NLP fundamentals and build text-based AI applications

Week 7: NLP Fundamentals

Day 46-47: Text Preprocessing & Feature Engineering

Tokenization, stemming, lemmatization
Stop words removal, text cleaning
Bag of Words, TF-IDF
Exercise: Build a text preprocessing pipeline
Mini Project: Document similarity finder

Day 48-49: Word Embeddings

Word2Vec (CBOW, Skip-gram)
GloVe, FastText
Embedding visualization (t-SNE)
Exercise: Train custom word embeddings
Mini Project: Word analogy solver (king - man + woman = queen)

Day 50-51: Text Classification

Sentiment analysis
Naive Bayes, Logistic Regression for text
Deep learning for text (CNN, LSTM)
Mini Project: Movie review sentiment classifier
Exercise: Multi-label text classification

Day 52: Named Entity Recognition (NER)

NER with spaCy
Custom NER models
Mini Project: Extract entities from news articles

Week 8: Sequence Models & Attention

Day 53-54: Sequence-to-Sequence Models

Encoder-decoder architecture
Seq2Seq with attention
Exercise: Build a simple translation model
Mini Project: Text summarization tool

Day 55-56: Attention Mechanism & Transformers

Self-attention, multi-head attention
Transformer architecture deep dive
Positional encoding
Exercise: Implement self-attention from scratch
Code: Build a mini transformer

Day 57-58: Pre-trained Language Models

BERT, RoBERTa, DistilBERT
Fine-tuning BERT for classification
Feature extraction with BERT
Mini Project: Question-answering system with BERT

Day 59: Advanced NLP Tasks

Text generation
Zero-shot classification
Few-shot learning with GPT
Exercise: Experiment with Hugging Face Transformers

Day 60: 🎯 PROJECT 4 - NLP Multi-Task Application

Build: Comprehensive text analysis tool
Sentiment analysis
Named entity recognition
Text summarization
Topic modeling
Deliverable: Web API (FastAPI) with multiple NLP endpoints
Suggested: News article analyzer or social media insights tool

🤖 Phase 5: Large Language Models & Modern NLP (Days 61-75)

Goal: Master LLMs, RAG systems, and build production LLM applications

Week 9: Large Language Models

Day 61-62: Understanding LLMs

GPT architecture deep dive
Tokenization (BPE, WordPiece)
LLM training process (pre-training, fine-tuning)
Exercise: Explore GPT-2/GPT-3 API
Mini Project: Text completion app

Day 63-64: Prompt Engineering

Zero-shot, few-shot, chain-of-thought prompting
Prompt templates and optimization
In-context learning
Exercise: Build a prompt library for common tasks
Mini Project: AI assistant with optimized prompts

Day 65-66: Fine-Tuning LLMs

Full fine-tuning vs LoRA vs QLoRA
Parameter-efficient fine-tuning (PEFT)
Instruction tuning
Exercise: Fine-tune a small LLM (Flan-T5, GPT-2)
Mini Project: Domain-specific chatbot

Day 67-68: LangChain Framework

LangChain basics (chains, agents, memory)
Prompt templates and output parsers
LLM chains and sequential chains
Exercise: Build complex chains
Mini Project: Research assistant with LangChain

Week 10: RAG & Vector Databases

Day 69-70: Vector Databases & Embeddings

Embedding models (OpenAI, Sentence-Transformers)
Vector databases (Pinecone, Weaviate, ChromaDB, FAISS)
Similarity search
Exercise: Build a semantic search engine
Mini Project: Document similarity search

Day 71-72: Retrieval-Augmented Generation (RAG)

RAG architecture and workflow
Document loading, chunking strategies
Retrieval optimization
Exercise: Build a basic RAG system
Mini Project: “Chat with your PDF” application

Day 73: Advanced RAG Techniques

Hybrid search (keyword + semantic)
Re-ranking and MMR (Maximal Marginal Relevance)
Metadata filtering
Exercise: Improve RAG system accuracy

Day 74: LLM Evaluation & Safety

Evaluation metrics for LLMs
Guardrails and content filtering
Handling hallucinations
Cost optimization
Exercise: Benchmark different LLM approaches

Day 75: 🎯 PROJECT 5 - Production RAG Application

Build: ChatGPT-like application with custom knowledge base
Multi-document RAG system
Conversation memory and context management
Source attribution
Deliverable: Full-stack application (FastAPI + React/Streamlit)
Suggested: Company knowledge base chatbot or legal document Q&A

🚀 Phase 6: MLOps & Production (Days 76-85)

Goal: Learn to deploy, monitor, and maintain ML models in production

Week 11: Deployment & MLOps

Day 76-77: Model Serving & APIs

FastAPI for ML models
REST API design for ML
Request/response handling
Model versioning
Exercise: Create API endpoints for multiple models
Mini Project: Model serving API with FastAPI

Day 78: Docker for ML

Containerization basics
Docker for ML applications
Multi-stage builds
Exercise: Dockerize ML application
Mini Project: Docker Compose setup for ML app + database

Day 79: Model Optimization

Model quantization (int8, fp16)
Knowledge distillation
ONNX Runtime
Exercise: Optimize model for inference
Mini Project: Compare inference speeds

Day 80-81: ML Experiment Tracking

MLflow for experiment tracking
Weights & Biases (W&B)
Model registry
Exercise: Track experiments for a model
Mini Project: Complete ML experiment pipeline

Day 82: Model Monitoring & Observability

Model drift detection
Data drift monitoring
Logging and alerting
Exercise: Set up monitoring dashboard
Tools: Evidently AI, WhyLabs

Day 83: CI/CD for ML

GitHub Actions for ML
Automated testing for ML code
Continuous training
Exercise: Build CI/CD pipeline
Mini Project: Automated model retraining pipeline

Day 84: Cloud Deployment

AWS SageMaker basics (or GCP Vertex AI)
Serverless deployment (AWS Lambda)
Scaling strategies
Exercise: Deploy model to cloud

Day 85: 🎯 PROJECT 6 - Production ML System

Build: End-to-end production ML system
Model training pipeline
Automated deployment
Monitoring and alerting
CI/CD integration
Deliverable: Fully deployed, monitored ML application
Suggested: Real-time recommendation system or fraud detection

🏆 Phase 7: Capstone & Advanced Topics (Days 86-100)

Goal: Build a comprehensive AI project and explore cutting-edge topics

Week 12-13: Advanced Topics

Day 86-87: Multi-Modal AI

Vision-language models (CLIP, BLIP)
Audio processing (Whisper)
Multi-modal applications
Mini Project: Image captioning or visual question answering

Day 88-89: AI Agents & LangGraph

Autonomous agents
ReAct framework
Tool use with LLMs
Mini Project: AI agent that can browse and use tools

Day 90-91: Reinforcement Learning Basics

MDP, Q-learning basics
Policy gradients
RL for practical applications
Exercise: Train an agent in a simple environment
Mini Project: Game-playing AI

Day 92-93: Advanced Generative AI

Stable Diffusion and image generation
ControlNet, LoRA for Stable Diffusion
Text-to-image applications
Mini Project: AI art generator

Day 94-100: 🎯 CAPSTONE PROJECT - Full-Stack AI Application

Build a comprehensive, production-ready AI application that combines multiple concepts:

Project Ideas:

AI-Powered Content Platform
- Text generation, image generation
- RAG-based Q&A
- Content moderation
- User analytics
Intelligent Personal Assistant
- Voice input (Whisper)
- Multi-turn conversations with memory
- Tool use (calendar, email, web search)
- Task automation
AI-Powered Healthcare Assistant
- Medical image analysis
- Symptom checker with RAG
- Health record summarization
- Privacy-preserving design
Smart Education Platform
- Personalized learning paths
- Auto-grading with explanations
- Interactive tutoring chatbot
- Progress tracking

Requirements:

Frontend (React/Vue or Streamlit)
Backend API (FastAPI)
Multiple AI models (CV + NLP + LLM)
Database integration
Authentication & authorization
Docker deployment
Monitoring and logging
Documentation

Deliverables:

Complete codebase on GitHub
Deployed application
Technical documentation
Demo video
Blog post explaining architecture

🛠️ Tech Stack & Tools

Essential Tools

Languages: Python
DL Frameworks: PyTorch, TensorFlow/Keras
ML Libraries: scikit-learn, XGBoost, LightGBM
NLP: Hugging Face Transformers, spaCy, NLTK
LLM: OpenAI API, LangChain, LlamaIndex
Vector DBs: ChromaDB, FAISS, Pinecone
Computer Vision: OpenCV, torchvision, timm
Data: NumPy, Pandas, Polars
Visualization: Matplotlib, Seaborn, Plotly
MLOps: MLflow, Weights & Biases, DVC
Deployment: FastAPI, Docker, AWS/GCP
Version Control: Git, GitHub

Development Environment

# Create conda environment
conda create -n ai-engineer python=3.10
conda activate ai-engineer

# Install core packages
pip install torch torchvision torchaudio
pip install transformers datasets
pip install langchain openai chromadb
pip install fastapi uvicorn
pip install mlflow wandb
pip install streamlit gradio
pip install scikit-learn pandas numpy matplotlib seaborn

📊 Progress Tracking

Create a daily log:

## Day X: [Topic]

### What I Learned
- Key concept 1
- Key concept 2

### Code Implemented
- [Link to code/notebook]

### Challenges Faced
- Challenge and how I solved it

### Resources Used
- Tutorial/article links

### Tomorrow's Goal
- What I plan to learn next

🎓 Learning Resources

📝 Blog Articles (NEW!)

🔥 BLOG_ARTICLES.md - 150+ Curated Blog Posts

We’ve researched and compiled 150+ high-quality blog articles from trusted sources for every topic in the curriculum:

✅ Organized by Phase: Articles matched to each day’s learning
✅ 2024-2025 Content: Latest tutorials and best practices
✅ Code Examples: All include practical implementations
✅ Verified Quality: Hand-picked from top platforms (Medium, Towards Data Science, official docs)

Topics Include: NumPy, Pandas, ML algorithms, PyTorch, CNNs, YOLO, NLP, BERT, Transformers, LLMs, Fine-tuning, RAG, LangChain, Vector Databases, MLOps, Docker, and more!

👉 See BLOG_ARTICLES.md for the complete collection

Also check RESOURCES.md for comprehensive books, courses, tools, and platforms.

Free Courses

Fast.ai - Practical Deep Learning
Stanford CS229 - Machine Learning
Stanford CS224N - NLP with Deep Learning
DeepLearning.AI - Deep Learning Specialization
Hugging Face Course - NLP with Transformers

Books

“Hands-On Machine Learning” by Aurélien Géron
“Deep Learning” by Ian Goodfellow
“Natural Language Processing with Transformers” by Lewis Tunstall
“Designing Machine Learning Systems” by Chip Huyen

Platforms

Kaggle - Competitions and datasets
Papers with Code - Latest research
Hugging Face - Models and datasets
GitHub - Open source projects

YouTube Channels

Andrej Karpathy
StatQuest
3Blue1Brown (Math)
Yannic Kilcher (Paper reviews)

💡 Tips for Success

Code Every Day - Even 30 minutes counts
Build Projects - Theory without practice is useless
Read Research Papers - Stay updated with latest techniques
Join Communities - Reddit (r/MachineLearning), Discord servers
Document Your Journey - Blog, GitHub, LinkedIn posts
Don’t Just Tutorial Hell - Build original projects
Understand, Don’t Memorize - Focus on concepts, not code
Debug and Experiment - Break things and fix them
Review Regularly - Revisit concepts weekly
Stay Consistent - 100 days straight is better than random practice

🎯 Success Metrics

By Day 100, you should be able to:

Build and deploy ML models end-to-end
Implement neural networks from scratch
Fine-tune and deploy LLMs
Create RAG applications
Build computer vision applications
Design and implement MLOps pipelines
Read and implement research papers
Contribute to open source ML projects
Pass AI Engineer technical interviews

📁 Repository Structure

100DaysOfAIEngineer/
│
├── 📄 README.md                         # Main curriculum & overview
│
├── 📚 Learning & Resources:
│   ├── RESOURCES.md                     # Curated learning resources
│   ├── PROJECT_GUIDE.md                 # Project specifications
│   ├── BLOG_ARTICLES.md                 # 150+ curated blog posts
│   └── FAQ.md                           # Frequently asked questions
│
├── 🤝 Community & Accountability:
│   ├── COMMUNITY.md                     # CODERCOPS Discord integration
│   ├── COMMUNITY_GUIDELINES.md          # Community rules
│   ├── ACCOUNTABILITY.md                # Daily tracking system
│   ├── PEER_REVIEW_GUIDE.md             # Code review guidelines
│   └── HALL_OF_FAME.md                  # Graduate recognition
│
├── 🎯 Quality & Standards:
│   ├── QUALITY_STANDARDS.md             # Completion criteria
│   ├── ANTI_PATTERNS.md                 # Common mistakes to avoid
│   └── FAILURE_RECOVERY.md              # Restart protocols
│
├── 💼 Career Development:
│   └── JOB_HUNTING_PLAYBOOK.md          # Job search strategies
│
├── 📁 daily_checklists/                 # ⭐ CORE CURRICULUM
│   ├── day01/                           # NumPy Basics
│   │   ├── README.md                    # Daily guide with resources
│   │   ├── code/                        # Your code here
│   │   ├── notebooks/                   # Jupyter notebooks
│   │   └── notes.md                     # Personal notes
│   ├── day02/                           # Advanced NumPy
│   ├── day03/                           # Pandas Fundamentals
│   │   └── ...
│   └── day100/                          # 🎉 Celebration & Reflection
│
└── 📁 weekly_reviews/                   # Weekly reflection & planning
    ├── week01/
    ├── week02/
    │   └── ...
    └── week14/

How to use this repository:

Start here: Read this README completely
Join community: COMMUNITY.md - Discord is REQUIRED
Begin Day 1: daily_checklists/day01/
Track progress: Update your daily README, post in Discord
Review weekly: Complete weekly reflections in weekly_reviews/
Build projects: Push your code to each day’s directory
Stay accountable: Daily Discord posts, 3x/week social media

🤝 Contributing

Found an error or want to improve the curriculum? Feel free to:

Open an issue
Submit a pull request
Share your progress and projects

📜 License

MIT License - Feel free to use and adapt this roadmap for your learning journey!

🚀 Let’s Begin!

Start with Day 1 and commit to the journey. Remember: Consistency beats intensity.

Your AI Engineering journey starts now! 🎉

Created with ❤️ for aspiring AI Engineers

Last Updated: 2025