🧠 AI Engineering Mastery Plan [with all the resources]
This roadmap is designed to help you transition into AI roles like Machine Learning Engineer, LLM Engineer, or Applied AI Developer.
1. Foundations of AI & Machine Learning [1–2 Days]
Topics to Learn:
What is AI vs. ML vs. Deep Learning
Supervised, Unsupervised, and Reinforcement Learning
The AI workflow: Data → Model → Evaluation → Deployment
Basic Math Refresh (Linear Algebra, Probability, Statistics)
Hands-on Practice:
Implement a simple Linear Regression model from scratch using Python.
Explore AI applications (Chatbots, Recommendations, Image Recognition).
Resources:
🎥 Machine Learning with Python and Scikit-Learn – Full Course — freeCodeCamp
Watch on YouTube🎥 Machine Learning Crash Course: Intro & What’s New — Google for Developers
Watch on YouTube📘 Book: “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” — Aurélien Géron
2. Python for AI Engineers [1–2 Days]
Topics to Learn:
Python Basics (Functions, Loops, OOP, File Handling)
Libraries: NumPy, Pandas, Matplotlib, Seaborn
Data preprocessing and feature scaling
Reading/writing CSV, Excel, and JSON data
Hands-on Practice:
Clean a real-world dataset using Pandas.
Visualize feature correlations and outliers.
Resources:
🎥 Python for Data Science – Full Course — freeCodeCamp
Watch on YouTube🎥 NumPy, Pandas & Matplotlib Tutorials — Corey Schafer
Watch on YouTube📘 Kaggle Python Course
Visit Kaggle
3. Machine Learning Algorithms [2–3 Days]
Topics to Learn:
Regression: Linear, Logistic
Classification: Decision Trees, Random Forest, SVM
Clustering: K-Means, DBSCAN
Evaluation Metrics (Accuracy, Precision, Recall, F1, ROC-AUC)
Feature Engineering & Model Selection
Hands-on Practice:
Train and evaluate ML models on Kaggle datasets.
Compare accuracy using different algorithms.
Resources:
🎥 Machine Learning Full Course — freeCodeCamp
Watch on YouTube🎥 ML Algorithms Explained Simply — StatQuest
Watch on YouTube📘 Scikit-Learn Documentation & Tutorials
Visit Scikit-Learn
4. Deep Learning & Neural Networks [2–3 Days]
Topics to Learn:
Artificial Neural Networks (ANN) basics
Activation Functions, Forward & Backpropagation
CNNs for Image Data
RNNs, LSTMs for Sequence Data
Hyperparameter Tuning
Hands-on Practice:
Build an image classifier using TensorFlow/Keras.
Train a simple text classifier using an RNN.
Resources:
🎥 Deep Learning Crash Course — freeCodeCamp
Watch on YouTube🎥 Neural Networks Demystified — Welch Labs
Watch on YouTube📘 Deep Learning Specialization — Andrew Ng (Coursera)
Visit Coursera
5. Large Language Models (LLMs) & Generative AI [2 Days]
Topics to Learn:
Transformer Architecture
Pretrained Models: GPT, BERT, T5, Llama
Fine-tuning vs. Prompt Engineering
RAG (Retrieval-Augmented Generation)
Tokenization, Embeddings, and Context Windows
Hands-on Practice:
Build a chatbot using OpenAI API or Hugging Face Transformers.
Implement a document Q&A bot using RAG.
Resources:
🎥 LLMs Explained Simply — Andrej Karpathy
Watch on YouTube🎥 How Transformers Work — CodeEmporium
Watch on YouTube📘 Hugging Face Course (Free official guide)
Visit Hugging Face
6. MLOps & Model Deployment [2 Days]
Topics to Learn:
Model Saving (Pickle, Joblib, ONNX)
APIs with FastAPI / Flask
Version Control: DVC & Git
Containerization: Docker for ML
CI/CD for ML Projects
Hands-on Practice:
Deploy a model as an API using FastAPI.
Containerize it using Docker.
Optionally push to AWS Lambda / Hugging Face Spaces.
Resources:
🎥 Deploy ML Models using FastAPI + Docker — Krish Naik
Watch on YouTube🎥 MLOps Crash Course for Beginners — freeCodeCamp
Watch on YouTube📘 Made With ML by Goku Mohandas (Free online course)
Visit Made With ML
7. Cloud for AI Engineers (AWS, GCP, Azure) [1–2 Days]
Topics to Learn:
Cloud ML Services (SageMaker, Vertex AI, Azure ML Studio)
Training on cloud GPUs
Using cloud APIs for NLP, Vision, and Speech
Model Monitoring & Scaling
Hands-on Practice:
Train a small model on Google Colab GPU.
Deploy a basic model on AWS SageMaker (Free Tier).
Resources:
🎥 AWS SageMaker Full Course — freeCodeCamp
Watch on YouTube🎥 GCP Vertex AI Tutorial — Google Cloud Tech
Watch on YouTube📘 Google Cloud ML Engineer Learning Path
Visit Google Cloud
8. Applied AI Projects [2–3 Days]
Project Ideas:
AI Resume Analyzer (NLP)
Customer Review Sentiment Classifier
Image Recognition App (CNN)
Personal Chatbot with Memory (LLM + LangChain)
Sales Forecasting Dashboard
Tasks:
Choose one end-to-end project and complete it.
Include: Data cleaning, Model training, Evaluation, Deployment.
Resources:
🎥 AI Projects with Python – Full Series — Codebasics
Watch on YouTube🎥 LangChain End-to-End Projects — Prompt Engineering YouTube
Watch on YouTube🧰 Datasets: Kaggle, Hugging Face Datasets
Visit Kaggle
9. AI Engineering Tools & Ecosystem
Essential Tools to Master:
IDEs: VS Code, Jupyter, Google Colab
Libraries: NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch
ML Platforms: Hugging Face, LangChain, OpenAI
Workflow Tools: MLflow, DVC, Airflow
10. Portfolio, Resume & Interview Prep [1–2 Days]
Topics to Learn:
Building an AI Engineer Resume (highlight projects, tools, outcomes)
GitHub Portfolio Setup
Interview prep: ML theory, system design for AI, case studies
Mock interviews
Resources:
🎥 AI Engineer Resume + Portfolio Review — Krish Naik
Watch on YouTube🎥 Machine Learning Interview Questions — Simplilearn
Watch on YouTube📘 Book: “Ace the Data Science Interview”
Visit Amazon
11. Bonus: AI Agents & Automation
Topics to Learn:
What are AI Agents
Frameworks: LangGraph, CrewAI, AutoGen, OpenDevin
Orchestration & Memory Management
Multi-agent Collaboration
Hands-on Practice:
Build an AutoGPT-style assistant using LangChain + CrewAI.
Connect APIs (Google Search, Slack, Notion).
Resources:
🎥 LangChain AI Agents Crash Course — freeCodeCamp
Watch on YouTube🎥 CrewAI & LangGraph Tutorials — Prompt Engineering YouTube
Watch on YouTube📘 LangChain Docs + OpenDevin GitHub
Visit LangChain
