Become an AI Engineer

Welcome to your all-in-one roadmap to become a professional AI Engineer. This beginner-friendly course takes you step by step through the core skills, tools, and projects needed to enter the world of Artificial Intelligence — with no prior experience required.

📌 Course Highlights

  • Level: Beginner to Advanced
  • Format: Blog series → PDF guide → Video tutorials
  • Duration: Self-paced
  • Access: 100% Free
  • Final Outcome: Become job-ready with portfolio projects and deployable AI apps

🎯 What You’ll Achieve

By the end of this course, you will:

✅ Understand how AI works from the ground up
✅ Build machine learning and deep learning models
✅ Use real-world datasets to solve business problems
✅ Work with powerful tools like TensorFlow, Hugging Face & Streamlit
✅ Deploy your AI projects like a pro
✅ Showcase your work in a GitHub portfolio

🧠 Skills You Will Learn

  • Python programming for data and AI
  • Math for machine learning: Linear Algebra, Statistics, Probability
  • Supervised & unsupervised ML algorithms
  • Deep learning (MLPs, CNNs) with TensorFlow/Keras
  • Natural Language Processing (NLP) with scikit-learn & Hugging Face
  • Working with LLMs (GPT, BERT, etc.)
  • Model deployment using Streamlit and FastAPI
  • Portfolio building and career preparation

🗂 Course Structure

The course is divided into 5 main modules. Each topic will be released as an individual blog post, along with code, explanation, and assignments.

🟢 Module 1: Foundation of AI

  1. What is an AI Engineer? Career Path, Skills & Tools
  2. Python Basics for AI (with NumPy, Pandas, Features, Labels & Models Explained)
  3. ✅ Math Essentials: Linear Algebra, Probability, Statistics
  4. ✅ Setting up Tools: Jupyter, Colab, GitHub, VSCode

🔵 Module 2: Machine Learning

  1. ✅ Introduction to Machine Learning & Types
  2. ✅ Your First ML Project: House Price Prediction
  3. ✅ Classification Models: Logistic Regression, Decision Trees
  4. ✅ Evaluation Metrics: Accuracy, Precision, Recall, F1

🔴 Module 3: Deep Learning

  1. ✅ Neural Networks (Perceptron, Activation, Backprop)
  2. ✅ Build a Neural Net with TensorFlow/Keras
  3. ✅ Computer Vision with CNNs (MNIST, CIFAR-10)
  4. ✅ Overfitting, Dropout, Regularization & Model Tuning

🟣 Module 4: NLP & Large Language Models

  1. ✅ Basics of NLP: Text Preprocessing & Tokenization
  2. ✅ Sentiment Analysis Project with scikit-learn
  3. ✅ Introduction to Transformers & GPT-style Models
  4. ✅ Hands-on with Hugging Face & LLM-based Text Generation

⚫ Module 5: Deployment & Career

  1. ✅ Deploy AI Models with Streamlit and FastAPI
  2. ✅ Top 3 Projects: Resume Scanner, Chatbot, Vision Classifier
  3. ✅ Build Your GitHub Portfolio
  4. ✅ Resume Building, Freelancing & Interview Prep

📦 What You’ll Get

  • ✅ 20+ Full-length blog posts
  • ✅ GitHub codebase for every module
  • ✅ PDF downloads (Coming Soon)
  • ✅ YouTube video tutorials (Coming Soon)
  • ✅ Free lifetime access

🧑‍💼 Who This Course is For

  • Beginners exploring AI or data science
  • Software engineers transitioning into ML/AI
  • Entrepreneurs building AI-powered products
  • Students or job seekers preparing for AI roles

🔗 Stay Connected

📌 Bookmark this page to follow each new module
📺 Subscribe to @ImPranjalK for video tutorials
📣 Tag your projects using #AIwithPranjal on LinkedIn or GitHub

💡 Final Note

This course is crafted with simplicity, clarity, and hands-on practicality. No unnecessary theory. No fluff. Just the skills and experience you need to become a confident AI Engineer — one module at a time.

🧠 Let’s build your AI career together!

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