Artificial Intelligence & Machine Learning

Build a strong foundation in AI/ML — from Python basics and math fundamentals to neural networks, NLP, and real-world projects that prepare you for internships and jobs.

Learning Roadmap

  1. Foundations

    Python programming, NumPy, Pandas, Matplotlib, and core mathematics — linear algebra, probability, and statistics.

  2. Machine Learning Basics

    Supervised & unsupervised learning, regression, classification, and clustering with Scikit-learn.

  3. Deep Learning

    Neural networks, CNNs, RNNs, and frameworks like TensorFlow and PyTorch.

  4. Specialization

    Choose a track: Natural Language Processing, Computer Vision, or Generative AI / LLMs.

  5. Projects & Portfolio

    Build 3-5 real projects, deploy models with Flask/FastAPI, and showcase them on GitHub.

Courses & Resources

Python for AI/ML

Master Python, NumPy, and Pandas — the building blocks for every AI/ML project. Learn data manipulation, vectorized operations, and how to prep raw data for models.

BeginnerFree

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Machine Learning Fundamentals

Understand regression, classification, decision trees, and model evaluation through hands-on labs using real datasets like housing prices and customer churn.

IntermediateScikit-learn

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Deep Learning with PyTorch

Build and train neural networks, CNNs, and RNNs from scratch using PyTorch, including backpropagation, loss functions, and optimizers.

AdvancedPyTorch

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Generative AI & LLMs

Explore transformer architecture, prompt engineering, fine-tuning open-source models, and building applications powered by large language models.

AdvancedTrending

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Math for Machine Learning

Linear algebra, calculus, and probability explained intuitively with visual examples, focused on building geometric intuition for ML algorithms.

BeginnerTheory

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Computer Vision Essentials

Image classification, object detection, and OpenCV basics, with projects like face detection and image augmentation pipelines.

IntermediateOpenCV

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Project Ideas

Spam Email Classifier

Build a text classification model using Naive Bayes or Logistic Regression, with TF-IDF features extracted from real email datasets.

Handwritten Digit Recognition

Train a convolutional neural network on the MNIST dataset and deploy it as a simple web app where users can draw digits.

Chatbot with LLM API

Create a domain-specific chatbot using an LLM API, a custom knowledge base, and a lightweight frontend for conversations.

Movie Recommendation Engine

Implement collaborative filtering and content-based recommendation using user ratings and movie metadata.