AI Learning Paths

Find curated learning paths for all levels, from beginner to expert.

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Beginner
Start Here
Intermediate
Build Skills
Advanced
Deepen Expertise
Expert
Lead & Innovate

Beginner

  • Understand the foundations of AI and machine learning, including key terminology, history, and real-world impact.
  • Learn Python programming with a focus on data science workflows, libraries (NumPy, Pandas), and basic algorithms.
  • Explore simple, hands-on projects such as predicting Titanic survival and recognizing handwritten digits, building intuition for model training and evaluation.
  • Get familiar with essential tools: Jupyter Notebooks, Google Colab, and basic data visualization techniques.

Featured Projects

Titanic Survival Predictor

Build a model to predict survival on the Titanic dataset, following the official Kaggle competition and documentation.

Kaggle Titanic Competition
Handwritten Digit Recognition

Classify digits using the MNIST dataset, referencing the official Yann LeCun MNIST page and TensorFlow tutorial.

Yann LeCun MNIST • TensorFlow Tutorial

Career Roadmap

  • AI Research Intern: Assist with data collection, annotation, and basic model experiments.
  • Junior ML Engineer: Support project teams with data preparation and simple model deployment.

Intermediate

  • Dive deeper into supervised and unsupervised learning, including regression, classification, clustering, and dimensionality reduction.
  • Master model evaluation, hyperparameter tuning, and cross-validation to improve performance and reliability.
  • Work on projects like sentiment analysis and image classification, applying NLP and computer vision techniques with popular frameworks (scikit-learn, Keras).
  • Learn to interpret model results, visualize data distributions, and communicate findings effectively.

Featured Projects

Sentiment Analysis

Analyze sentiment in text using the IMDb dataset and official NLTK documentation.

IMDb Dataset • NLTK Sentiment Guide
Image Classification

Classify images using CIFAR-10 and official Keras documentation for CNNs.

CIFAR-10 Dataset • Keras Example

Career Roadmap

  • ML Engineer: Build, test, and deploy models for real-world applications.
  • Data Analyst: Extract insights from data, create reports, and support decision-making.

Advanced

  • Master deep learning concepts: neural networks, CNNs, RNNs, and transformers for complex tasks in vision, language, and generative modeling.
  • Apply transfer learning and fine-tuning to leverage pre-trained models for new domains and datasets.
  • Develop advanced projects such as object detection and text generation, integrating state-of-the-art architectures and evaluation metrics.
  • Optimize models for speed, accuracy, and scalability, and learn best practices for reproducible research.

Featured Projects

Object Detection

Detect objects in images using the official YOLO (You Only Look Once) documentation and COCO dataset.

COCO Dataset • YOLO Docs
Text Generation

Generate text using Hugging Face Transformers and the official OpenAI GPT-2 documentation.

Hugging Face Guide • OpenAI GPT-2

Career Roadmap

  • AI Scientist: Design and experiment with novel architectures and algorithms.
  • Senior ML Engineer: Lead technical projects, mentor teams, and drive innovation.

Expert

  • Lead research, innovation, and strategy in AI, driving breakthroughs in generative models, reinforcement learning, and explainable AI.
  • Explore cutting-edge topics: generative adversarial networks, RL for autonomous systems, and XAI for transparency and trust.
  • Develop custom architectures and apply AI for social good, tackling global challenges in health, environment, and ethics.
  • Publish research, present at conferences, and contribute to open-source communities and standards.

Featured Projects

Custom AI Architectures

Design and implement novel neural network architectures, referencing arXiv for the latest research and official PyTorch documentation for implementation.

arXiv Recent • PyTorch Docs
AI for Social Good

Apply AI to solve real-world problems in health, environment, and society, referencing Google AI for Social Good and the World Health Organization's AI initiatives.

Google AI for Social Good • WHO AI Initiatives

Career Roadmap

  • AI Research Lead: Guide teams, set vision, and publish influential work.
  • AI Product Manager: Shape strategy, drive adoption, and ensure ethical AI deployment.

Your Goal-Driven AI Journey

A personalized roadmap to help you discover your purpose, build real projects, connect with the AI community, and shape the future. Inspired by top learning paths, tailored for Everything AI.

Find Your AI Purpose

Explore how AI can solve problems you care about. Define your motivation and set clear, meaningful goals.

Explore Concepts

Build Real Projects

Apply your skills by building hands-on projects. Start simple, then tackle real-world challenges as you grow.

See Projects

Join the AI Community

Connect, share, and learn with others. Attend events, join discussions, and collaborate to accelerate your growth.

Find Events

Shape the Future

Innovate, lead, and make an impact. Use AI ethically and creatively to solve global challenges and inspire others.

Learn Ethics