AutoML Tutorials

Master the art of automated machine learning and decentralized AI through our comprehensive learning path.

22 Weeks Total

Comprehensive learning journey from basics to advanced AutoML

Community-Driven

Learn alongside fellow researchers and practitioners

Hands-On Projects

Real challenges and practical applications throughout

Learning Phases

Phase 1: Foundations

ML fundamentals, intuition building, and practical hands-on projects

2 Weeks
1
Week 1

ML Fundamentals & Intuition

  • The ML Mindset – problem‑first framing, data exploration
  • Linear & Logistic Regression – Titanic challenge, AUC‑ROC, error trade‑offs
  • ML Landscape & Taxonomy – supervised vs unsupervised, algorithm personalities
  • Optimization & Learning Theory – gradient descent game, No Free Lunch intro
Week 2

Practical ML & Initial Project

  • Feature Engineering & Data Prep – competition & "feature detective"
  • Model Evaluation & Validation – cross‑validation, model court simulation
  • Kolmogorov Corner – algorithmic information theory & compression challenge
  • ML Ethics & Real Stakes – demilitarization, "hard choices" simulation
  • Capstone: Build‑a‑Baseline — churn, sentiment transfer, anomaly detection

Phase 2: Deep Learning & Transformers

Neural networks, specialized architectures, and modern transformer systems

10 Weeks
2
Weeks 1‑2

Neural Network Fundamentals

  • NumPy NN from scratch, backpropagation, optimization olympics
Weeks 3‑4

Specialized Architectures

  • CNNs, RNNs, modern training techniques with visualizers
Weeks 5‑6

RL & Efficient Fine‑tuning

  • RL foundations, LoRA & adapter‑based tuning
Weeks 7‑8

Transformers & Attention

  • Self‑attention, transformer build‑up, pre‑trained model adaptation
Weeks 9‑10

Capstone & Advanced Topics

  • Self‑supervised, generative, multi‑modal learning & architecture defense

Phase 3: Decentralized Training & AutoML

Distributed systems, communication-efficient training, and automated ML

10 Weeks
3
Weeks 1‑2

Distributed Training Foundations

  • Data/model/pipeline parallelism, gradient traffic control
Weeks 3‑4

Communication‑Efficient Training

  • DCT & sparsification, EATreeSGD synchronization
Weeks 5‑6

Parallel Evolution & Auto‑Tuning

  • Seed chase, evolutionary strategies at scale
Weeks 7‑8

Decentralized Ecosystem

  • Federated learning, fault tolerance drills
Weeks 9‑10

AutoML & Self‑Improving Systems

  • NAS, continuous updating AI colony project

Community & Culture

Daily Rituals

  • • Algorithm meditations and theory discussions
  • • Paper reading and implementation sessions
  • • Distributed debugging workshops
  • • Peer code reviews and collaboration

Weekly Events

  • • Failure Friday: Learning from mistakes
  • • Distributed Disasters: System resilience
  • • Future Friday: Emerging tech discussions
  • • Demo Day: Project showcases

Learning Environment

  • • Access to GPU clusters for training
  • • Visual dashboards and monitoring tools
  • • Real-time collaboration platforms
  • • Ethical reflection workshops

Assessment & Projects

  • • Hands-on capstone projects
  • • Competition-style challenges
  • • Peer evaluation and feedback
  • • Real-world problem solving

Ready to Begin Your Journey?

The Hiveverse Bootcamp is a collaboration between Hivetensor and Intelliverse, designed to create the next generation of AutoML practitioners and distributed AI researchers.

Prerequisites

  • • Basic Python programming experience
  • • Linear algebra and calculus fundamentals
  • • Enthusiasm for machine learning
  • • Willingness to collaborate and share knowledge

What You'll Gain

  • • Deep understanding of AutoML systems
  • • Distributed training expertise
  • • Real-world project portfolio
  • • Global network of AI practitioners
View Full SyllabusStart with Challenges