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AI & Data • Step-by-Step Curriculum

Artificial Intelligence, ML & Data Science

Progress from Python scripting and data visualization to classical ML, neural networks, and modern LLM/RAG engineering.

Track Progress 0/8 Steps (0%)
Sign in to save progress, pass the Mastery Test, and earn a verified MESHLUMA Certificate. Practice Mastery Test (5 Questions)
Practical Equipment & Study Requirements
What laptop, phone, internet, and free tools you need for this field—plus cloud options if your device is low-spec.
Phone / 4GB Laptop + Free Cloud GPU
Laptop / PC Specs

Minimum: Any 4GB RAM laptop with Chrome/Edge — run heavy Python & ML models in the cloud for free without local GPU.

Recommended: 8GB–16GB RAM, 256GB+ SSD (NVIDIA GPU optional; Google Colab & Kaggle give free cloud T4 GPUs).

Free Cloud Option: Google Colab & Kaggle Notebooks — zero setup, free Python + Pandas + PyTorch + free GPU in your browser.

Phone & Internet

Phone-Friendly Start

Steps 1–2 work on a phone via Kaggle Learn, Google Colab mobile browser, or Pydroid 3 (Android); switch to a laptop/tablet for multi-file projects.

Internet: 2–5 Mbps recommended for notebooks & downloading public datasets (small CSVs work on mobile data).

Free Software & Hardware Kit
Google Colab (Free Cloud) Kaggle Account Python 3.11+ & JupyterLab VS Code

Hardware / Kit: No expensive GPU hardware needed — use free cloud GPUs on Google Colab and Kaggle.

Best Way to Study This Field Easily: Skip complex local Anaconda installs at first: open Kaggle Learn and Google Colab directly in your browser for instant execution.
All Levels (1–8) Beginner Intermediate Advanced Expert
Advanced Stage

Production patterns, architecture, automation, and security rigor.

Step 5 advanced ~24 hrs

Deep Learning & Neural Networks with PyTorch

Train neural networks for vision and structured data using modern deep learning workflows.

What You Will Learn
  • Backpropagation & optimizers
  • CNNs & transfer learning
  • GPU training & regularization
Free Public Courses & Official Docs
Practical Deep Learning for Coders
fast.ai • Course
Open Free
PyTorch Tutorials
PyTorch • Docs
Open Free
Hands-On Milestone Project: Train an image classifier (e.g., crop disease or sign recognition) via transfer learning.
Step 6 advanced ~20 hrs

Natural Language Processing & Transformers

Understand tokenization, attention mechanisms, embeddings, and fine-tuning transformer models.

What You Will Learn
  • Tokenizers & self-attention
  • Hugging Face Transformers pipeline
  • Semantic search & embeddings
Free Public Courses & Official Docs
Hugging Face NLP Course
Hugging Face • Interactive Course
Open Free
CS231n Deep Learning Notes
Stanford • Notes
Open Free
Hands-On Milestone Project: Fine-tune or build a multilingual text classifier and semantic search tool.
Field Mastery Assessment

Artificial Intelligence, ML & Data Science — Practical Mastery Assessment

Score 80% or higher (5 scenario questions) + submit your real practical capstone project after at least 7 days of study to earn a Verified MESHLUMA Certificate.

Sign In & Complete Study Steps to Unlock Test

Tests are given after studying all 8 steps to prove real practical mastery.

Sign In to Track Study & Unlock Test
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