📘 Getting Started with AI Model Coding – With Minimal Math

🚦 Step-by-Step Guide to Start Learning AI Model Coding

✅ Step 1: Learn the Python Basics

If you’re not yet comfortable with Python, start here:

• Topics to learn: variables, loops, functions, classes, lists, dictionaries, NumPy.
• Resources:
  • Python for Everybody
  • W3Schools Python Tutorial
  • LearnPython.org

✅ Step 2: Understand Core AI/ML Concepts

• Topics: supervised/unsupervised learning, classification vs regression, overfitting, accuracy, loss function, training vs testing.
• Resources:
  • Google's Machine Learning Crash Course
  • Kaggle: Intro to Machine Learning

✅ Step 3: Learn a Machine Learning Library (scikit-learn)

• Use scikit-learn to train small, efficient models.
• Scikit-learn Tutorial

✅ Step 4: Dive into Neural Networks (TensorFlow or PyTorch)

PyTorch

• More Pythonic and beginner-friendly.
• PyTorch Beginner Guide

TensorFlow

• Good abstraction for prototyping and deployment.
• TensorFlow Quickstart

✅ Step 5: Practice with Real Projects

Beginner ideas:

• Spam Email Classifier
• Digit Recognizer (MNIST)
• Sentiment Analyzer
• Rock/Paper/Scissors Classifier

✅ Step 6: Learn How to Use Datasets

• Kaggle Datasets
• UCI ML Repository
• Hugging Face Datasets

✅ Step 7: Version Control with Git + GitHub Codespaces

• Learn: git add, git commit, git push, git pull
• Use GitHub for backup and collaboration

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🧮 Math for AI: What You Need & When

✅ 1. Linear Algebra (High Priority)

• Why: Core to how models process data.
• Concepts: Vectors, matrices, dot product, matrix multiplication.
• Resources:
  • 3Blue1Brown - Essence of Linear Algebra
  • Khan Academy - Linear Algebra

✅ 2. Calculus (Medium Priority)

• Why: Powers backpropagation and gradient descent.
• Concepts: Derivatives, gradients, chain rule.
• Resources:
  • Khan Academy - Calculus
  • 3Blue1Brown - Calculus

✅ 3. Probability & Statistics (High Priority)

• Why: Needed to evaluate and interpret AI models.
• Concepts: Mean, variance, distributions, Bayes’ theorem.
• Resources:
  • StatQuest YouTube
  • Khan Academy - Stats & Probability

✅ 4. Discrete Math & Logic (Optional)

• Use in symbolic AI, rule systems, and planning algorithms.

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