ML & AI Roadmap for Non CS People

Initial Stage: Build Your Foundation

Goal: Understand the basics of programming and math used in ML.

Learn Python (Essential)

• What to Learn:
  • Variables, loops, functions
  • Lists, dictionaries
  • Numpy & Pandas basics
• Resources:
  • Python for Everybody – FreeCourse
  • W3Schools Python

Learn Basic Math (ML-Focused)

• What to Learn:
  • Linear Algebra (vectors, matrices)
  • Probability & Statistics
  • Calculus (just the basics of derivatives)
• Resources:
  • Khan Academy (Linear Algebra & Probability)
  • Essence of Linear Algebra (YouTube)

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Stage 1: Understand Machine Learning

Goal: Learn ML theory, without heavy math.

Core Concepts

• Supervised vs. Unsupervised Learning
• Regression, Classification, Clustering
• Overfitting, Underfitting, Bias-Variance
• Resources:
  • Google Machine Learning Crash Course
  • YouTube: StatQuest by Josh Starmer
  • Machine Learning Simplified (YouTube)

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Stage 2: Hands-On Practice with ML

Goal: Apply ML with real code.

Learn Libraries

• Numpy & Pandas (Data handling)
• Matplotlib & Seaborn (Visualization)
• Scikit-learn (ML models)

Mini Projects

• Titanic survival prediction
• House price prediction
• Iris flower classification
• Resources:
  • Kaggle Courses (Free)
  • Hands-On ML with Scikit-learn (Book)

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Stage 3: Deep Learning & Neural Networks

Goal: Learn how deep learning works.

Key Concepts

• Perceptron, Neural Networks
• Activation Functions
• Backpropagation & Gradient Descent

Learn TensorFlow or PyTorch

• Start with: TensorFlow + Keras (beginner-friendly)

Projects

• Handwritten digit recognition (MNIST)
• Image classification with CNNs
• Resources:
  • DeepLearning.AI on Coursera
  • FreeCodeCamp Deep Learning

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Stage 4: Learn NLP / Computer Vision (Optional Path)

Choose based on your interest.

NLP (Natural Language Processing)

• Text classification, sentiment analysis
• Tools: HuggingFace Transformers, SpaCy

Computer Vision

• Object detection, face recognition
• Tools: OpenCV, TensorFlow/Keras

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Stage 5: Build & Share

Goal: Create a portfolio and build confidence.

Portfolio Projects Ideas

• ML-powered web app (e.g., spam classifier)
• Face mask detector
• Resume screening bot
• Diabetes prediction app

Tools to Learn:

• Flask or Streamlit (to deploy your ML model)
• GitHub (to share your projects)

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Stage 6: Go Pro!

Goal: Deepen knowledge, apply to jobs, research or freelance.

Advanced Topics:

• Time Series Forecasting
• Model Optimization (Hyperparameter Tuning)
• Transfer Learning, GANs
• ML Ops (Model Deployment)

Credentials

• Kaggle competitions
• Google/IBM AI certifications
• Publish on LinkedIn or Medium

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Summary Roadmap (Simplified View)

Stage	What You Learn
0	Python + Math Basics
1	ML Theory (No Code)
2	Practical ML + Projects
3	Deep Learning
4	NLP or Computer Vision
5	Deploy Projects
6	Specialize & Grow