🤖 AI Course Curriculum (2026)

 

🤖 AI Course Curriculum (2026)

🧠 Module 1: Fundamentals

  • What is AI, ML, Deep Learning

  • Types of AI (Narrow, General, Generative)

  • Real-world applications

  • Intro to Python (if needed)

👉 Tools:

  • Python

  • Jupyter Notebook

📊 Module 2: Math for AI (important basics)

  • Linear Algebra (vectors, matrices)

  • Probability & Statistics

  • Basics of calculus (derivatives)

🐍 Module 3: Python for Data Science

  • NumPy (arrays, operations)

  • Pandas (data handling)

  • Data cleaning & preprocessing

👉 Libraries:

  • NumPy

  • Pandas

📈 Module 4: Data Visualization

  • Charts, graphs

  • Exploratory Data Analysis (EDA)

👉 Tools:

  • Matplotlib

  • Seaborn

🤖 Module 5: Machine Learning (Core)

  • Supervised learning

    • Linear Regression

    • Logistic Regression

    • Decision Trees

  • Unsupervised learning

    • K-Means clustering

  • Model evaluation (accuracy, precision, recall)

👉 Library:

  • Scikit-learn

🧠 Module 6: Deep Learning

  • Neural networks basics

  • Activation functions

  • Backpropagation

  • CNN (for images)

  • RNN/LSTM (for sequences)

👉 Frameworks:

  • TensorFlow

  • PyTorch

💬 Module 7: Natural Language Processing (NLP)

  • Text preprocessing

  • Tokenization

  • Sentiment analysis

  • Chatbots

👉 Tools:

  • NLTK

  • spaCy

🧠 Module 8: Generative AI (Trending 🔥)

  • Large Language Models (LLMs)

  • Prompt engineering

  • Chatbots & assistants

  • Image generation basics

👉 Platforms:

  • OpenAI

  • Google DeepMind

⚙️ Module 9: AI Tools & Deployment

  • Model deployment (API creation)

  • Flask / FastAPI

  • Cloud basics (AWS, Azure)

🚀 Module 10: Real Projects (VERY IMPORTANT)

  • Chatbot using NLP

  • House price prediction

  • Image classifier (cats vs dogs)

  • Recommendation system

  • Resume screening AI

📊 Module 11: Advanced Topics (Optional)

  • Reinforcement Learning

  • Computer Vision

  • Transformers (BERT, GPT)

🎯 Course Duration

  • Beginner: 2–3 months

  • Intermediate: 3–6 months

  • Full mastery: 6–12 months

🏆 Final Outcome

After this course, you can become:

  • AI Engineer

  • Data Scientist

  • ML Engineer

  • AI App Developer

💡 Pro Tip (Important)

Since you already know programming:
👉 Focus more on:

  • Projects

  • Real datasets

  • Building apps (not just theory)

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