AI Diploma – Beginner to Pro complete syllabus
Module 1: Foundations of AI (Beginner Level)
1.1 Introduction to Artificial Intelligence
- What is AI?
- History & evolution
- Types of AI (Narrow, General, Super)
- AI vs ML vs DL
1.2 Basic Mathematics for AI
- Linear Algebra (Vectors, Matrices)
- Probability & Statistics
- Calculus (Basics of derivatives and gradients)
- Graph Theory (Optional but helpful)
1.3 Programming Fundamentals with Python
- Python basics (variables, loops, functions)
- Libraries: NumPy, Pandas, Matplotlib
- Data structures & OOP in Python
- Jupyter Notebooks & Colab
Module 2: Machine Learning (Intermediate Level)
2.1 Supervised Learning
- Regression (Linear, Polynomial)
- Classification (Logistic, KNN, SVM)
- Decision Trees & Random Forests
- Model evaluation (Accuracy, Precision, Recall, F1-score)
2.2 Unsupervised Learning
- Clustering (K-means, DBSCAN, Hierarchical)
- Dimensionality Reduction (PCA, t-SNE)
2.3 Model Optimization
- Cross-validation
- Hyperparameter tuning (Grid Search, Random Search)
- Overfitting/Underfitting
- Bias-Variance tradeoff
Module 3: Deep Learning (Advanced Level)
3.1 Neural Networks Basics
- Perceptron and MLP
- Activation functions (ReLU, Sigmoid, Tanh)
- Loss functions (MSE, Cross-Entropy)
3.2 Advanced Neural Networks
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- LSTMs & GRUs
3.3 Deep Learning Frameworks
- TensorFlow (2.x)
- PyTorch
- Keras
Module 4: Specialized AI Domains
4.1 Natural Language Processing (NLP)
- Text preprocessing (tokenization, stemming)
- Sentiment analysis
- Named Entity Recognition (NER)
- Transformers (BERT, GPT basics)
4.2 Computer Vision
- Image classification
- Object detection (YOLO, RCNN)
- Image segmentation
4.3 Reinforcement Learning
- Q-learning
- Deep Q Networks
- OpenAI Gym basics
Module 5: AI in Practice
5.1 Real-World Projects
- AI Chatbot with NLP
- Stock price prediction
- Image classifier (e.g., dog vs cat)
- Recommendation system (like Netflix)
5.2 AI Deployment
- Streamlit / Flask for AI app
- Model saving/loading (Pickle, joblib)
- Docker basics
- Deployment on AWS/GCP
5.3 Ethics & Future of AI
- AI Bias & Fairness
- Explainable AI (XAI)
- AI Ethics, Safety & Governance
- Job market & career in AI
Bonus: AI Tools & Trends (Optional but Important)
- AutoML (Google AutoML, H2O.ai)
- Low-code AI platforms (Teachable Machine, Lobe.ai)
- Prompt Engineering (for GPT-like models)
- LangChain & Agent-based AI
- Introduction to OpenAI APIs
Capstone Project (Final)
A large-scale project combining all learnings:
Example: “AI-Powered Medical Diagnosis App” or “End-to-End E-commerce Recommendation Engine”
Deliverables & Outcomes
- 10–15 mini-projects
- 1 Capstone Project
- GitHub portfolio
- Resume & LinkedIn optimization
- Internship/job readiness
