| S.No |
Topic |
Sub-Topics |
|
1
|
AI Fundamentals
|
AI definition, AI types, Narrow AI, General AI, Generative AI, Predictive AI, AI applications
|
|
2
|
AI & ML Foundations
|
AI vs ML, Supervised learning, Unsupervised learning, Semi-supervised learning, Reinforcement learning, Training, Inference
|
|
3
|
Data Fundamentals
|
Structured data, Unstructured data, Data collection, Data preprocessing, Data quality, Features, Labels
|
|
4
|
Mathematics for AI
|
Linear algebra, Vectors, Matrices, Matrix operations, Probability, Statistics, Calculus basics
|
|
5
|
Probability & Statistics
|
Probability distributions, Conditional probability, Bayes theorem, Mean, Variance, Standard deviation, Correlation
|
|
6
|
Machine Learning Fundamentals
|
Regression, Classification, Clustering, Feature engineering, Training, Validation, Prediction
|
|
7
|
ML Algorithms
|
Linear regression, Logistic regression, Decision trees, Random forest, SVM, KNN, Naive Bayes
|
|
8
|
Ensemble Learning
|
Bagging, Boosting, Random forest, AdaBoost, Gradient boosting, XGBoost, LightGBM
|
|
9
|
Model Evaluation
|
Train-test split, Cross-validation, Accuracy, Precision, Recall, F1-score, ROC-AUC
|
|
10
|
Feature Engineering
|
Feature selection, Feature extraction, Encoding, Scaling, Normalization, Missing values, Outliers
|
|
11
|
Unsupervised Learning
|
K-Means, Hierarchical clustering, DBSCAN, PCA, Dimensionality reduction, Anomaly detection, Cluster evaluation
|
|
12
|
Deep Learning Fundamentals
|
Neural networks, Neurons, Layers, Weights, Bias, Activation functions, Forward propagation
|
|
13
|
Neural Network Training
|
Loss functions, Backpropagation, Gradient descent, Learning rate, Optimizers, Batch size, Epochs
|
|
14
|
Deep Learning Architectures
|
CNN, RNN, LSTM, GRU, Autoencoder, Transformer, Attention
|
|
15
|
Computer Vision
|
Image classification, Object detection, Image segmentation, OCR, Image embeddings, Vision transformers, Image generation
|
|
16
|
NLP Fundamentals
|
Text preprocessing, Tokenization, Stemming, Lemmatization, N-grams, Text classification, Named Entity Recognition
|
|
17
|
Embeddings
|
Word embeddings, Sentence embeddings, Document embeddings, Vector representation, Cosine similarity, Semantic search, Embedding models
|
|
18
|
Generative AI
|
Generative models, LLMs, Text generation, Image generation, Audio generation, Video generation, Multimodal AI
|
|
19
|
LLM Fundamentals
|
Transformer, Tokens, Context window, Attention, Parameters, Pre-training, Inference
|
|
20
|
Prompt Engineering
|
Zero-shot, Few-shot, Role prompting, Prompt templates, Structured prompts, Prompt chaining, Output constraints
|
|
21
|
RAG
|
Document ingestion, Chunking, Embeddings, Vector database, Retrieval, Reranking, Context generation
|
|
22
|
Vector Databases
|
Vector indexing, Similarity search, ANN, Metadata filtering, Hybrid search, Reranking, Vector DB selection
|
|
23
|
AI Agents
|
Agent architecture, Planning, Tool calling, Function calling, Memory, ReAct, Multi-agent systems
|
|
24
|
AI Tools & MCP
|
Tool definitions, API tools, Database tools, Function execution, MCP concepts, MCP servers, MCP clients
|
|
25
|
Fine-Tuning
|
Instruction tuning, Dataset preparation, Supervised fine-tuning, LoRA, QLoRA, PEFT, Fine-tuning evaluation
|
|
26
|
AI Evaluation
|
Model evaluation, RAG evaluation, Faithfulness, Relevance, Groundedness, LLM-as-a-judge, Benchmarking
|
|
27
|
AI Security
|
Prompt injection, Jailbreaking, Data leakage, PII protection, Tool security, Output validation, Guardrails
|
|
28
|
AI MLOps / LLMOps
|
Model deployment, Model registry, Versioning, Monitoring, Logging, Drift detection, Experiment tracking
|
|
29
|
AI Performance & Cost
|
Model selection, Quantization, Caching, Batching, Token optimization, Latency optimization, Cost optimization
|
|
30
|
Production AI Architecture
|
AI application architecture, RAG architecture, Agent architecture, Data layer, Model layer, Security layer, Monitoring & deployment
|
