Artificial Intelligence Roadmaps

Intelligent systems aur smart software banana jo human-like decisions le sakein.

• Foundations: Discrete Mathematics, Logic, Linear Algebra & Calculus.
• Programming: Python, C++, Object-Oriented Programming (OOP).
• AI Search Algorithms: A* Search, BFS/DFS, Minimax Algorithm (Game Playing).
• Knowledge Representation: Expert Systems, Rule-based engines, Decision Trees.

Data se computer ko automatic patterns seekhne aur learn karne ki capability dena.

• Math & Stats: Probability, Hypothesis Testing, Matrices, Optimization.
• Data Handling: NumPy, Pandas, Matplotlib, Seaborn.
• Supervised Learning: Linear/Logistic Regression, Decision Trees, Random Forest, SVM.
• Unsupervised Learning: K-Means Clustering, PCA (Principal Component Analysis).
• ML Frameworks: Scikit-Learn, XGBoost.

Human brain jaisey Neural Networks banana complex problems solve karne ke liye.

• Neural Network Core: Perceptrons, Activation Functions, Forward & Backpropagation.
• Architectures: ANN (Artificial Neural Networks), CNN (Convolutional Neural Networks), RNN/LSTM.
• Deep Learning Frameworks: PyTorch, TensorFlow, Keras.
• Optimization: Gradient Descent, Adam Optimizer, Regularization (Dropout).

Raw data se meaningful insights nikalna, analyze karna, aur future predictions karna.

• Data Wrangling: Data Cleaning, Missing Value Imputation, Feature Engineering.
• SQL & Databases: Complex Queries, Relational Databases, PostgreSQL/MySQL.
• Data Visualization: Tableau, Power BI, Plotly.
• Predictive Analytics: Statistical Modeling, Forecasting, A/B Testing.

Images aur videos ko computer se samajhna aur analyze karwana.

• Image Processing: OpenCV Basics, Image Transformations, Filtering, Edge Detection.
• Object Detection & Tracking: YOLO (You Only Look Once), R-CNN, OpenCV DNN.
• Segmentation: Semantic & Instance Segmentation (U-Net, Mask R-CNN).
• Applications: Face Recognition, Autonomous Driving Systems, Gesture Detection.

Human language (English, Urdu, etc.) samajhne aur process karne wale systems.

• Text Preprocessing: Tokenization, Stemming, Lemmatization, Stopwords removal.
• Text Vectorization: Bag of Words, TF-IDF, Word2Vec, GloVe embeddings.
• NLP Frameworks: NLTK, spaCy, Hugging Face Transformers.
• Modern NLP: Transformers Architecture, BERT, Text Summarization, Sentiment Analysis.

Voice recognition, speech-to-text, aur text-to-speech audio systems.

• Audio Processing: Signal Processing, Spectrograms, MFCC (Mel-Frequency Cepstral Coefficients), Librosa library.
• Automatic Speech Recognition (ASR): Whisper API, DeepSpeech, Speech-to-Text models.
• Text-to-Speech (TTS): Tacotron, WaveNet, ElevenLabs APIs integration.
• Voice Assistants: Intent Recognition, Voice Command Bots.

Naya content (Text, Images, Code, Audio) create karne wale modern AI models.

• Generative Models Basics: GANs (Generative Adversarial Networks), Variational Autoencoders (VAEs).
• LLMs (Large Language Models): GPT-4, LLaMA, Gemini Architecture & Fine-tuning.
• Prompt Engineering & RAG: LangChain, LlamaIndex, Vector Databases (Pinecone, ChromaDB).
• Diffusion Models: Stable Diffusion, Midjourney concepts, Text-to-Image Generation.