Master in Artificial Intelligence and Machine Learning

Curriculum covering Python Programming for AI (Month for Master in Artificial Intelligence and Machine Learning. Topics include: • 1) • Python Fundamentals • Introduction to Python • Python Installation & Environment Setup • Variables & Data Types • Operators • Conditional Statements • Loops • Functions • Lambda Functions • Modules & Packages • Advanced Python • Object-Oriented Programming • Classes & Objects • Inheritance • Encapsulation • Polymorphism • Exception Handling • File Handling • Regular Expressions • Python Libraries • NumPy • Pandas • Matplotlib • Seaborn • Plotly

Curriculum covering Mathematics & Statistics for AI for Master in Artificial Intelligence and Machine Learning. Topics include: • (Month 2) • Linear Algebra • Vectors • Matrices • Matrix Operations • Eigenvalues & Eigenvectors • Linear Transformations • Calculus • Functions • Derivatives • Gradients • Optimization • Statistics • Mean • Median • Mode • Variance • Standard Deviation • Probability • Distribution • Hypothesis Testing • Machine Learning Mathematics • Cost Functions • Gradient Descent • Regression Mathematics

Curriculum covering Data Science & Data Analytics for Master in Artificial Intelligence and Machine Learning. Topics include: • (Month 3) • Data Handling • Data Collection • Data Cleaning • Data Preprocessing • Missing Values Handling • Data Transformation • Exploratory Data Analysis (EDA) • Data Visualization • Pattern Detection • Feature Analysis • Tools • Jupyter Notebook • Google Colab • Anaconda • SQL for AI • Database Concepts • SQL Queries • Data Extraction • Data Management

Curriculum covering Machine Learning Master Level for Master in Artificial Intelligence and Machine Learning. Topics include: • (Month 4 • 5) • Introduction to Machine Learning • AI vs ML vs Deep Learning • ML Workflow • Supervised Learning • Unsupervised Learning • Reinforcement Learning • Regression Algorithms • Linear Regression • Multiple Regression • Polynomial Regression • Ridge Regression • Lasso Regression • Classification Algorithms • Logistic Regression • Decision Tree • Random Forest • Naive Bayes • KNN • Support Vector Machine (SVM) • Clustering • K-Means • Hierarchical Clustering • DBSCAN • PCA • Model Evaluation • Train-Test Split • Cross Validation • Accuracy • Precision • Recall • F1 Score • ROC Curve • Confusion Matrix • ML Libraries • Scikit-Learn • TensorFlow Basics • PyTorch Basics

Curriculum covering Deep Learning & Neural Networks for Master in Artificial Intelligence and Machine Learning. Topics include: • (Month 6 • 7) • Neural Network Fundamentals • Artificial Neural Networks • Perceptron • Activation Functions • Forward Propagation • Back Propagation • Loss Functions • Optimization Algorithms • Deep Learning Frameworks • TensorFlow • Keras • PyTorch • Advanced Neural Networks • CNN (Convolutional Neural Networks) • RNN (Recurrent Neural Networks) • LSTM • GRU • Computer Vision • Image Processing • Image Classification • Object Detection • Face Recognition • OpenCV

Curriculum covering Natural Language Processing (NLP) for Master in Artificial Intelligence and Machine Learning. Topics include: • (Month 8) • NLP Fundamentals • Text Processing • Tokenization • Stemming • Lemmatization • Stop Words • Text Cleaning • NLP Algorithms • Bag of Words • TF-IDF • Word Embeddings • Word2Vec • GloVe • Advanced NLP • Sentiment Analysis • Text Classification • Chatbots • Language Translation • NLP Libraries • NLTK • SpaCy • Transformers

Curriculum covering Generative AI & Large Language for Master in Artificial Intelligence and Machine Learning. Topics include: • Models (Month 9) • Generative AI Fundamentals • Introduction to Generative AI • AI Content Generation • Prompt Engineering • Large Language Models (LLMs) • GPT Architecture • Transformer Architecture • Attention Mechanism • Fine-Tuning Concepts • AI Application Development • OpenAI API Integration • AI Chat Applications • AI Assistants • Zero Shot Prompting • Few Shot Prompting • Chain of Thought Prompting • AI Automation • Tools • ChatGPT • Claude • Gemini • Hugging Face

Curriculum covering Computer Vision & Advanced AI for Master in Artificial Intelligence and Machine Learning. Topics include: • (Month 10) • Advanced Computer Vision • CNN Architecture • Transfer Learning • YOLO Object Detection • Image Segmentation • AI Applications • OCR Systems • Recommendation Systems • Predictive Analytics • Voice Recognition • Frameworks • OpenCV • TensorFlow • PyTorch

Curriculum covering MLOps & AI Deployment (Month for Master in Artificial Intelligence and Machine Learning. Topics include: • 11) • Model Deployment • Machine Learning API Development • Flask • FastAPI • Cloud AI Deployment • AWS Machine Learning Basics • Google Cloud AI Basics • Azure AI Basics • DevOps for AI • Git • GitHub • Docker • Kubernetes Basics • CI/CD Pipeline • Model Management • MLflow • Model Versioning • Model Monitoring

Curriculum covering Master AI Capstone & Career for Master in Artificial Intelligence and Machine Learning. Topics include: • Preparation (Month 12) • Industry Capstone Projects • Students will develop complete AI solutions: