Name: AI and Data Science
Price: 56000 INR
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Module 1:

✔ Introduction to Python and Setup
✔ Python Syntax and Variables
✔ Data Types (int, float, string, boolean)
✔ Operators (Arithmetic, Comparison, Logical, Assignment)
✔ Control Flow (if-else, loops)
✔ Data Structures (Lists, Tuples, Sets, Dictionaries)
✔ Functions and Lambda Expressions
✔ Object-Oriented Programming (Classes, Objects, Inheritance)
✔ Modules and Packages
✔ File Handling (CSV, JSON, Excel, Text files)
✔ Exception Handling and Debugging
✔ NumPy for Numerical Computing
✔ Pandas for Data Manipulation
✔ Data Cleaning and Preprocessing
✔ Matplotlib for Data Visualization
✔ Seaborn for Statistical Plots
✔ Working with APIs and Web Scraping
✔ Regular Expressions
✔ DateTime Module for Time Series

Module 2:

✔ Introduction to Machine Learning and Its Types
✔ Supervised Learning vs Unsupervised Learning vs Reinforcement Learning
✔ Data Preprocessing and Feature Engineering
✔ Handling Missing Values and Outliers
✔ Feature Scaling (Normalization, Standardization)
✔ Encoding Categorical Variables (One Hot, Label Encoding)
✔ Linear Regression and Polynomial Regression
✔ Ridge and Lasso Regression (Regularization)
✔ Logistic Regression for Classification
✔ Decision Trees and Random Forests
✔ Support Vector Machines (SVM)
✔ K-Nearest Neighbors (KNN)
✔ Naive Bayes Classifier
✔ Ensemble Methods (Bagging, Boosting, Stacking)
✔ XGBoost, AdaBoost and Gradient Boosting
✔ K-Means Clustering
✔ Hierarchical Clustering
✔ DBSCAN and Other Clustering Algorithms
✔ Principal Component Analysis (PCA)
✔ t-SNE for Dimensionality Reduction
✔ Model Evaluation Metrics (Accuracy, Precision, Recall, F1-Score, ROC-AUC)
✔ Confusion Matrix and Classification Report
✔ Cross-Validation Techniques
✔ Hyperparameter Tuning (Grid Search, Random Search)
✔ Overfitting, Underfitting, and Regularization (L1, L2)
✔ Bias-Variance Tradeoff
✔ Recommendation Systems
✔ Time Series Forecasting (ARIMA, Prophet)
✔ Anomaly Detection Techniques

Module 3:

✔ Introduction to Neural Networks
✔ Perceptrons and Multi-Layer Perceptrons (MLPs)
✔ Activation Functions (ReLU, Sigmoid, Tanh, Softmax)
✔ Forward and Backward Propagation
✔ Loss Functions and Optimization Algorithms (SGD, Adam, RMSprop)
✔ Deep Neural Networks (DNNs) Architecture
✔ Convolutional Neural Networks (CNNs)
✔ Image Classification with CNNs
✔ Object Detection (YOLO, R-CNN, SSD)
✔ Image Segmentation Techniques
✔ Recurrent Neural Networks (RNNs)
✔ Long Short-Term Memory (LSTM)
✔ Gated Recurrent Units (GRU)
✔ Sequence-to-Sequence Models
✔ Autoencoders and Variational Autoencoders (VAE)
✔ Generative Adversarial Networks (GANs)
✔ Transfer Learning Concepts
✔ Pre-trained Models (VGG, ResNet, InceptionNet, MobileNet)
✔ Fine-tuning Pre-trained Models
✔ TensorFlow and Keras Framework
✔ PyTorch Framework
✔ Regularization Techniques (Dropout, Batch Normalization)
✔ Hyperparameter Tuning for Deep Learning
✔ GPU Acceleration and Distributed Training
✔ Model Deployment and Serving

Module 4:

✔ Introduction to Natural Language Processing
✔ Text Preprocessing and Cleaning
✔ Tokenization Techniques
✔ Stemming and Lemmatization
✔ Stop Words Removal
✔ Bag of Words (BoW) Model
✔ TF-IDF (Term Frequency-Inverse Document Frequency)
✔ Word Embeddings (Word2Vec, GloVe, FastText)
✔ N-grams and Language Models
✔ Sentiment Analysis
✔ Text Classification
✔ Named Entity Recognition (NER)
✔ Part-of-Speech (POS) Tagging
✔ Topic Modeling (LDA, LSA)
✔ Text Summarization
✔ Language Translation
✔ Chatbot Development
✔ Attention Mechanism
✔ Transformer Architecture
✔ BERT and Its Variants
✔ GPT Models Overview
✔ Large Language Models (LLMs)
✔ Prompt Engineering Techniques
✔ Hugging Face Transformers Library
✔ NLTK and spaCy Libraries
✔ Question Answering Systems

Module 5:

✔ Introduction to Databases
✔ Relational Database Management Systems (RDBMS)
✔ Database Design and ER Diagrams
✔ Normalization (1NF, 2NF, 3NF, BCNF)
✔ SQL Basics and Syntax
✔ Data Definition Language (DDL) - CREATE, ALTER, DROP
✔ Data Manipulation Language (DML) - SELECT, INSERT, UPDATE, DELETE
✔ Filtering Data with WHERE Clause
✔ Sorting and Grouping Data (ORDER BY, GROUP BY, HAVING)
✔ Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)
✔ SQL Joins (INNER, LEFT, RIGHT, FULL OUTER, CROSS)
✔ Subqueries and Nested Queries
✔ Common Table Expressions (CTEs)
✔ Window Functions (ROW_NUMBER, RANK, DENSE_RANK, LEAD, LAG)
✔ Views and Materialized Views
✔ Indexes and Query Optimization
✔ Stored Procedures and Functions
✔ Triggers and Events
✔ Transactions and ACID Properties
✔ NoSQL Databases Introduction
✔ MongoDB Basics and Operations
✔ Connecting Databases with Python (SQLAlchemy, PyMongo)
✔ Database Migration and Version Control

Module 6:

✔ Introduction to Cloud Computing
✔ Cloud Service Models (IaaS, PaaS, SaaS)
✔ Cloud Deployment Models (Public, Private, Hybrid)
✔ Overview of AWS (Amazon Web Services)
✔ Overview of Microsoft Azure
✔ Overview of Google Cloud Platform (GCP)
✔ Cloud Storage Solutions (S3, Azure Blob, Google Cloud Storage)
✔ AWS SageMaker for ML Model Training and Deployment
✔ Azure Machine Learning Studio
✔ Google AI Platform and Vertex AI
✔ Containerization with Docker
✔ Container Orchestration with Kubernetes
✔ Serverless Computing (AWS Lambda, Azure Functions)
✔ Model Deployment Strategies
✔ Cloud-based APIs and Endpoints
✔ CI/CD Pipelines for ML Models
✔ Model Monitoring and Logging
✔ Auto-scaling and Load Balancing
✔ Cloud Security and IAM (Identity Access Management)
✔ Cost Optimization in Cloud

Module 7:

✔ Introduction to Web Frameworks
✔ Flask Installation and Setup
✔ Flask Application Structure
✔ Routing and URL Mapping
✔ HTTP Methods (GET, POST, PUT, DELETE)
✔ Request and Response Handling
✔ Jinja2 Templating Engine
✔ Static Files and CSS Integration
✔ Form Handling with Flask-WTF
✔ Building RESTful APIs
✔ JSON Serialization and Deserialization
✔ API Authentication (JWT, OAuth)
✔ Error Handling and Logging
✔ Serving Machine Learning Models via API
✔ FastAPI Introduction and Benefits
✔ Building APIs with FastAPI
✔ Data Validation with Pydantic
✔ Asynchronous Programming with FastAPI
✔ API Documentation with Swagger/OpenAPI
✔ Testing Flask/FastAPI Applications
✔ Deployment to Production (Gunicorn, Nginx)

Module 8:

✔ Jupyter Notebook and JupyterLab
✔ Google Colab for Cloud Computing
✔ VS Code Setup and Extensions for Data Science
✔ PyCharm IDE for Python Development
✔ Anaconda Distribution and Package Management
✔ Git and GitHub for Version Control
✔ Git Branching and Merging Strategies
✔ Collaboration with Pull Requests
✔ Plotly for Interactive Visualizations
✔ Dash for Building Analytical Web Applications
✔ Streamlit for Rapid Prototyping
✔ Tableau for Business Intelligence
✔ Power BI for Data Visualization
✔ Excel for Data Analysis
✔ Docker Fundamentals and Containerization
✔ Docker Compose for Multi-container Applications
✔ MLflow for Experiment Tracking
✔ Model Registry and Versioning
✔ Apache Airflow for Workflow Orchestration
✔ DVC (Data Version Control)
✔ Great Expectations for Data Quality

Module 9:

✔ Introduction to Generative AI
✔ Generative vs Discriminative Models
✔ Applications of Generative AI
✔ Large Language Models (LLMs) Overview
✔ GPT (Generative Pre-trained Transformer) Architecture
✔ BERT, T5, and Other Transformer Models
✔ Prompt Engineering Fundamentals
✔ Prompt Design Patterns and Best Practices
✔ Few-Shot and Zero-Shot Learning
✔ Chain-of-Thought Prompting
✔ Working with OpenAI API
✔ GPT-3.5, GPT-4, and GPT-4 Turbo
✔ Azure OpenAI Service Integration
✔ Google Gemini and Palm API
✔ Hugging Face Transformers Library
✔ LangChain Framework for LLM Applications
✔ LlamaIndex for Data-Augmented LLMs
✔ Retrieval Augmented Generation (RAG)
✔ Vector Databases (Pinecone, ChromaDB, FAISS)
✔ Building AI Chatbots
✔ Content Generation Applications
✔ AI-Powered Virtual Assistants
✔ Code Generation with AI (GitHub Copilot, Codex)
✔ Image Generation (DALL-E, Stable Diffusion, Midjourney)
✔ Fine-Tuning LLMs for Custom Use Cases
✔ Model Deployment and Serving
✔ Ethical AI and Responsible AI Development
✔ AI Governance and Compliance
✔ Bias Detection and Mitigation

AI and Data Science
Mastery Program

What You'll Learn

Ideal Candidates

Syllabus

Module 1:

Module 2:

Module 3:

Module 4:

Module 5:

Module 6:

Module 7:

Module 8:

Module 9:

Tools & Technologies You'll Master

Languages

Libraries & Frameworks

Platforms & Tools

Your Learning Journey

Learn

Practice

Build

Get Placed

Our Alumni Work At

Learn from Industry Leaders

VCTC Expert Faculty

What Our Students Say

Frequently Asked Questions

Your AI and Data Science Career
Starts Here

AI and Data ScienceMastery Program

What You'll Learn

Ideal Candidates

Syllabus

Module 1:

Module 2:

Module 3:

Module 4:

Module 5:

Module 6:

Module 7:

Module 8:

Module 9:

Tools & Technologies You'll Master

Languages

Libraries & Frameworks

Platforms & Tools

Your Learning Journey

Learn

Practice

Build

Get Placed

Our Alumni Work At

Learn from Industry Leaders

VCTC Expert Faculty

What Our Students Say

Frequently Asked Questions

Your AI and Data Science CareerStarts Here

AI and Data Science
Mastery Program

Your AI and Data Science Career
Starts Here