Master the core concepts and algorithms of machine learning.

Topics covered:

• * Supervised Learning Algorithms
• * Unsupervised Learning Techniques
• * Model Evaluation & Validation
• * Feature Engineering
• * Ensemble Methods
• * Reinforcement Learning Basics

Explore advanced neural network architectures and deep learning techniques.

Topics covered:

• * Neural Network Architecture
• * Convolutional Neural Networks
• * Recurrent Neural Networks
• * Transfer Learning
• * GANs & Autoencoders
• * Deep Learning Frameworks

Learn to build systems that can understand and process visual information.

Topics covered:

• * Image Processing
• * Object Detection & Recognition
• * Face Recognition
• * Video Analysis
• * OpenCV Programming
• * Real-time Vision Applications

Develop skills in processing and analyzing human language data.

Topics covered:

• * Text Processing & Analysis
• * Sentiment Analysis
• * Language Generation
• * Chatbot Development
• * BERT & Transformers
• * Speech Recognition

• * AI Engineer
• * Machine Learning Engineer
• * Data Scientist
• * Computer Vision Engineer
• * NLP Specialist
• * Research Scientist
• * AI Solutions Architect
• * Robotics Engineer

• * Industry-aligned curriculum
• * Hands-on project experience
• * Real-world applications
• * Research paper implementations
• * Industry expert mentorship
• * Advanced tool training
• * Placement assistance
• * Certificate of completion