ELINAUT

E L I N A U T

Address
Vennala puthiya Road Thykauv jun, opp. Clearway Appt, Arakkakadavu, Kakkanad, Kerala 682028
Email
mail@elinaut.com
Contact
+91 9567863498

Select Curriculum:

Introduction to AI and ML - 1-month Intermediate AI and ML - 3 Month Advanced AI and ML - 6 month

Target Audience

Intermediate learners or those who completed the 3-month course.

Objective

Master advanced AI/ML techniques, including deep learning, reinforcement learning, and real-world applications.

What You'll Learn

480 hours of comprehensive training

20 hours per week

Hands-on practical experience

Industry-relevant curriculum

Expert instructors

Week 1-6: Advanced Deep Learning

Advanced Deep Learning Architectures (18 hours)
- Recurrent Neural Networks (RNNs), LSTMs, and GRUs for sequential data.
- Generative models: Autoencoders, GANs (Generative Adversarial Networks).
Deep Learning Optimization (12 hours)
- Advanced optimization techniques: Adam, RMSprop.
- Handling overfitting: Dropout, regularization.
Lab: Building an LSTM for time-series prediction or a GAN for image generation (20 hours).
Transfer Learning and Fine-Tuning (10 hours)
- Fine-tuning pre-trained models for specific tasks.
- Applications in computer vision and NLP.

Week 7-12: Reinforcement Learning and Advanced NLP

Reinforcement Learning (RL) (15 hours)
- RL fundamentals: Markov Decision Processes, Q-Learning.
- Deep RL: DQN, Policy Gradients.
Advanced NLP (15 hours)
- Transformer models: BERT, GPT architectures.
- Applications: Chatbots, text summarization, translation.
Lab: Implementing a reinforcement learning agent or a transformer-based NLP model (20 hours).
AI Ethics and Scalability (10 hours)
- Ethical considerations in AI deployment.
- Scalable AI systems using cloud platforms (e.g., AWS, Azure).

Week 13-18: AI in Industry Applications

Computer Vision Applications (15 hours)
- Object detection (YOLO, Faster R-CNN), semantic segmentation.
- Real-time vision applications (e.g., autonomous vehicles).
AI in Specialized Domains (12 hours)
- AI in healthcare, finance, and robotics.
- Time-series analysis for predictive maintenance.
Lab: Developing a computer vision or domain-specific AI model (23 hours).

Week 19-24: Advanced Projects and Deployment

Advanced Model Deployment (15 hours)
- Deploying AI models at scale (e.g., Kubernetes, Docker).
- Real-time inference with edge devices (e.g., Jetson Nano).
Project Management and AI Pipelines (10 hours)
- End-to-end AI project lifecycle: Data collection to deployment.
- MLOps: Continuous integration and delivery for ML models.
Lab: Building and deploying a scalable AI system (25 hours).
Capstone Project: End-to-end AI/ML system for a real-world application (e.g., autonomous navigation, medical diagnosis) (20 hours).
Industry Case Studies and Certification Prep (10 hours)
- Analysis of real-world AI/ML projects (e.g., Tesla Autopilot, Google Translate).
- Preparation for certifications (e.g., AWS Certified Machine Learning, TensorFlow Developer).

Prerequisites

Basic programming knowledge (Python preferred) for the 1-month course; completion of prior modules or equivalent for 3-month and 6-month courses.

Tools & Software

Python

TensorFlow

Keras

scikit-learn

PyTorch

Jupyter Notebook

Flask

AWS

Docker.

Delivery Mode

Combination of lectures, hands-on labs, and project work.

Assessment

Weekly quizzes, lab assignments, and capstone projects.

Certification

Certificate of completion for each module; preparation for industry-recognized certifications in the 6-month course.

Enquire Now WhatsApp

Duration 480 hours
Weekly 20 hours
Level All Levels

Available Curricula

Introduction to AI and ML - 1-month

Duration: 1-month

Select

Intermediate AI and ML - 3 Month

Duration: 3-month

Select

Advanced AI and ML - 6 month

Duration: 6-month

Selected

Course Highlights

Expert instructors with industry experience
Hands-on projects and real-world applications
Flexible learning schedule
Placement assistance

Artificial Intelligence and Machine Learning

Category