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Industrial Program

Advanced Machine Learning

Master the Future of AI with Advanced Machine Learning Techniques.

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Enroll now for early access of e-LMS

MODE
Online/ e-LMS

TYPE
Self Paced

LEVEL
Moderate

DURATION
12 Weeks

About

The Advanced Machine Learning course is designed for those who wish to deepen their understanding of sophisticated machine learning techniques and their real-world applications. This course covers advanced topics such as ensemble methods, deep reinforcement learning, adversarial training, and transfer learning. Participants will gain hands-on experience with advanced Python programming and popular frameworks like TensorFlow and PyTorch. By the end of the course, learners will be proficient in building and deploying state-of-the-art machine learning models, ready to tackle complex AI challenges.

Aim

To equip advanced learners and professionals with the expertise to implement, optimize, and deploy complex machine learning models, preparing them for cutting-edge challenges in the AI industry.

Program Objectives

Understand and apply advanced machine learning techniques.
Implement ensemble methods, deep reinforcement learning, and adversarial training.
Utilize transfer learning for efficient model development.
Gain proficiency in using TensorFlow and PyTorch for building advanced models.
Develop practical skills through hands-on coding exercises and real-world projects.
Optimize machine learning models for performance and robustness.
Prepare for advanced roles in AI and machine learning through comprehensive training and practical applications.

Program Structure

Introduction to Advanced Machine Learning:

Overview of Advanced Machine Learning Techniques.
Key Concepts and Terminologies.
Applications and Use Cases.

Ensemble Methods:

Bagging and Boosting.
Random Forests.
Gradient Boosting Machines (GBM).
XGBoost and LightGBM.

Deep Reinforcement Learning:

Fundamentals of Reinforcement Learning.
Deep Q-Learning.
Policy Gradients.
Proximal Policy Optimization (PPO).

Adversarial Training:

Understanding Adversarial Examples.
Adversarial Attack Techniques.
Defensive Techniques against Adversarial Attacks.
Robust Model Training.

Transfer Learning:

Concept and Importance of Transfer Learning.
Pre-trained Models and Fine-tuning.
Domain Adaptation.
Case Studies and Applications.

Practical Implementation:

Advanced Python Programming Techniques.
Using TensorFlow for Advanced Model Building.
Implementing Deep Learning Models with PyTorch.
Integrating Models into Real-world Applications.

Participant’s Eligibility

Graduate students in Computer Science and related fields.
Professionals in data science, machine learning, and AI development seeking to deepen their expertise.

Program Outcomes

Master advanced machine learning techniques and concepts.
Implement and optimize complex machine learning models.
Gain hands-on experience with deep reinforcement learning and adversarial training.
Utilize ensemble methods for improved model performance.
Apply transfer learning to leverage pre-trained models.
Develop practical skills with TensorFlow, PyTorch, and advanced Python programming.
Complete real-world projects demonstrating advanced AI applications.
Earn a certificate of completion recognized by industry leaders.

Fee Structure

Standard Fee: INR ~~14,998~~ USD ~~258~~

Discounted Fee: INR 7499 USD 129

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Certificate

Program Assessment

Certification to this program will be based on the evaluation of following assignment (s)/ examinations:

Exam	Weightage
Mid Term Assignments	50 %
Project Report Submission (Includes Mandatory Paper Publication)	50 %

To study the printed/online course material, submit and clear, the mid term assignments, project work/research study (in completion of project work/research study, a final report must be submitted) and the online examination, you are allotted a 1-month period. You will be awarded a certificate, only after successful completion/ and clearance of all the aforesaid assignment(s) and examinations.