Internship on Reinforcement Learning with Python

Internship on Reinforcement Learning with Python: Building Intelligent Agents

This internship offers hands-on experience in developing intelligent agents using Reinforcement Learning (RL) with Python. Participants will explore key RL conc...

Darsha Innovations

English

Certificate Course

1 Students

00h 00m

Course Overview

Overview:

This internship provides a hands-on opportunity to explore Reinforcement Learning (RL), one of the most exciting fields in Artificial Intelligence. Participants will gain practical experience in designing and implementing intelligent agents capable of learning from interactions with their environment using Python-based RL frameworks. This program is ideal for students, researchers, and professionals looking to strengthen their understanding of AI-driven decision-making and automation.

What You Will Learn:

Fundamentals of Reinforcement Learning, Markov Decision Processes (MDPs), and reward-based learning.
Implementation of key RL algorithms such as Q-learning, Deep Q-Networks (DQN), and Policy Gradient methods.
Use of popular RL libraries such as OpenAI Gym, TensorFlow, and PyTorch.
Training agents to solve real-world tasks, including game playing, robotics, and autonomous decision-making.
Hyperparameter tuning, performance evaluation, and optimization techniques in RL.

Who Should Apply?

Undergraduate and graduate students in Computer Science, Data Science, AI, or related fields.
Professionals looking to expand their knowledge in Machine Learning and AI.
Anyone with a basic understanding of Python and an interest in building intelligent systems.

Why Join?

Gain hands-on experience with real-world RL projects.
Learn from industry experts and collaborate with peers.
Strengthen your portfolio with AI-driven applications.
Receive a certificate upon successful completion.

What You Will Learn:

Fundamentals of Reinforcement Learning, Markov Decision Processes (MDPs), and reward-based learning.
Implementation of key RL algorithms such as Q-learning, Deep Q-Networks (DQN), and Policy Gradient methods.
Use of popular RL libraries such as OpenAI Gym, TensorFlow, and PyTorch.
Training agents to solve real-world tasks, including game playing, robotics, and autonomous decision-making.
Hyperparameter tuning, performance evaluation, and optimization techniques in RL.

Who Should Apply?

Undergraduate and graduate students in Computer Science, Data Science, AI, or related fields.
Professionals looking to expand their knowledge in Machine Learning and AI.
Anyone with a basic understanding of Python and an interest in building intelligent systems.

Why Join?

Gain hands-on experience with real-world RL projects.
Learn from industry experts and collaborate with peers.
Strengthen your portfolio with AI-driven applications.
Receive a certificate upon successful completion.

FAQ

FAQ area empty

Course curriculum

Requirment

Outcomes

Instructor

Darsha Innovations

0.0

1 Student
3 Courses
0 Review

View Details

Reviews

Rate this course :

Remove all

₹ 99.00

Buy Now

Students
1
language
English
Duration
00h 00m
Level
advanced