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In this video, What Is Q-Learning in Reinforcement Learning, we’re diving into the essentials of Q-learning, one of the most powerful techniques in Artificial Intelligence. Q-learning helps an agent learn the best way to make decisions by exploring an environment, gathering feedback from rewards and penalties, and constantly improving its actions even without knowing the rules beforehand. We’ll start by explaining Q-values and how they help an agent understand the “quality” of each action in any given state. Then, we’ll dive into rewards and how the agent learns to maximize them, moving from random actions to more strategic choices as it gains experience. .We’ll also explore the concept of exploration vs. exploitation with the ε-greedy policy. You’ll understand how Q-learning helps agents learn and adapt in complex environments
00:00 Introduction
01:40 What is Q-Learning ?
05:15 Key Components of Q-Learning
13:49 Determining Q-Value
16:13 Implementing Q-Learning Algorithm with Python
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➡️ About Post Graduate Program in AI and Machine Learning
Boost your career with this AI and ML Certification program, delivered in collaboration with Purdue University and IBM. Learn in-demand skills such as machine learning, deep learning, NLP, computer vision, reinforcement learning, generative AI, prompt engineering, ChatGPT, and many more.
Key Features:
✅ Program completion certificate from Purdue University and Simplilearn
✅ Live-online masterclasses delivered by Purdue faculty and IBM experts
✅ Live interactive sessions on the latest AI trends, such as ChatGPT, generative AI, prompt engineering, and more
✅ Core curriculum delivered in live classes by industry experts
✅ Simplilearn's JobAssist helps you get noticed by top hiring companies
✅ Gain exposure to ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools
✅ 3 capstones and 25+ hands-on projects from various industry domains
✅ Access to Purdue’s alumni association membership on program completion
✅ Exclusive hackathons and Ask Me Anything sessions by IBM
Skills Covered:
✅ Generative AI
✅ Prompt Engineering
✅ ChatGPT
✅ Explainable AI
✅ Conversational AI
✅ Large Language Models
✅ Supervised and Unsupervised Learning
✅ Model Training and Optimization
✅ Model Evaluation and Validation
✅ Ensemble Methods
✅ Deep Learning
✅ Natural Language Processing
✅ Computer Vision
✅ Reinforcement Learning
✅ Speech Recognition
✅ Machine Learning Algorithms
Learning Path:
✅ PG AIML - Program Induction
✅ PG AIML: Programming Refresher
✅ PG AIML - Python for Data Science (IBM)
✅ PG AIML - Applied Data Science with Python
✅ PG AIML - Machine Learning
✅ PG AIML - Deep Learning with TensorFlow (IBM)
✅ PG AIML - Deep Learning Specialization
✅ PG AIML - Essentials of Generative AI, Prompt Engineering & ChatGPT
✅ PG AIML - Capstone Project
Electives:
✅ Academic Masterclass by Purdue University Online
✅ Industry Masterclass by IBM
✅ PG AIML - ADL & Computer Vision
✅ PG AIML - NLP and Speech Recognition
✅ PG AIML - Reinforcement Learning
✅ PG AIML - Advanced Generative AI
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