Artificial Intelligence
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20 Hours
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96 Students
Data-Driven Artificial Intelligence Fundamentals
Join over 118,480 students in this definitive guide to Artificial Intelligence. Created by Dr. Chuck Severance at Yale University, this Masterclass covers everything from fundamental principles to highly advanced techniques, featuring real-world proj
Lead Faculty
Lead Instructor, Xovix
Labs
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$16.99
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Core Objectives
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Understand the fundamental principles of Artificial Intelligence
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Apply machine learning and deep learning techniques to real-world problems
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Develop and deploy AI models using industry-standard tools and frameworks
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Integrate AI with data analytics to drive business insights and decision-making
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Explore the latest advancements in AI research and development
Who this is for
"Data Scientists and Analysts
Business Leaders and Strategists
AI and Machine Learning Engineers
IT and Technology Professionals
Researchers and Academics"
Course Overview
Unlock the power of Data-Driven Artificial Intelligence with this comprehensive Masterclass, crafted by the renowned Dr. Chuck Severance at Yale University. With over 118,480 students already enrolled, this course is the definitive guide to Artificial Intelligence, covering the fundamental principles and advancing to highly sophisticated techniques. Through a combination of real-world projects and comprehensive assignments, you will gain hands-on experience in applying AI concepts to drive business success and innovation. From the basics of machine learning to the latest advancements in deep learning, this course provides a thorough understanding of the AI landscape, empowering you to make data-driven decisions and drive meaningful impact in your organization. Whether you're a seasoned professional or just starting your AI journey, this Masterclass is your gateway to unlocking the full potential of Artificial Intelligence and transforming your career.
Syllabus
Module 1
Introduction to Artificial Intelligence
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Module 1
Introduction to Artificial Intelligence
1.1
History and Evolution of AI
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1.2
AI Applications and Future
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1.3
AI and Data Science
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Module 2
Machine Learning Fundamentals
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Module 2
Machine Learning Fundamentals
2.1
Supervised Learning
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2.2
Unsupervised Learning
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2.3
Deep Learning
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Module 3
Data Preprocessing and Visualization
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Module 3
Data Preprocessing and Visualization
3.1
Data Cleaning and Preprocessing
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3.2
Data Visualization
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3.3
Feature Engineering
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Module 4
Deep Learning with Python
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Module 4
Deep Learning with Python
4.1
Introduction to TensorFlow
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4.2
Building Neural Networks with Keras
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4.3
Convolutional Neural Networks with Python
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Module 5
Natural Language Processing
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Module 5
Natural Language Processing
5.1
Introduction to NLP
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5.2
Text Preprocessing
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5.3
Sentiment Analysis
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