Artificial Intelligence
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30 Hours
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130 Students
Data-Driven Artificial Intelligence Fundamentals
Join over 414,384 students in this definitive guide to Artificial Intelligence. Created by Rob Percival at MIT, this Specialization covers everything from fundamental principles to highly advanced techniques, featuring real-world projects and compreh
Lead Faculty
Lead Instructor, Xovix
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$14.99
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Core Objectives
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Understand the fundamental principles of Artificial Intelligence
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Apply data-driven techniques to AI model development
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Design and deploy AI solutions for real-world problems
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Develop skills in programming languages such as Python and R
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Work with popular AI frameworks and libraries
Who this is for
"Data Scientists
Machine Learning Engineers
Business Analysts
AI Enthusiasts
Software Developers"
Course Overview
Unlock the full potential of Artificial Intelligence with this comprehensive course, crafted by renowned expert Rob Percival at MIT. Over 414,384 students have already embarked on this transformative journey, and now it's your turn. In this definitive guide to Data-Driven Artificial Intelligence Fundamentals, you'll delve into the core principles, explore cutting-edge techniques, and apply your knowledge to real-world projects and assignments. With a focus on hands-on learning, you'll gain practical experience in designing, developing, and deploying AI solutions that drive business value and spark innovation. As you progress through the course, you'll discover how to harness the power of data to inform and optimize your AI models, and develop a deeper understanding of the complex relationships between data, algorithms, and outcomes. Whether you're a seasoned professional or an aspiring AI enthusiast, this course will equip you with the skills, knowledge, and expertise to succeed in this rapidly evolving field.
Syllabus
Module 1
Introduction to Artificial Intelligence
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Module 1
Introduction to Artificial Intelligence
1.1
What is Artificial Intelligence?
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1.2
History of Artificial Intelligence
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1.3
Applications of Artificial Intelligence
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Module 2
Machine Learning Fundamentals
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Module 2
Machine Learning Fundamentals
2.1
Introduction to Machine Learning
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2.2
Supervised Learning
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2.3
Unsupervised Learning
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Module 3
Deep Learning Fundamentals
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Module 3
Deep Learning Fundamentals
3.1
Introduction to Deep Learning
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3.2
Convolutional Neural Networks (CNNs)
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3.3
Recurrent Neural Networks (RNNs)
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Module 4
Data Preprocessing
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Module 4
Data Preprocessing
4.1
Introduction to Data Preprocessing
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4.2
Data Cleaning
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4.3
Data Transformation
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Module 5
Machine Learning Algorithms
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Module 5
Machine Learning Algorithms
5.1
Introduction to Machine Learning Algorithms
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5.2
Linear Regression
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5.3
Decision Trees
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