Artificial Intelligence and Machine Learning
Artificial Intelligence
| Beginner
- 11 videos | 1h 36m 46s
- Includes Assessment
- Earns a Badge
This course will demystify the world of artificial intelligence (AI) and machine learning (ML), taking you from foundational concepts to practical applications. You'll learn to distinguish AI and ML, explore how algorithms learn, and perform common tasks like classification and clustering. You will begin by learning to confidently distinguish between the broad umbrella of AI and the specific subset of ML, understanding how each contributes to the landscape of intelligent systems. Next, you'll explore the milestones that shaped AI. Then you will discover how to classify the diverse approaches of machine learning. Finally, you will explore the practical aspects of common machine learning problems. You'll learn the meaning of regression, classification, and clustering and how they're applied in real-world scenarios. Discover how to evaluate model performance and explore the workings of popular traditional models like linear regression and decision trees. You'll also be introduced to ensemble learning, where the "wisdom of the crowds" fuels even more accurate predictions.
WHAT YOU WILL LEARN
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Discover the key concepts covered in this course
Recognize the nuances in defining artificial intelligence (ai) and machine learning (ml)
Provide an overview of how machine learning works
Outline important milestones in artificial intelligence over the last few decades
Compare supervised and unsupervised learning techniques
Outline how regression models work and how they can be evaluated -
Outline how classification models work and how they can be evaluated
Outline how clustering models work and how they can be evaluated
Recognize the algorithmic structure of linear regression, logistic regression, and decision tree models
Outline how ensemble techniques are implemented
Summarize the key concepts covered in this course
IN THIS COURSE
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2mIn this video, we will discover the key concepts covered in this course. FREE ACCESS
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10m 9sAfter completing this video, you will be able to recognize the nuances in defining artificial intelligence (AI) and machine learning (ML). FREE ACCESS
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3. Understanding Machine Learning13m 56sUpon completion of this video, you will be able to provide an overview of how machine learning works. FREE ACCESS
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4. A Brief History of Machine Learning11m 36sAfter completing this video, you will be able to outline important milestones in artificial intelligence over the last few decades. FREE ACCESS
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5. Supervised and Unsupervised Learning6m 20sUpon completion of this video, you will be able to compare supervised and unsupervised learning techniques. FREE ACCESS
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6. Regression10m 46sAfter completing this video, you will be able to outline how regression models work and how they can be evaluated. FREE ACCESS
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7. Classification8m 59sUpon completion of this video, you will be able to outline how classification models work and how they can be evaluated. FREE ACCESS
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8. Clustering10m 38sAfter completing this video, you will be able to outline how clustering models work and how they can be evaluated. FREE ACCESS
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9. Linear Regression, Logistic Regression, and Decision Trees11m 18sUpon completion of this video, you will be able to recognize the algorithmic structure of linear regression, logistic regression, and decision tree models. FREE ACCESS
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10. Ensemble Learning8m 42sAfter completing this video, you will be able to outline how ensemble techniques are implemented. FREE ACCESS
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11. Course Summary2m 22sIn this video, we will summarize the key concepts covered in this course. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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