English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 01m | 156 MB
Data science is opening up exciting new opportunities, especially for Microsoft developers who want to take advantage of the artificial intelligence and machine learning capabilities of Azure. This introductory course provides an overview of the basic concepts underlying Azure Machine Learning. Learn the difference between supervised, unsupervised, and reinforcement learning and important factors that impact the success of any data science project: the quality of the data you use, the questions you ask, and the predictions you make. Instructor Sahil Malik also reviews some of the machine learning algorithms—clustering, anomaly detection, classification, and regression—that are most relevant to Azure.
Topics include:
- Artificial intelligence and Azure Machine Learning
- Supervised vs. unsupervised learning
- Reinforcement learning
- Data quality
- Making predictions
- Machine learning algorithms
Table of Contents
Introduction
1 Welcome
2 What you should know
The Landscape of AI
3 What is AI
4 What is Azure machine learning
5 Why is AI suddenly so relevant
What Is Machine Learning
6 What is machine learning
7 Supervised learning
8 Unsupervised machine learning
9 Reinforcement learning
Data Science for Absolute Beginners
10 How good is your data
11 Ask the right question
12 Predict an answer
Machine Learning Algorithms
13 Clustering
14 Anomaly detection
15 Classification
16 Regression
Conclusion
17 Next steps
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