Learn about the concept and importance of machine learning.
- Machine Learning or ML is one of the major tools of the trade for data scientists. ML is a branch of Artificial Intelligence or AI. AI is a field that studies ways for computers to mimic human intelligence. In fact, ML is one of the mechanisms that implements the idea of AI. In essence, ML is an algorithm manifested in the form of a computer program that can learn from experience to improve its performance.
ML has a significance in data science because its performance is largely driven by and dependent on the quality and quantity of data available. By the way, if you haven't already noticed, ML gets its experience by the data fed into its software. Take fraud detection. You have so many data points to consider such as customer gender, purchase and payment history, location, time, among other things.
There are also tens of millions of customers you need to keep track of at once. And the time is of the essence here. The sooner you detect the fraud, the less damage a criminal can inflict. Processing that much data at such a high speed is well beyond human capacity and ability and ML is a great solution to this problem. It can very easily monitor numerous targets at the same time and is extremely good at number crunching.
The precision of detection gets better and better as ML can look back on previous decisions and fine tunes its parameters to produce a more accurate detection result. This process of reflection and correction is what we call learning in ML. However, ML alone cannot accomplish this impressive feat. It needs all the necessary infrastructures to be able to handle the volume, velocity, and voracity requirements of adequately processing input data.
And this is only made possible by the use of tools such as cloud computing, distributed systems like Hadoop and distributed processing solutions such as Spark in addition to ML.
Author
Released
8/30/2018- Enabling technologies in data science
- Cloud computing and virtualization
- Installing and working with Proxmox, Hadoop, Spark, and Weka
- Managing virtual machines on Proxmox
- Distributed processing with Spark
- Fundamental applications of machine learning
- Distributed systems and distributed processing
- How Hadoop, Spark, and Weka can work together
Skill Level Beginner
Duration
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Introduction
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Course organization1m 17s
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1. Introduction to Data Science
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Introduction1m 51s
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Data science2m 53s
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Fundamental skills3m 42s
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Enabling technologies2m 4s
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2. Cloud Computing
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Cloud fundamentals3m 29s
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Types of cloud3m 19s
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Solution providers2m 22s
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Proxmox: Installation2m 26s
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3. Distributed File Systems
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Distributed file systems2m 44s
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Fundamentals2m 45s
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Hadoop hands-on2m 8s
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Hadoop: Preparation4m 11s
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Hadoop: Installation4m 18s
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Hadoop: MapReduce hands-on8m 52s
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4. Distributed Processing
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Spark: Installation6m 24s
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Spark: Spark shell4m 28s
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Spark: pyspark4m 32s
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Spark: Application4m 1s
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5. Machine Learning
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Machine learning2m 41s
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Fundamentals2m 16s
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Types of machine learning2m 59s
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Weka: Installation2m 33s
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Weka: GUI3m 35s
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Weka: Training vs. testing3m 21s
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Weka: Clustering2m 12s
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6. Case Study
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Putting it all together2m 42s
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Hadoop cluster: Operation4m 57s
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Spark, YARN, and Hadoop6m 42s
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Weka and Spark3m 12s
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Conclusion
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Next steps41s
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Video: Machine learning