Learn about different types of machine learning.
- [Instructor] There are two major types…of machine learning, supervised and unsupervised.…Supervised machine learning is aided…by examples of inputs and their desired outputs.…In this situation a machine learning or ML program tweaks…its algorithm until it can produce best results…based on the given examples.…Here the roll of the algorithm is to capture a general rule…that best maps the inputs to their corresponding outputs.…
We separate the example dataset under supervised ML…into training and test datasets.…As their names suggest the training dataset is only used…for teaching an ML algorithm,…while the test dataset is for evaluating the effectiveness…of the same ML algorithm after training is over.…We keep our test datasets much smaller…then their counterpart for training…because our focus needs to be on training…rather then testing to accomplish as high accuracy…as possible.…
Let's say that your ML goal is to automate the process…of differentiating oranges, apples and bananas.…In this case you can provide a training dataset…
- 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
Course organization1m 17s
1. Introduction to Data Science
2. Cloud Computing
3. Distributed File Systems
4. Distributed Processing
5. Machine Learning
6. Case Study
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