machine learning

In this chapter, we'll define machine learning and its relation to data science and artificial intelligence. Then, we'll unpack important machine learning jargon and end with the machine learning workflow for building models.

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Created by Last updated Wed, 08-Apr-2020 English
What will i learn?
  • On completion of the course students will be expected to: Have a good understanding of the fundamental issues and challenges of machine learning: data, model selection, model complexity, etc. Have an understanding of the strengths and weaknesses of many popular machine learning approaches. Appreciate the underlying mathematical relationships within and across Machine Learning algorithms and the paradigms of supervised and un-supervised learning. Be able to design and implement various machine learning algorithms in a range of real-world applications

Curriculum for this course
2 Lessons 00:00:00 Hours
machine learning
2 Lessons 00:00:00 Hours
  • machine learning
  • machine learning 00:00:00
Requirements
  • #1: Large, diverse data sets The development of a machine learning algorithm depends on large volumes of data, from which the learning process draws many entities, relationships, and clusters. To broaden and enrich the correlations made by the algorithm, machine learning needs data from diverse sources, in diverse formats, about diverse business processes. For the most comprehensive learning experience, you should provide diverse training data -- integrated from multiple sources and concerning various business entities, collected across multiple time frames -- to make algorithmic assessments more real-world, accurate, and successful in production. Once in production, a machine learning algorithm continues to read large, diverse data sets to keep its model up-to-date and growing. Savvy organizations are deploying tools for multiple types of analytics (not just machine learning), because each type tells them something unique and valuable. Each of these analytics approaches needs data that is prepared and presented in a certain way that is optimal for the analytics tool or the user practice involved. Machine learning algorithms are almost always optimized for raw, detailed source data. Thus, the data environment must provision large quantities of raw data for discovery-oriented analytics practices such as data exploration, data mining, statistics, and machine learning.
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Description

Machine Learning (ML) is basically that field of computer science with the help of which computer systems can provide sense to data in much the same way as human beings do. In simple words, ML is a type of artificial intelligence that extract patterns out of raw data by using an algorithm or method. The key focus of ML is to allow computer systems to learn from experience without being explicitly programmed or human intervention.

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