04.04.2022, 19:09
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Real World Data Science Project : Plant Disease Prediction
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 58.0 GB | Duration: 121h 19m
Solve business problems using data science, machine learning practically and build real world projects using python
What you'll learn
Make powerful analysis, Make robust Machine Learning models
Master Machine Learning on Python
Know which Machine Learning model to choose for each type of problem
Implement Machine Learning Algorithms
Explore how to deploy your machine learning models.
Understand the full product workflow for the machine learning lifecycle.
Present Data Science projects to management
Real life case studies and projects to understand how things are done in the real world
Build a portfolio of work to have on your resume
Requirements
Basic knowledge of machine learning
Description
"Algorithms that parse data, learn from that data, and then apply what they've learned to make informed decisions"
An easy example of a machine learning algorithm is an on-demand music streaming service. For the service to make a decision about which new songs or artists to recommend to a listener, machine learning algorithms associate the listener's preferences with other listeners who have a similar musical taste. This technique, which is often simply touted as AI, is used in many services that offer automated recommendations.
Machine learning fuels all sorts of automated tasks that span across multiple industries, from data security firms that hunt down malware to finance professionals who want alerts for favorable trades. The AI algorithms are programmed to constantly be learning in a way that simulates as a virtual personal assistant-something that they do quite well.
Machine learning involves a lot of complex math and coding that, at the end of the day, serves a mechanical function the same way a flashlight, a car, or a computer screen does. When we say something is capable of "machine learning", it means it's something that performs a function with the data given to it and gets progressively better over time. It's like if you had a flashlight that turned on whenever you said "it's dark," so it would recognize different phrases containing the word "dark."
Who this course is for
Beginners in data science
Homepage
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