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Machine learning foundations: a case study approach.
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Learn basic data visualization principles and how to apply them using ggplot2.
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Applications and examples of machine learning.
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A very deep and comprehensive course for learning some of the core basics of machine learning.
This course is the first in the micromasters program and will prepare you for modeling compartmentalization and regression problems with statistical and machine learning methods.
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How to complete a machine learning course on Coursera?
Follow the rest of the instructions on this page to complete your program. When you are done, instead of uploading your code, you will answer a series of quiz questions (see the quiz after this reading) to document your completion of this assignment. The instructions will indicate what data to collect for answering the quiz.
How to use mL in a case study?
Use various ML Methods in different case studies Regression Case Study 1: Predicting house prices Classification Case Study 2: Sentiment Analysis Clustering Case Study 3: Document retrieval Matrix Factorization Case Study 4: Product recommendation Deep Learning Cases Study 5: Visual product recommender Wk 2 Regression
How to write a case study for machine learning?
-Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models.
What are the results of the machine learning quiz?
There are 3 results you need to gather along the way to enter into the quiz after this reading. Selection and summary statistics: In the notebook we covered in the module, we discovered which neighborhood (zip code) of Seattle had the highest average house sale price.
Last Update: Oct 2021
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20.10.2021 02:18
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Machine-learning-foundations-a-case-study-approach week 1 first appearance getting started with python to discharge notebooks once preceding is installed calendar week 2 regression: predicting house prices calendar week 3 classification: analyzing sentiment week 4 clustering and similarity: retrieving documents calendar week 5 recommender system.
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