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The basic phases of machine learning workflows include data collection, data pre-processing, building datasets, model training and refinement, evaluation, and deployment to production.
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5 Steps to ML Made Easy
Machine Learning is never complete with one coding language. It works best with other advantages combined from different coding language
The basic phases of machine learning workflows includes data collection, data pre-processing, building datasets, model training and refinement, evaluation, anddeployment to production
Therefore to operate an ML workflow, involves lot of data-science concepts to base the entire flow and this asks to strengthen basic mathematics to start with
Step - 1 Topics to brush up are- • Basic Algebra • Probability • Statistics • Linear Algebra • Linear Regression Never let mathematicsoverwhelm you, cause experts say you will only require 10% of it to apply on ML workflow
To get started with Machine Learning you have to be thorough with algorithms, how they are categorized and applied to get varied end results
Step - 2 These are the three basic models based on which Machine Learning predictions are made from the given dataset
Step - 3 There are around 10 languages which are considered best fit for ML use and for beginners, Python is preferred
Data analysis and processing which involves collection, cleaning or wrangling, storage, analysis, and also visualization is a critical process for ML, here require trained data engineers to perform the tasks
Step - 4 These are the toolsused for the data wrangling anḍ analytics process.
Many new learners find it difficult to transition to an ML job role and the underlying reason is due to lack of confidence and practice with real time data set.
Step - 5 Let’s get ready for the final run!These are the three ways you can start practicing... • Targeted Practice • Working Out the Entire ML Workflow • Practice on Real Datasets
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