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Tool Learning 101 Family Bootcamp Class Intro to AI
Requirements
Python 101 (3-10 hours)
Information Discipline 101 (3-10 hours)
Business in Data Discipline (3-10 hours)
Description
Organization Learning 101 League Bootcamp Series NYC
Python Scikit-learn Accumulation
Supervised vs Unattended Acquisition
Regression vs Sorting models
Unqualified vs Continuous flick spaces
Carving Bedrock: Test-train tear, Sweep proof(CV), Bias-variance tradeoff, Precision and Resemble, Collection models
Rendering Results of Reversal and Arrangement Models (Keeping On)
Parameters and Hyper Parameters
SVM, K-Nearest Edge, Neuronal Networks
Magnitude Change
Guardianship on:
Savvy and Rendition results of Abnormalcy and Logistic Retrogression using Google Spreadsheets and Python
Conniving R-Square, MSE, Logit manually in surpass for enhanced module (Doubled Reversion)
Savvy features of Favourite Datasets: Titanic, Iris (Scikit) and Lodging Prices
Operative Provision Retrogression on Titanic Aggregation Set
Lengthwise Retrogression, Supplying Regression, SVM and Haphazard Plant on Iris Dataset
Who this bed is for:
Python and Collection Analytics
Programmers with no noesis of Maths
New Entrants in Aggregation Discipline Champaign
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