Learn ML With Python Offline
Machine Learning With Python Quick Offline Guide
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App description
Android App Analysis and Review: Learn ML With Python Offline, Developed by Incognito Inventions. Listed in Education Category. Current Version Is 1.0.5, Updated On 06/10/2022 . According to users reviews on Google Play: Learn ML With Python Offline. Achieved Over 12 thousand Installs. Learn ML With Python Offline Currently Has 27 Reviews, Average Rating 4.1 Stars
This FREE app will help you to understand ML With Python Tutorial properly and teach you about how to Start Coding using ML With Python. Here we are covering almost all Classes, Functions, Libraries, attributes, references. The sequential tutorial let you know from basic to advance level.
This "ML With Python Tutorial" is helpful for students to learn Coding step by step from basic to advance level.
***FEATURES***
* FREE of Cost
* Easy to Learn Programming
* ML With Python Basic
* ML With Python Advance
* ML With Python Object Oriented
* ML With Python Offline Tutorial
***LESSONS***
ML With Python Basic Tutorial
Python Ecosystem
Methods for Machine Learning
Data Loading for ML Projects
Understanding Data with Statistics
Understanding Data with Visualization
Preparing Data
Data Feature Selection
Introduction
Logistic Regression
Support Vector Machine(SVM)
Decision Tree
Naïve Bayes
Random Forest
Overview
Linear Regression
Overview
K-Means Algorithm
Mean Shift Algorithm
Hierarchical Clustering
Finding Nearest Neighbors
Performance Metrics
Automatic Workflows
Improving Performance of ML Models
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We are currently offering version 1.0.5. This is our latest, most optimized version. It is suitable for many different devices. Free download directly apk from the Google Play Store or other versions we're hosting. Moreover, you can download without registration and no login required.
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Changelog / What's New
Decision Tree
Naïve Bayes
Random Forest
Linear Regression
K-Means Algorithm
Mean Shift Algorithm
Hierarchical Clustering
Finding Nearest Neighbors
Performance Metrics
Automatic Workflows
Improving Performance of ML Models
Naïve Bayes
Random Forest
Linear Regression
K-Means Algorithm
Mean Shift Algorithm
Hierarchical Clustering
Finding Nearest Neighbors
Performance Metrics
Automatic Workflows
Improving Performance of ML Models