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Udemy Clustering and Classification with Machine Learning in Python

Udemy Clustering and Classification with Machine Learning in Python

Added Date: February 01, 2020

Downloads: 1466

Free

Udemy Clustering and Classification with Machine Learning in Py / Specification

Manufacturer: Udemy Yodemi Link to
English Name: Udemy Clustering and Classification with Machine Learning in Python
Name Classification And Clustering Training With Machine Learning In Python
Language Of Instruction:English
Teacher: Minerva Singh
Educational Level:Primary, Secondary
Training Time:5 Hours + 21 Minutes
Size:923 MB
Release Date:02:55|2020.02.01
Rating: 2.5 Star(s) 2.5/5

Udemy Clustering and Classification with Machine Learning in Py / Screenshots

Udemy Clustering and Classification with Machine Learning in Python
Udemy Clustering and Classification with Machine Learning in Python
Udemy Clustering and Classification with Machine Learning in Python
Udemy Clustering and Classification with Machine Learning in Python
Udemy Clustering and Classification with Machine Learning in Python

Udemy Clustering and Classification with Machine Learning in Py / Description

In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal. By becoming proficient in unsupervised and supervised learning in Python, you can give your company a competitive edge and level up in your career. This course will give you a robust grounding in clustering and classification, the main aspects of machine learning.

The course consists of 7 sections that will help you master Python machine learning. You’ll begin with an introduction to Python data science and Anaconda, which is a powerful Python-driven framework for data science. Next, you'll delve into Pandas and read data structures, including CSV, Excel, and HTML data. As you advance, you’ll perform data cleaning and munging to remove NAs\no data and discover how to handle conditional data, group by attributes, and do much more. You’ll also grasp basic concepts of unsupervised learning such as K-means clustering and its implementation on the Iris dataset. The course will take you through the theory of dimension reduction and feature selection for machine learning and help you understand Principal Component Analysis (PCA) using two case studies. You’ll get to grips with the linear and non-linear classification of SVM along with Gradient Boosting Machine (GBM) and Naive Bayes Classification. Finally, you’ll explore neural networks and discover the powerful H20 framework and for deep learning classification. Additionally, you’ll learn about perceptrons and Artificial Neural Networks (ANN) for binary classification.


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