Machine Learning with Python: SVM, Kmeans, KNN, LinReg, PCA, DBS

[LeeAndro]

Created by Shrirang Korde | Last updated 5/2021
Duration: 14h 30m | 4 sections | 55 lectures | Video: 1280x720, 44 KHz | 6.66 GB
Genre: eLearning | Language: English + Sub

The course covers Machine Learning in exhaustive way.

Hands-on Machine Learning

Applications of Machine Learning to various data, Unsupervised Learning, Supervised Learning

simple programming knowledge is added advantage

The presentations and hands-on practical are made such that it's made easy. The knowledge gained through this tutorial series can be applied to various real world scenarios.

UnSupervised learning does not require to supervise the model. Instead, it allows the model to work on its own to discover patterns and information that was previously undetected. It mainly deals with the unlabeled data. The machine is forced to build a compact internal representation of its world and then generate imaginative content.

Supervised learning deals with providing input data as well as correct output data to the machine learning model. The goal of a supervised learning algorithm is to find a mapping function to map the input with the output. It infers a function from labeled training data consisting of a set of training examples.

UnSupervised Learning and Supervised Learning are dealt in-detail with lots of bonus topics.

The course contents are given below:

Introduction to Machine Learning

Introductions to Deep Learning

Installations

Unsupervised Learning

Clustering, Association

Agglomerative, Hands-on

DBSCAN, Hands-on

Mean Shift, Hands-on

K Means, Hands-on

Association Rules, Hands-on

(PCA: Principal Component Analysis)

Supervised Learning

Regression, Classification

Train Test Split, Hands-on

k Nearest Neighbors, Hands-on

kNN Algo Implementation

Support Vector Machine (SVM), Hands-on

Support Vector Regression (SVR), Hands-on

SVM (non linear svm params), Hands-on

SVM kernel trick, Hands-on

SVM mathematics

Linear Regression, Hands-on

Gradient Descent overview

One Hot Encoding (Dummy vars)

One Hot Encoding with Linear Regr, Hands-on

Info about Datasets




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