What you'll learn
Applications of Machine Learning to various data, Unsupervised Learning, Supervised Learning
Requirements
working knowledge of python
Description
The course covers Machine Learning in exhaustive way. 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 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
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
Who this course is for:
python programmers, C/C++ programmers, working of scripting (like jаvascript), fresh developers and intermediate level programmers who want to learn Machine Learning

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