Clustering & Classification With Machine Learning in Python
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Clustering & Classification With Machine Learning in Python at Eduonix Overview
Duration | 38 hours |
Mode of learning | Online |
Schedule type | Self paced |
Difficulty level | Intermediate |
Credential | Certificate |
Clustering & Classification With Machine Learning in Python at Eduonix Highlights
- Start instantly and learn at your own schedule.
- Lifetime Access. No Limits!
- A great course for learning Machine Learning
- Self paced Course
Clustering & Classification With Machine Learning in Python at Eduonix Course details
- This course covers main aspect of practical data science and if you take this course, you can do away with taking other courses or buying books on Python based data science.This course will give you a robust grounding in the main aspects of machine learning- clustering & classification.
Clustering & Classification With Machine Learning in Python at Eduonix Curriculum
Section 1 : INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools
Welcome to Clustering & Classification with Machine Learning in Python
What is Machine Learning?
Data and Scripts For the Course
Python Data Science Environment
For Mac Users
Introduction to IPython
IPython in Browser
Python Data Science Packages To Be Used
Section 2 : Read in Data From Different Sources With Pandas
What are Pandas?
Read in Data from CSV
Read in Online CSV
Read in Excel Data
Read in HTML Data
Read in Data from Databases
Section 3 : Data Cleaning & Munging
Remove Missing Values
Conditional Data Selection
Data Grouping
Data Subsetting
Ranking & Sorting
Concatenate
Merging & Joining Data Frames
Section 4 : Unsupervised Learning in Python
Unsupervised Classification- Some Basic Concepts
K-Means Clustering:Theory
Implement K-Means on the Iris Data
Quantifying K-Means Clustering Performance
K-Means Clustering with Real Data
How To Select the Optimal Number of Clusters?
Gaussian Mixture Modelling (GMM)
Hierarchical Clustering-theory
Hierarchical Clustering-practical
Section 5 : Dimension Reduction & Feature Selection for Machine Learning
Principal Component Analysis (PCA)- Theory
Principal Component Analysis (PCA)-Case Study 1
Principal Component Analysis (PCA)-Case Study 2
Linear Discriminant Analysis(LDA) for Dimension Reduction
t-SNE Dimension Reduction
Feature Selection to Select the Most Relevant Predictors
Recursive Feature Elimination (RFE)
Section 6 : Supervised Learning: Classification
Concepts Behind Supervised Learning
Data Preparation for Supervised Learning
Pointers on Evaluating the Accuracy of Classification Modelling
Using Logistic Regression as a Classification Model
kNN- Classification
Naive Bayes Classification
Linear Discriminant Analysis
SVM- Linear Classification
Non-Linear SVM Classification
RF Classification
Gradient Boosting Machine (GBM)
Voting Classifier
Section 7 : Neural Networks and Deep Learning Based Classification Techniques
Perceptrons for Binary Classification
Artificial Neural Networks (ANN) for Binary Classification
Multi-class Classification With MLP
Introduction to H20
Use H20 for Deep Learning Classification
Specify the Activation Function
H20 Deep Learning for Classification
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