Clustering & Classification With Machine Learning In Python
4.2 (162 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
2,645 students enrolled

Clustering & Classification With Machine Learning In Python

Harness The Power Of Machine Learning For Unsupervised & Supervised Learning In Python
4.2 (162 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
2,645 students enrolled
Created by Minerva Singh
Last updated 12/2019
English
English [Auto]
Current price: $139.99 Original price: $199.99 Discount: 30% off
5 hours left at this price!
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This course includes
  • 6 hours on-demand video
  • 1 article
  • 2 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Harness The Power Of Anaconda/iPython For Practical Data Science
  • Read In Data Into The Python Environment From Different Sources
  • Carry Out Basic Data Pre-processing & Wrangling In Python
  • Implement Unsupervised/Clustering Techniques Such As k-means Clustering
  • Implement Dimensional Reduction Techniques (PCA) & Feature Selection
  • Implement Supervised Learning Techniques/Classification Such As Random Forests In Python
  • Neural Network & Deep Learning Based Classification
Course content
Expand all 57 lectures 05:49:31
+ Read in Data From Different Sources With Pandas
6 lectures 46:17
What are Pandas?
12:06
Read in Data from CSV
05:42
Read in Online CSV
03:42
Read in Excel Data
05:31
Read in HTML Data
12:06
Read in Data from Databases
07:10
+ Data Cleaning & Munging
7 lectures 01:02:29
Remove Missing Values
10:28
Conditional Data Selection
05:24
Data Grouping
09:47
Data Subsetting
09:44
Ranking & Sorting
08:03
Concatenate
08:16
Merging & Joining Data Frames
10:47
+ Unsupervised Learning in Python
9 lectures 44:43
Unsupervised Classification- Some Basic Concepts
01:38
K-Means Clustering:Theory
02:31
Implement K-Means on the Iris Data
08:01
Quantifying K-Means Clustering Performance
03:53
K-Means Clustering with Real Data
04:16
How To Select the Optimal Number of Clusters?
05:38
Gaussian Mixture Modelling (GMM)
05:17
Hierarchical Clustering-theory
04:10
Hierarchical Clustering-practical
09:19
+ Dimension Reduction & Feature Selection for Machine Learning
7 lectures 27:49
Principal Component Analysis (PCA)-Case Study 1
03:52
Principal Component Analysis (PCA)-Case Study 2
04:11
Linear Discriminant Analysis(LDA) for Dimension Reduction
05:13
t-SNE Dimension Reduction
03:49
Feature Selection to Select the Most Relevant Predictors
04:44
Recursive Feature Elimination (RFE)
03:23
+ Supervised Learning: Classification
12 lectures 01:22:56
Data Preparation for Supervised Learning
09:47
Pointers on Evaluating the Accuracy of Classification Modelling
09:42
Using Logistic Regression as a Classification Model
08:26
kNN- Classification
07:46
Naive Bayes Classification
06:05
Linear Discriminant Analysis
03:48
SVM- Linear Classification
03:10
Non-Linear SVM Classification
02:06
RF-Classification
12:02
Gradient Boosting Machine (GBM)
05:54
Voting Classifier
04:00
+ Neural Networks and Deep Learning Based Classification Techniques
7 lectures 27:28
Perceptrons for Binary Classification
04:27
Artificial Neural Networks (ANN) for Binary Classification
03:26
Multi-class Classification With MLP
04:53
Introduction to H20
04:14
Use H20 for Deep Learning Classification
03:20
Specify the Activation Function
02:06
H20 Deep Learning for Classification
05:02
+ Miscellaneous Information
1 lecture 07:13
Using Colabs for Online Jupyter Notebooks
07:13
Requirements
  • Be Able To Operate & Install Software On A Computer
  • Prior Exposure To Common Machine Learning Terms Such As Unsupervised & Supervised Learning
Description

HERE IS WHY YOU SHOULD TAKE THIS COURSE:

This course your complete guide to both supervised & unsupervised learning using Python. This means, this course covers all the main aspects 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.

 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 & supervised learning in Python, you can give your company a competitive edge and boost your career to the next level.

LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE:

My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I also just recently finished a PhD at Cambridge University.

I have several years of experience in analyzing real life data from different sources  using data science techniques and producing publications for international peer reviewed journals.

Over the course of my research I realized almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic .

This course will give you a robust grounding in the main aspects of machine learning- clustering & classification. 

Unlike other Python instructors, I dig deep into the machine learning features of Python and gives you a one-of-a-kind grounding in Python Data Science!

You will go all the way from carrying out data reading & cleaning  to machine learning to finally implementing simple deep learning based models using Python

THE COURSE COMPOSES OF 7 SECTIONS TO HELP YOU MASTER PYTHON MACHINE LEARNING:

• A full introduction to Python Data Science and powerful Python driven framework for data science, Anaconda • Getting started with Jupyter notebooks for implementing data science techniques in Python  • Data Structures and Reading in Pandas, including CSV, Excel and HTML data • How to Pre-Process and “Wrangle” your Python data by removing NAs/No data, handling conditional data, grouping by attributes, etc. 

• Machine Learning, Supervised Learning, Unsupervised Learning in Python

• Artificial neural networks (ANN) and Deep Learning. You’ll even discover how to use artificial neural networks and deep learning structures for classification! 

With such a rigorous grounding in so many topics, you will be an unbeatable data scientist by the end of the course.

NO PRIOR PYTHON OR STATISTICS OR MACHINE LEARNING KNOWLEDGE IS REQUIRED:

You’ll start by absorbing the most valuable Python Data Science basics and techniques.

I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in Python.

My course will help you implement the methods using real data obtained from different sources.

After taking this course, you’ll easily use packages like Numpy, Pandas, and Matplotlib to work with real data in Python..

You’ll even understand concepts like unsupervised learning, dimension reduction and supervised learning.. I will even introduce you to deep learning and neural networks using the powerful H2o framework! 

Most importantly, you will learn to implement these techniques practically using Python. You will have access to all the data and scripts used in this course. Remember, I am always around to support my students!

JOIN MY COURSE NOW!

Who this course is for:
  • Students Interested In Getting Started With Data Science Applications In The Python Environment
  • People Wanting To Master The Anaconda iPython Environment For Data Science & Scientific Computations
  • Students Wishing To Learn The Implementation Of Unsupervised Learning On Real Data Using Python
  • Students Wishing To Learn The Implementation Of Supervised Learning (Classification) On Real Data Using Python
  • Students Looking To Get Started With Artificial Neural Networks & Deep Learning