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Clustering & Classification With Machine Learning in Python

With so many Python based Data Science & Machine Learning courses around, why should you take this course? As the title name suggests- this course your complete guide to both supervised & unsupervi...

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Course Description

With so many Python based Data Science & Machine Learning courses around, why should you take this course? As the title name suggests- this course your complete guide to both supervised & unsupervised learning using Python. This means, this course covers 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...

With so many Python based Data Science & Machine Learning courses around, why should you take this course? As the title name suggests- this course your complete guide to both supervised & unsupervised learning using Python. This means, this course covers 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. THIS IS MY PROMISE TO YOU COMPLETE THIS ONE COURSE & BECOME A PRO IN PRACTICAL PYTHON BASED MACHINE LEARNING

What you’ll learn
  • You learn how to code in C# and build video game levels.
  • know about Photoshop to make game art. One of the best features is that you can watch the courses at any speed you want.
  • Learning how to code is a great way to jump in a new career or enhance your current career

Covering Topics

1
Section 1 : INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools
2
Section 2 : Read in Data From Different Sources With Pandas
3
Section 3 : Data Cleaning & Munging
4
Section 4 : Unsupervised Learning in Python
5
Section 5 : Dimension Reduction & Feature Selection for Machine Learning
6
Section 6 : Supervised Learning: Classification
7
Section 7 : Neural Networks and Deep Learning Based Classification Techniques

Curriculum

      Section 1 : INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools
1
Welcome to Clustering & Classification with Machine Learning in Python Preview
2
What is Machine Learning?
3
Data and Scripts For the Course
4
Python Data Science Environment
5
For Mac Users
6
Introduction to IPython
7
IPython in Browser
8
Python Data Science Packages To Be Used
      Section 2 : Read in Data From Different Sources With Pandas
9
What are Pandas? Preview
10
Read in Data from CSV
11
Read in Online CSV
12
Read in Excel Data
13
Read in HTML Data
14
Read in Data from Databases
      Section 3 : Data Cleaning & Munging
15
Remove Missing Values Preview
16
Conditional Data Selection
17
Data Grouping
18
Data Subsetting
19
Ranking & Sorting
20
Concatenate
21
Merging & Joining Data Frames
      Section 4 : Unsupervised Learning in Python
22
Unsupervised Classification- Some Basic Concepts
23
K-Means Clustering:Theory Preview
24
Implement K-Means on the Iris Data
25
Quantifying K-Means Clustering Performance
26
K-Means Clustering with Real Data
27
How To Select the Optimal Number of Clusters?
28
Gaussian Mixture Modelling (GMM)
29
Hierarchical Clustering-theory
30
Hierarchical Clustering-practical
      Section 5 : Dimension Reduction & Feature Selection for Machine Learning
31
Principal Component Analysis (PCA)-Theory Preview
32
Principal Component Analysis (PCA)-Case Study 1
33
Principal Component Analysis (PCA)-Case Study 2
34
Linear Discriminant Analysis(LDA) for Dimension Reduction
35
t-SNE Dimension Reduction
36
Feature Selection to Select the Most Relevant Predictors
37
Recursive Feature Elimination (RFE)
      Section 6 : Supervised Learning: Classification
38
Concepts Behind Supervised Learning
39
Data Preparation for Supervised Learning
40
Pointers on Evaluating the Accuracy of Classification Modelling
41
Using Logistic Regression as a Classification Model
42
kNN- Classification
43
Naive Bayes Classification
44
Linear Discriminant Analysis
45
SVM- Linear Classification
46
Non-Linear SVM Classification
47
RF-Classification
48
Gradient Boosting Machine (GBM)
49
Voting Classifier
      Section 7 : Neural Networks and Deep Learning Based Classification Techniques
50
Perceptrons for Binary Classification
51
Artificial Neural Networks (ANN) for Binary Classification
52
Multi-class Classification With MLP
53
Introduction to H20
54
Use H20 for Deep Learning Classification
55
Specify the Activation Function
56
H20 Deep Learning for Classification

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