ADVANCED COURSE IN MACHINE LEARNING USING PYTHON

30,000.00

 

Next course starts: 11/08/2018 Intended audience: College students and working professionals.

Objectives:

• introduce advanced machine learning algorithms
• code advanced and cutting-edge machine learning algorithms using Python
• solve practical case studies

Learning Outcomes:

  •  Describe a flow process for data science problems (Remembering)
  • Classify data science problems into standard typology (Comprehension)
  • Develop Python codes for data science solutions (Application)
  • Correlate results to the solution approach followed (Analysis)
  • Assess the solution approach (Evaluation)




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Description

 

+ Additional Information: Each module will be followed by an assignment.

 

Week  Module description Duration
1

Welcome notes and introduction to Data science
• Why Python for Data science?
• Opening, creating and managing Python IDE
• Basics of programming

Variables
Operators
Data types
Data structures – Lists, Tuples Dictionary
Control structures in Python
Function files in Python

• Essential Python Library for data science with hands-on

Numpy (Numeric Python)
Spicy
Pandas
Matplotlib

8 H
  Assignment 1 based on Python programming 
2 Theory : (2 hours)
• Introduction to Machine learning
• Correlation analysis and dimensionality reduction technique

Principal component analysis (PCA)
Linear Discriminant analysis (LDA)

Practical’s: ( 6 hours)
• Exploratory Data analysis
• Correlation
• Feature Extraction8H

8H
  Assignment 2 based on Correlation , PCA & LDA
3

Theory: (4 hours)
• Univariate and Multi variate linear regression
• Model assessment
• Verifying assumptions used in Linear regression
• Assessing importance of different variables, subset selection
• Regularization
• Tree Based Model (RF)

Practical: ( 4 hours)
• Advanced Regression Modeling using Scikit-learn (Case Study)

Linear Regression
Ensemble Modeling
Random forest
Hyperparameters tuning

8H
  Assignment 3 based on Regression Modelling
4

Theory: (2 hours)
• Introduction to Classification
• Classification using Logistic Regression
• Classification using k-Nearest Neighbors (kNN)

Practical: (6 hours)
• Classification Modeling in Python

Logistic Regression
K-Nearest Neighbors

8H
  Assignment 4 based on Classification techniques
5

Theory: ( 2 hours)
Classification using clustering techniques

K-Mean clustering
Hierarchical Clustering

Practical: ( 6 hours)
Classification using clustering techniques in Python

K-Mean clustering
Hierarchical Clustering

8H
  Assignment 5 based on clustering techniques
6 Final exam – case study
-H





 

 

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