Software Requirements:
Operating System: Ubuntu 16.04 LTS.
Python 2.7, PyCharm and Anaconda.
Hardware Requirements:
Processor: Pentium Dual Core +
RAM: Min 2GB and Internet Connection
Prerequisites: Basic coding knowledge in Python is mandatory.
Module 1
Python Programming:
– Installation and History
– Language basics
– Basic Syntax, Operators,
– Decision Making statements
– Loops control statements
– Strings, Lists
– Tuples, Dictionary
– Creating and using Functions
– Creating Modules and Packages
– Files I/O
– Command line arguments
– Exception Handling
Module 2
Installing Python and Anaconda
Data Preprocessing
– Get the dataset
– Importing the Libraries
– Importing the Dataset
– Object-oriented programming: classes & objects
– Missing Data
– Categorical Data
– Splitting the Dataset into the Training set and Test set
– Feature Scaling
Module 3
Regression
– What is Regression
– Simple Linear Regression
– Multiple Linear Regression
– Polynomial Regression
– Theoritical introduction
– Accepting and using datasets
– Practical example
– Application approach
Module 4
Logistic Regression
– Theoritical introduction
– Accepting and using datasets
– Practical example
– Application approach
Decision Tree
– Theoritical introduction
– Accepting and using datasets
– Practical example
– Application approach
Support Vector Regression
– Theoritical introduction
– Accepting and using datasets
– Practical example
– Application approach
Module 5
Random Forest Algorithm
– Theoretical introduction
– Accepting and using datasets
– Practical example
– Application approach
Classification
– Theoretical introduction
– Accepting and using datasets
– Practical example
– Application approach
K- Nearest Neighbor
– Theoretical introduction
– Accepting and using datasets
– Practical example
– Application approach
Support Vector Machine
– Theoretical introduction
– Accepting and using datasets
– Practical example
– Application approach
Module 6
Naive Bayes
– Theoretical introduction
– Accepting and using datasets
– Practical example
– Application approach
K-means Clustering
– Theoretical introduction
– Accepting and using datasets
– Practical example
– Application approach
Associative Rule Mining
– Apriori-Algorithm [Market Basket Analysis]
– Theoretical introduction
– Accepting and using datasets
– Practical example
– Application approach
Case Studies and Practical Based Projects