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MACHINE LEARNING USING PYTHON

PROCODE TECH

Data Analytics/Data Science/Machine Learning

25000

4y ago

Course Duration: 2 Months

Course Details

Machine Learning with Python is a 60 hour long, Online Course. Our training is going to help you crack Job interviews by making you understanding the logic of all the algorithms, with loads of real time examples, hackathons and surprise tests. Join the best Online Training Institute in India and get trained on Machine Learning using Python.
Syllabus:Module 1:

Why should you learn to write programs?
Introduction to Python. History of Python
Variables.
Numbers
Number Methods
Expressions and statements.
Conditional execution.
Built in Functions.
User Defined Functions.
Multiple Argument.
Multiple Keyword Argument.
Print statements.
Print formatting.

Module 2:

For loop.
While Loop.
For-else.
While-else.
String handling.
String manipulate.
String Methods.
Indexing.
Slicing.
Range & Xrange.
File handling – Open, Write, Read, Insert, Delete.
File handling csv, excel, text.

Module 3:

Lists.
List Comprehension.
List Methods.
Dictionaries.
Dictionary Comprehension.
Dictionary Methods.
Tuples.
Tuple Methods.
Regular Expressions:
- Findall
- Search
- Match
- Meta characters
- Special Sequences
- Sets.

Module 4:

Object Oriented Programming Basic Understanding using real time scenarios.
- Class
- Instance
- Object
- Functions
- Methods
- Inheritance
- Abstraction
- Encapsulation
- Polymorphism
- Simple Examples with Tests
- Complex examples with Test.

Module 5:

Basics of Networking using Python.
- Socket Module
  - Server Socket Methods
  - Client Socket Methods
  - General Socket Methods
- Overview of Python Internet Modules.

Module 6:

Importing Data sets.

Working with Data sets.

Fixing missing data issue.

Fixing categorical data issue.

Feature Scaling.

Train Test Split.

Module 7:

Simple Linear Regression Technique.

Multiple Linear Regression Technique.

Polynomial Regression Technique.

Tree Regression Technique.

Random Forest Technique.

Support Vector Regression Technique.

K-Nearest Neighbor Classification Technique.

Logistic Regression Technique.

Kernel Support Vector Machine.

Tree Classification Technique.

Random Forest Classification Technique.

Naive Bayes.

Module 8:

K-Means Clustering.

Hierarchical Clustering.

Module 9:

Apriori Rule.

Module 10:

Natural Language Processing with Project.

Module 11:

Linear Discriminant Analysis.

Kernel Principle Component Analysis.

Module 12:

Model Selection using K-Fold Cross Validation.

Model Selection using Grid Search.

Visit: http://procodetech.com/course/machine-learning-using-python/

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