Course Name: – Learn Data Science – Do Programming using Python
Date & Time: – Sat 04th Apr to Sat 30th May 2020 every Saturday from 08:00 AM to 11:00 AM
Booking between 24 Feb 2020 to 14th March 2020 – 1000 INR discount, you pay 19000 INR
Booking between 15th March to 28th March 2020 – 500 INR discount, you pay 19500 INR
Booking between 29th March to 03rd April 2020 – 0 INR discount, you pay 20000 INR
How to Join?
- No PPT’s completely Hands-on Data Science – Mathematics and Python training.
- Installation required in your laptop for training
Why Data Science? and choose as career?
Executional Syllabus: –
|Python Quick Start and Basics||Installing Python framework and Pycharm IDE.
Creating and Running your first Python project.
Python is case-sensitive
Variables, data types, inferrence & type()
Python is a dynamic language
Comments in Python
Creating function, whitespaces & indentation
Importance of new line
List in python, Index, Range & Negative Indexing
For loops and IF conditions
PEP, PEP 8, Python enhancement proposal
ELSE and ELSE IF
Array vs Python
Reading text files in Python
Casting and Loss of Data
Referencing external libararies
Applying linear regression using sklearn
Creating classes and objects.
Zip and UnZip
Numpy and Pandas
|Array vs Pandas||Referencing Numpy and Pandas
Creating a Numpy array
Numpy Array vs Normal Python array
Why do we need Pandas?
Revising Arrays vs Numpy Array vs Pandas
|Machine Learning Fundamentals – BOW, Vectors and Labels
||What is Machine learning?
Algorithm and Training data.
Models in Machine Learning.
Features and Labels.
Bag of words.
Implementing BOW using SKLearn.
The fit Method.
The transform Method.
|Understanding TD and IDF
||Corupus / Documents, Document and Terms.
Performing calculations of TF IDF.
Implementing TF IDF using SkLearn
IDF calculation in SkLearn.
|Regression||Linear Regression Models
Non Linear Regression Models
||Classification Decision Tree
Support Vector Machinesa
||K-means Clustering and Case Study
DBSCAN Clustering and Case study
Visualization on Associated Rules