Learn Data Science – Do Programming using Python

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

Cost: – 

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?

Click Here

Key Features

      • No PPT’s completely Hands-on Data Science – Mathematics and Python training.
      • Installation required in your laptop for training
        • Python download link, get from here
        • IPython download link, get from here
        • For MAC system download link of Python, NumPy, SciPy & matplotlib get from here
        • All at only 20000 INR

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
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.
Understanding TF
Understanding IDF
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
Logistic Regression
Support Vector Machinesa
K-means Clustering and Case Study
DBSCAN Clustering and Case study
Hierarchical Clustering
Apriori Algorithm
Candidate Generation
Visualization on Associated Rules