Python programming for Data Science - Advance

Overview

Advanced Python Programming training course will give you a detailed overview of advance python programming. It helps to know Random Forest, Decision Tree.

Availability: In stock

Regular Price: ₹25,999.00

Special Price ₹24,999.00

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Regular Price: ₹25,999.00

Special Price ₹24,999.00

Upcoming Classes

8th Feb 2020
12 Days
Weekend Online (WebEx) & Offline (Bangalore) Classes - Saturday & Sunday
Time : 11 AM to 6 PM
Price : 24999
21st mar 2020
12 Days
Weekend Online (WebEx) & Offline (Bangalore) Classes - Saturday & Sunday
Time : 11 AM to 6 PM
Price : 24999

Course Description

  • We will start with set of objective questions which will give us idea of participants and also it will encourage participants to learn.
  • Every fundamental of learning is backed by objective questions and hands on excercise.
  • We will last our course with set of multiple choice questions which demonstrate the improvements in participants
  • This is  24 days or 120 hours course.

Course Instructor : Raju Kumar Mishraview more

Objective

Python Basic :

  • Introduction to Python
  • Running Python from eclipse
  • IDLE
  • Data type in Python
      • Integer
      • Long
      • Float
      • Complex
      • None
  • Typecasting data
  • Operators in python

Collections  in  python:

  • List

◦     Introduction to List

◦     List operations

◦     List Comprehensions

  • Dictionary

◦     Introduction to Dictionary

◦     Dictionaries operations

  • Set

◦     Introduction to Set

◦     Set Operations

  • Tuples

◦     Introduction to Tuples

◦     Tuples Operations

Conditionals and Looping

  • Introduction to conditionals
  • if
  • if and else
  • if elif and else
  • Introduction to looping
  • for loop
  • while loop
  • break
  • continue
  • pass

Strings:

  • Introduction to Strings
  • String Operations

 

Functions modules and Package:

  • Inbuilt Functions
  • User Defined Functions and its definition
  • return statement
  • Local and global variables
  • Default argument
  • Variable length arguments
  • Anonymous functions
  • Modules in Python
  • Packages in python
  • Writing packages
  • importing packages

 

Exception handling in python :

  • Introduction to exception
  • Raising Exception
  • try and except
  • Different type of exception
  • Multiple except
  •  Role of finally
  • try, except and finally together
  • assert

IO in python

  • Reading from and writing to console.
  • Reading from a file
  • Difference between read() and readLine() function.
  • Writing to a file
  • Reading and Writing csv (Comma Separated Files)
  • Reading and Writing JSON files.
  • Reading pdf files
  • Reading and writing excel files.

 

Some useful packages in Python

  • os

◦     Joining path

◦     Creating new directory

◦     Absolute and relative path

◦     File size

◦     Getting content of a folder

  • shutil

◦     Copying files and directories

◦     Deleting files and directories

◦     Renaming files and directories

Class and Objects :

  • Class in Python
  • Constructors
  • Objects
  • Inheritance
  • Namespace

Debugging in Python

NumPy :

  • Introduction to NumPy.
  • NumPy data type
  • NumPy array and its operations
  • NumPy ndarray
  • Indexing slicing and Stacking of array and ndarray
  • Manipulating array shapes
  • Splitting arrays
  • Matrices and its operation in NumPy
  • Linear systems and NumPy
  • File IO in NumPy

Matplotlib :

  • Introduction to Matplotlib.
  • Line plot multiple line plot
  • Scatter plot
  • Bar plot
  • Histograms
  • Box plot
  • Error bars 
  • Contour plot
  • Piplot
  • Different aspects of coloring
  • Violin plots
  • Text plot
  • Word Cloud

NLTK :

  • Introduction to natural language processing.
  • Introduction to NLTK
  • Installing NLTK
  • Text analysis basics

◦     Tokenization

◦     Stemming

◦     Stop words

◦     Part of speech tagging

◦     Lemmatization

  • NLTK Corpora
  • Clustering

 

Pandas :

  • Introduction to Pandas.
  • Pandas data structure
  • Operations on Data structures
  • IO in Pandas
  • Data summarization and aggregation

Scikit-Learn

Introduction :

  • Introduction to Data Mining and Machine learning
  • Supervised and unsupervised learning.
  • Some use cases on machine learning.
  • Introduction to Scikit-Learn
  • Installing Scikit-Learn
  • What is data?
  • Steps in data analysis.
  • Types of data.
  • Data preprocessing and its importance.

Exploratory analysis of data :

  • Importance of data visualization
  • Initial questions in model building.
  • Model selection

Linear regression

  • Introduction to simple linear regression.
  • Assumption to simple linear regression.
  • Parameter estimation
  • Simple linear regression with Scikit-Learn
  • Multiple linear regression with Scikit-Learn
  • Model verification and linear regression assumption testing.
  • Data transformation and polynomial regression
  • Introduction to Ridge regression
  • Ridge regression and Scikit Learn
  • Introduction to Lasso Regression
  • Lasso Regression and Scikit Learn

 

 

Classification

  • Introduction to classification
  • Classification use cases
  • K Nearest neighbor

◦     Introduction to K Nearest neighbor

◦     K Nearest neighbor in Scikit Learn

◦     Strength and weakness of K Nearest neighbor

  • Logistic Regression

◦     Introduction to logistic regression

◦     Use cases of logistic regression

◦     Mathematical description of logistic regression

◦     Logistic regression with Scikit Learn

  • Naive Bayes Classifier

◦     Introduction to Bayes theorem

◦     Bayes theorem in classification

◦     Bayes classifier with Scikit Learn

  • Decision Tree

◦     Introduction to decision tree.

◦     Use cases of decision tree

◦     Partition algorithms for decision tree

▪     ID3

▪     Gini Index

▪     Cart

◦     Tree pruning

◦     Scikit Learn and decision tree

  • Ensemble methods
  • Random forest with Scikit Learn
  • Neural Networks

◦     Introduction to brain

◦     Introduction to neural network

◦     Perceptron

◦     Back propagation algorithm

◦     MLP and Scikit Learn

  • Classifier performance evaluation

◦     Confusion matrix

◦     Cohen kappa

◦     Precision, recall and F-measures

◦     Receiver operating characteristic (ROC)

Clustering

  • Introduction to clustering
  • Use cases of clustering
  • K-means clustering
  • Hierarchical clustering

◦     Different linkage type: Ward, complete and average linkage

  • DBSCAN
  • Clustering performance evaluation

◦     Adjusted Rand index

◦     Mutual Information based scores

◦     Homogeneity, completeness and V-measure

◦     Fowlkes-Mallows scores

◦     Silhouette Coefficient

 

 

 

   NLTK :

  • Introduction to natural language processing.
  • Introduction to NLTK
  • Installing NLTK
  • Text analysis basics

◦     Tokenization

◦     Stemming

◦     Stop words

◦     Part of speech tagging

◦     Lemmatization

  • NLTK Corpora
  • Clustering

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