DATA SCIENCE & ML USING PYTHON TRAINING IN DARBHANGA,BIHAR

4.5 out of 5 based on 1699 Votes.

Are you Looking for the Best Institute for Data Science ML using Python training in Darbhanga,Bihar? HINDUSTAN STUDY offers Data Science ML using Python training classes with live project by expert trainer in Darbhanga,Bihar. Our Data science machine learning with Python training program in Darbhanga,Bihar is specially designed for Under-Graduates (UG), Graduates, working professional and also for Freelancers. We provide end to end learning on Machine learning with Python Domain with deeper dives for creating a winning career for every profile.

This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science! Python is a general-purpose interpreted, interactive, object-oriented and high-level programming language. Currently Python is the most popular Language in IT. Python adopted as a language of choice for almost all the domain in IT including Web Development, Cloud Computing (AWS, OpenStack, VMware, Google Cloud, etc.. ), Infrastructure Automations , Software Testing, Mobile Testing, Big Data and Hadoop, Data Science, etc. This course to set you on a journey in python by playing with data, creating your own application, and also testing the same.

• .Introduction To Python

  • Why Python
  • Application areas ofpython
  • Python implementations
  • Cpython
  • Jython
  • Ironpython
  • Pypy
  • Pythonversions
  • Installingpython
  • Python interpreter architecture
  • Python byte code compiler
  • Python virtual machine(pvm)

  Writing and Executing First Python Program

  • Using interactive mode
  • Using script mode
  • General text editor and commandwindow
  • Idle editor and idleshell
  • Understanding print() function
  • How to compile python programexplicitly

  Python Language Fundamentals

  • Character set
  • Keywords
  • Comments
  • Variables
  • Literals
  • Operators
  • Reading input fromconsole
  • Parsing string to int, float

  Python Conditional Statements

  • If statement
  • If else statement
  • If elif statement
  • If elif else statement
  • Nested if statement

  Looping Statements

  • While loop
  • For loop
  • Nested loops
  • Pass, break and continuekeywords

  Standard Data Types

  • Int, float, complex, bool,nonetype
  • Str, list, tuple,range
  • Dict, set, frozenset

  String Handling

  • What is string
  • String representations
  • Unicode string
  • String functions, methods
  • String indexing andslicing
  • String formatting

  Python List

  • Creating and accessinglists
  • Indexing and slicinglists
  • List methods
  • Nested lists
  • List comprehension

  Python Tuple

  • Creating tuple
  • Accessing tuple
  • Immutability of tuple

  Python Set

  • How to create a set
  • Iteration over sets
  • Python set methods
  • Python frozenset

  Python Dictionary

  • Creating a dictionary
  • Dictionary methods
  • Accessing values fromdictionary
  • Updating dictionary
  • Iterating dictionary
  • Dictionary comprehension

  Python Functions

  • Defining a function
  • Calling a function
  • Types offunctions
  • Function arguments
  • Positional arguments, keywordarguments
  • Default arguments, non-defaultarguments
  • Arbitrary arguments, keyword arbitraryarguments
  • Function return statement
  • Nested function
  • Function as argument
  • Function as return statement
  • Decorator function
  • Closure
  • Map(), filter(), reduce(), any()functions
  • Anonymous or lambdafunction

  Modules & Packages

  • Why modules
  • Script v/smodule
  • Importingmodule
  • Standard v/s third partymodules
  • Why packages
  • Understanding pip utility

  File I/O

  • Introduction to filehandling
  • File modes
  • Functions and methods related to filehandling
  • Understanding with block

  Object Oriented Programming

  • Procedural v/s object orientedprogramming
  • OOP principles
  • Defining a class &objectcreation
  • Object attributes
  • Inheritance
  • Encapsulation
  • Polymorphism

  Exception Handling

  • Difference between syntax errors andexceptions
  • Keywords used in exceptionhandling
  • try, except, finally, raise,assert
  • Types of exceptblocks

  Regular Expressions(Regex)

  • Need of regularexpressions
  • Re module
  • Functions /methods related toregex
  • Meta characters &specialsequences

  GUI Programming

  • Introduction to tkinterprogramming
  • Tkinter widgets
  • Tk, label, Entry, Textbox,Button
  • Frame, messagebox, filedialogetc
  • Layout managers
  • Event handling
  • Displaying image

  Multi-Threading Programming

  • Multi-processing v/s Multi-threading
  • Need of threads
  • Creating child threads
  • Functions /methods related tothreads
  • Thread synchronization andlocking

  Introduction to Database

  • Database Concepts
  • What is DatabasePackage?
  • Understanding DataStorage
  • Relational Database (RDBMS)Concept

  SQL (Structured Query Language)

  • SQLbasics
  • DML, DDL & DQL
  • DDL: create, alter, drop
  • SQLconstraints:
  • Not null, unique,
  • Primary & foreign key, compositekey
  • Check, default
  • DML: insert, update, delete andmerge
  • DQL : select
  • Select distinct
  • SQLwhere
  • SQLoperators
  • SQLlike
  • SQL orderby
  • SQLaliases
  • SQLviews
  • SQLjoins
  • Inner join
  • Left (outer) join
  • Right (outer) join
  • Full (outer) join
  • Mysql functions
  • Stringfunctions
  • Char_length
  • Concat
  • Lower
  • Reverse
  • Upper
  • Numericfunctions
  • Max, min, sum
  • Avg, count,abs
  • Date functions
  • Curdate
  • Curtime
  • Now

  Statistics, Probability &Analytics:

  Introduction to Statistics

  • Sample or population
  • Measures of central tendency
  • Arithmetic mean
  • Harmonic mean
  • Geometric mean
  • Mode
  • Quartile
  • First quartile
  • Second quartile(median)
  • Third quartile
  • Standard deviation

  Probability Distributions

  • Introduction to probability
  • Conditional probability
  • Normal distribution
  • Uniform distribution
  • Exponential distribution
  • Right & left skeweddistribution
  • Random distribution
  • Centrallimittheorem

  HypothesisTesting

  • Normality test
  • Mean test
  • T-test
  • Z-test
  • ANOVA test
  • Chi square test
  • Correlation and covariance

  Numpy Package

  • Difference between list and numpyarray
  • Vector and matrixoperations
  • Array indexing andslicing

  Panda Package

  Introduction to pandas

  • Labeled and structureddata
  • Series and dataframe objects

  How to load datasets

  • From excel
  • From csv
  • From html table

  Accessing data from Data Frame

  • at &iat
  • loc&iloc
  • head() & tail()

  Exploratory Data Analysis (EDA)

  • describe()
  • groupby()
  • crosstab()
  • boolean slicing /query()

  Data Manipulation & Cleaning

  • Map(), apply()
  • Combining data frames
  • Adding/removing rows &columns
  • Sorting data
  • Handling missing values
  • Handling duplicacy
  • Handling data error

  Handling Date and Time

  Data Visualization using matplotlib and seaborn packages

  • Scatter plot, lineplot, barplot
  • Histogram, pie chart,
  • Jointplot, pairplot, heatmap
  • Outlier detection usingboxplot

  Machine Learning:

  Introduction To Machine Learning

  • Traditional v/s Machine LearningProgramming
  • Real life examples based onML
  • Steps of MLProgramming
  • Data Preprocessing revised
  • Terminology related toML

  Supervised Learning

  • Classification
  • Regression

  Unsupervised Learning

  • Clustering

  KNN Classification

  • Math behind KNN
  • KNN implementation
  • Understanding hyperparameters

  Performance metrics

  • Math behind KNN
  • KNN implementation
  • Understanding hyperparameters

  Regression

  • Math behind regression
  • Simple linear regression
  • Multiple linear regression
  • Polynomial regression
  • Boston price prediction
  • Cost or loss functions
  • Mean absolute error
  • Mean squared error
  • Root mean squarederror
  • Least square error
  • Regularization

  Logistic Regression for classification

  • Theory of logistic regression
  • Binary and multiclassclassification
  • Implementing titanic dataset
  • Implementing iris dataset
  • Sigmoid and softmaxfunctions

  Support Vector Machines

  • Theory of SVM
  • SVM Implementation
  • kernel, gamma, alpha

  Decision Tree Classification

  • Theory of decision tree
  • Node splitting
  • Implementation with iris dataset
  • Visualizingtree

  Ensemble Learning

  • Random forest
  • Bagging and boosting
  • Voting classifier

  Model Selection Techniques

  • Cross validation
  • Grid and random search for hyper parametertuning

  Recommendation System

  • Content based technique
  • Collaborative filteringtechnique
  • Evaluating similarity based oncorrelation
  • Classification-based recommendations

  Clustering

  • K-means clustering
  • Hierarchical clustering
  • Elbow technique
  • Silhouette coefficient
  • Dendogram

  Text Analysis

  • Install nltk
  • Tokenize words
  • Tokenizing sentences
  • Stop words customization
  • Stemming and lemmatization
  • Feature extraction
  • Sentiment analysis
  • Count vectorizer
  • Tfidfvectorizer
  • Naive bayes algorithms

  Dimensionality Reduction

  • Principal componentanalysis(pca)

  Open CV

  • Reading images
  • Understanding gray scaleimage
  • Resizing image
  • Understanding haar classifiers
  • Face, eyes classification
  • How to use webcam in opencv
  • Building image dataset
  • Capturing video
  • Face classification invideo
  • Creating model for genderprediction

  Tableau

  Tableau – Home

  • Tableau -overview
  • Tableau – environmentsetup
  • Tableau – getstarted
  • Tableau -navigation
  • Tableau – designflow
  • Tableau – filetypes
  • Tableau – datatypes
  • Tableau – showme
  • Tableau – dataterminology

  Tableau – Data Sources

  • Tableau – custom dataview
  • Tableau – datasources
  • Tableau – extractingdata
  • Tableau – fieldsoperations
  • Tableau – editingmetadata
  • Tableau – datajoining
  • Tableau – datablending

  Tableau – Work Sheet

  • Tableau – addworksheets
  • Tableau – renameworksheet
  • Tableau – save &deleteworksheet
  • Tableau – reorderworksheet
  • Tableau – pagedworkbook

  Tableau – Calculation

  • Tableau -operators
  • Tableau -functions
  • Tableau – numericcalculations
  • Tableau – stringcalculations
  • Tableau – datecalculations
  • Tableau – tablecalculations
  • Tableau – lodexpressions

  Tableau – Sorting & Filter

  • Tableau – basicsorting
  • Tableau – basicfilters
  • Tableau – quickfilters
  • Tableau – contextfilters
  • Tableau – conditionfilters
  • Tableau – topfilters
  • Tableau – filteroperations

  Tableau – Charts

  • Tableau – barchart
  • Tableau – linechart
  • Tableau – piechart
  • Tableau -crosstab
  • Tableau – scatterplot
  • Tableau – bubblechart
  • Tableau – bulletgraph
  • Tableau – boxplot
  • Tableau – treemap
  • Tableau – bumpchart
  • Tableau – ganttchart
  • Tableau -histogram
  • Tableau – motioncharts
  • Tableau – waterfallcharts
  • Tableau –dashboard

  Projects

  • One project using python &sql
  • One project using python &ml
  • One dashboard usingtableau