Python Training in Chandigarh | Mohali by experts

Best Python Training in Chandigarh and mohali is provided by 20 years experts at Oops Info solutions. We provides the best python training with machine learning in Chandigarh. Python is a key dialect that organizations are embracing as a stage of decision for different applications. There are a lot of organizations that are searching for Python prepared assets. In the event that you are hoping to better vocation openings, at that point getting yourself prepared in one the best Python preparing foundations in Chandigarh.

 

Python Training with Data Science

Oops Info solutions also provide python training with Data Science in Chandigarh – Mohali. Data science joins math and statistics, artificial intelligence (AI), machine learning specialized programming, advanced analytics with specific subject matter expertise to uncover actionable insights hidden in an organization’s data. These insights can be used to guide decision making and strategic planning and prediction.

Python Training with Machine Learning

Python Training with machine learning technology enables computers to learn automatically from past data. Machine learning uses various algorithms for building mathematical models and making predictions using historical data or information. Currently, it is being used for various tasks such as email filtering,image recognition, speech recognition, recommender system, and many more.

Python Training with web Development

Oops Info solutions also provide best python training with Django Framework in Chandigarh – Mohali. We also boost logic in python. Python is a perfect dialect to learn for novices as it is straightforward and simple to learn. Python is an open source widely used general purpose high level programming language that has been used for over a two decades. Python has straightforward programming construct, moderately less number of lines of code, simple comprehensibility and effective highlights settle on if a dialect of decision for many applications. Oops Info Solution also provide guaranteed placement in python in chandigarh and mohali.

Complete Syllabus

Introduction To Python

• Why Python
• Application areas of python
• Python implementations
  • Cpython
  • Jython
  • Ironpython
  • Pypy
• Python versions
• Installing python
• 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 command window
  • Idle editor and idle shell
• Understanding print() function
• How to compile python program explicitly

Python Language Fundamentals

• Character set
• Keywords
• Comments
• Variables
• Literals
• Operators
• Reading input from console
• 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 continue keywords

Standard Data Types

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

String Handling

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

Python List

• Creating and accessing lists
• Indexing and slicing lists
• 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 from dictionary
• Updating dictionary
• Iterating dictionary Dictionary comprehension

Python Functions

• Defining a function
• Calling a function
• Types of functions
• Function arguments
  • Positional arguments, keyword arguments
  • Default arguments, non-default arguments
  • Arbitrary arguments, keyword arbitrary arguments
• Function return statement
• Nested function
• Function as argument
• Function as return statement
• Decorator function
• Closure
• Map(), filter(), reduce(), any() functions
• Anonymous or lambda function

Modules & Packages

• Why modules
• Script v/s module
• Importing module
• Standard v/s third party modules
• Why packages
• Understanding pip utility

File I/O

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

Object Oriented Programming

• Procedural v/s object oriented programming
• OOP principles
• Defining a class & object creation
• Object attributes
• Inheritance
• Encapsulation
• Polymorphism

Exception Handling

• Difference between syntax errors and exceptions
• Keywords used in exception handling
  • try, except, finally, raise, assert
• Types of except blocks

Regular Expressions(Regex)

• Need of regular expressions
• Re module
• Functions /methods related to regex
• Meta characters & special sequences

GUI Programming + SQLITE + MYSQL

• Introduction to tkinter programming
• Tkinter widgets
  • Tk, label, Entry, Textbox, Button
  • Frame, messagebox, filedialog etc
• Layout managers
• Event handling
• Displaying image

PROJECT WITH Tkinter + SQLITE / MYSQL

Multi-Threading Programming

• Multi-processing v/s Multi-threading
• Need of threads
• Creating child threads
• Functions /methods related to threads
• Thread synchronization and locking

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 skewed distribution
• Random distribution
• Cenltral limit theorem

Hypothesis Testing

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

Numpy Package

• Difference between list and numpy array
• Vector and matrix operations
• Array indexing and slicing

Introduction to panda

• Labeled and structured data
• 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

Categorical Data Encoding

• Label Encoding
• One Hot Encoding
• Handling Date and Time

Data Visualization using matplotlib and seaborn packages

• Scatter plot, lineplot, bar plot
• Histogram, pie chart,
• Jointplot, pairplot, heatmap
• Outlier detection using boxplot

Machine Learning:

Introduction To Machine Learning

• Traditional v/s Machine Learning Programming
• Real life examples based on ML
• Steps of ML Programming
• Data Preprocessing revised
• Terminology related to ML

Supervised Learning

• Classification
• Regression

Unsupervised Learning

• clustering

KNN Classification

• Math behind KNN
• KNN implementation
• Understanding hyper parameters

Performance metrics

• Math behind KNN
• KNN implementation
• Understanding hyper parameters

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 squared error
  • Least square error
• Regularization

Logistic Regression for classification

• Theory of logistic regression
• Binary and multiclass classification
• Implementing titanic dataset
• Implementing iris dataset
• Sigmoid and softmax functions

Support Vector Machines

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

Decision Tree Classification

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

Ensemble Learning

• Random forest
• Bagging and boosting
• Voting classifier

Model Selection Techniques

• Cross validation
• Grid and random search for hyper parameter tuning

Recommendation System

• Content based technique
• Collaborative filtering technique
• Evaluating similarity based on correlation
• 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 component analysis(pca)

Open CV

• Reading images
• Understanding gray scale image
• Resizing image
• Understanding haar classifiers
• Face, eyes classification
• How to use webcam in open cv
• Building image data set
• Capturing video
• Face classification in video
• Creating model for gender prediction

 

Tableau

Tableau – Home

• Tableau – overview
• Tableau – environment setup
• Tableau – get started
• Tableau – navigation
• Tableau – design flow
• Tableau – file types
• Tableau – data types
• Tableau – show me
• Tableau – data terminology

Tableau – Data Sources

• Tableau – custom data view
• Tableau – data sources
• Tableau – extracting data
• Tableau – fields operations
• Tableau – editing metadata
• Tableau – data joining
• Tableau – data blending

Tableau – Work Sheet

• Tableau – add worksheets
• Tableau – rename worksheet
• Tableau – save & delete worksheet
• Tableau – reorder worksheet
• Tableau – paged workbook

Tableau – Calculation

• Tableau – operators
• Tableau – functions
• Tableau – numeric calculations
• Tableau – string calculations
• Tableau – date calculations
• Tableau – table calculations
• Tableau – lod expressions

Tableau – Sorting & Filter

• Tableau – basic sorting
• Tableau – basic filters
• Tableau – quick filters
• Tableau – context filters
• Tableau – condition filters
• Tableau – top filters
• Tableau – filter operations

Tableau – Charts

• Tableau – bar chart
• Tableau – line chart
• Tableau – pie chart
• Tableau – crosstab
• Tableau – scatter plot
• Tableau – bubble chart
• Tableau – bullet graph
• Tableau – box plot
• Tableau – tree map
• Tableau – bump chart
• Tableau – gantt chart
• Tableau – histogram
• Tableau – motion charts
• Tableau – waterfall charts
• Tableau – dashboard

DATA SCIENCE

• Pandas
• Numpy
• Matplotlib
• Scikit learn
• Keras
• Seaborn
• appache Spark
• Tableu
• PowerBI
• OpenCV
• Nltk
• Neural Network
• Regressions
• Tensorflow
• Pytorch
• Mongodb
• Statistics

 

PYTHON WEB DEVELOPMENT

 

Bootstrap (Powerful Mobile Front-End Framework)

What is Responsive Web Designing? Typography Features

Bootstrap Tables, Buttons, Dropdowns, Navbars Bootstrap Images

Bootstrap Responsive utilities Bootstrap Glyph icons

Bootstrap Grid System

What is a Grid?

What is Bootstrap Grid System? MOBILE FIRST STRATEGY

Working of Bootstrap Grid System Media Queries

Grid Options

Responsive column resets Offset columns Nested columns

 

Django Web Framework

What is a Framework Introduction to Django Django

– Design Philosophies History of Django

Why django and Features Environment setup

Web Server

MVC Pattern

MVC Architecture vs MVT Architecture Django MVC – MVT Pattern

Getting Started with Django

Creating the first Project

Integrating the Project to sublime text The Project Structure

Running the server

Solving the issues and Migrations Database Setup

Setting Up Your Project

Create an Application

What Django Follows Structure of django framework Model Layer

What are models Model fields Querysets

Django – Admin Interface

Starting the Admin Interface Migrations

Django – Admin Interface

Starting the Admin Interface Migrations

Views Layer

Simple View

Basic view(displaying hello world) Functional views, class based views

Django – URL Mapping

Organizing Your URLs Role of urls in djnago Working urls

Forms

 

Sending Parameters to Views Templates layer

The Render Function

Django Template Language (DTL)

Role of template layer in django

Filters,Tags, Tag if, Tag for, Block and Extend Tags Comment Tag, Usage of templates

Extending base template

Django – Models

Creating a Model Manipulating Data (CRUD) Linking Models

Django – Page Redirection

Django – Sending E-mails

Sending a Simple E-mail

Sending Multiple Mails with send_mass_mail Sending HTML E-mail

Sending HTML E-mail with Attachments

Django – Form Processing

Using Form in a View Usage of forms

Crud operations using forms Crispy forms in django

Django – File Uploading

Uploading an Image Django

– Apache Setup

Django – Cookies Handling

Django – Sessions Django – Comments

Django Admin

Creating Super User Using admin in Django Adding models to admin

Adding model objects using admin Displaying in cmd using querysets Admin interface Customization

DjangoORM(Object Relational Mapping) DjangoAPI(Application Program Interface)

 

Creating a serializer. Working with API views. Filtering back ends.

Enabling pagination. Executing CRUD operations. Managing serializer fields.

Testing API views.

 

Static files

Loading css files into templates Loading js files into templates Uploading image using models User authentication

Sample Projects and Websites

BLOGs Forums Ecommerce Web Site

Blood Bank etc

 

Why Choose oops for Python Training in Chandigarh region

• We have tie up with more than 50 companies who directly hire our students.

• We provide on training placement.

• Python is current requirement of the companies in chandigarh, mohali etc.

• Oops provide training since 2005 in all advanced technologies.

• We provide Free Demo Classes for one week.

 

OTHER JOB ORIENTED COURSES 

Mern Stack

Software Testing [/vc_column_text][/vc_column][/vc_row]

Software Testing [/vc_column_text][/vc_column][/vc_row]

Digital Marketing [/vc_column_text][/vc_column][/vc_row]