(500) Ratings
Software Development Life Cycle & Application Life Cycle Management
SDLC Methodologies
Waterfall Methodology
Agile Methodology
Scrum Framework
What is DevOps?
What is Cloud, SAAS, IAAS, PAAS?
What is Testing?
Web Application Architecture
Web Technologies
Web Technologies used in Projects
The Manifesto for Agile Software Development
The Principles behind the Agile Manifesto
Agile Approaches
Agile Frameworks
document Structure (DOCTYPE,head,body)
Basic TAgs(heading,paragraph,links,etc.)
Attributes
Favicons
HTML Comments
External,internal and inline Styles
Properties and values
Selectors(type,class,ID,attrbute,pseudo-element,combinator)
specific and Casacade
inheritaance and inititsl/Default Values
Typography in CSS(font-family,font-size,font-weight,line-height,letter-spacing)
BOx model(content,padding,border,margin)
Units of measurements in CSS
MArgin Collapsing
Border(border-radius,border-image)
Outline
Background properties(background-color,background-image,background-size,background-repeat,background-position)
Box-shadow and text-shadow
Lists(ordered,unordered,description lists)
Tables(thead,tbody,tfoot,tr,td,th)
input Types (text,pasword,raido,checkbox,etc.)
Form Attributes(actio,method,etc.)
Form VAlidations(required,pattern,etc.)
Labels and Accessiblity in forms
(static,relative,absolute,fixed,sticky)
Display Property (block,inline,inline-block,none)
Floating Elements (float,clear)
visibility (visibile,hidden,collapse)
Overflow (visible,hidden,scroll,auto)
z-index and Stacking Context
Layouts
Animations
Advanced tags
Responsive Design
Weekly Tasks
introduction to Bootstrap
Settingup Bootstrap
Basic Grid System
Typography and utilities
Bootstrap Components
Navigation
Alerts and Badges
Buttons and button Groups
Cards
Bootstrap Components 2 and Advanced Topics
Forms
Modals,Carousels and Tooltips
Flex Utilities and Responsive Deign
Wrap-up and Best Practices
introduction to Javascript
Basics
control Structures
Conditional Statements
Arrays
String Date Methods
Template Literals
Javascript Functions and Scope
Docment Object Model-DOM
AJAX
OOP in JAvascript
Regular Expressions
Javascript**capstone:LMS
Es6 JAvascript Concepts
Arrow Functions
Let and Const
Template Literals
Destructuring
Default Parameters
Spread and Rest Operators
Object Literal Enhancement
Modules
Promises and Async/Await
Map,Set and WeakMap/WeakSet
Array Methods(map,filter,reduce etc.)
Promises and Fetch API
introduction to React
Functional Components
JSX and Basic Rendering
Conditional Rendering and Lists
Props and prop Types
State Management with React Hooks
A complete list of React Hooks
Hooks
Event Handling in Functional Components
styling Functional Components
Custom Hooks
Fetching Data with useeffect and Axios
Routing with React Router
Forms and Form Handling
State Management behind useState
React children
Advanced topics and Best Practices
Testing Functional Components
introduction to Typescript
TypeScript Compiler
Variables
Data Types
Object oriented Programming
Classes
Inheritance
polymorphism
Abstraction
Interfaces
Abstaract classes
enums
Type Inference
Type Compatibility
Advanced Types
iterators and Generators
Namespaces
Modules
Angular Basics
Intro to MVC Architecture vs other architectures
Introduction to Angular
Different versions of angular
Difference between Angularjs,Angular versions
Environment Setup
First Application using Angular
Components
Generating Components using Angular CLI
Templates
Directives
Dependency Injection
Generating Services using CLI
Working with Data and Events
Property Binding
Attribute Binding
Adding bootstrap
Style Binding
Event Binding
Template Variables
Two-way Binding
Pipes
Custom Pipes
ure and Impure Pipes
Components in detail
Input Properties
Aliasing Input Properties
Output Properties
Passing Event Data
Aliasing Output Properties
Templates
View Encapsulation
ngContent and ngContainer
Deep dive in to Directives
introduction
ngif nd else
Hidden Property
ngFor and Change Detection
the Leading Asterisk
ngClass,ngStyle
safe traversal Operator
Creating Custom Attribute Directives
Creating Custom Structural Directives
Working with Templates driven forms
Building a Bootstrap form
Types of Forms
ngModel,ngForm,ngModelGroup
Working with check boxes,drop-down lists,Radio Buttons
Realtime Template driven forms,Developing Reactive Forms
introduction
Creating Controls Programatically
Adding Validations
Specific validations erros and custom validations
working with HTTP Services
Overview of web Services
SOAP and restful web Services
Creating Services
Consuming HTTP restful services
CRUD operations
Observables and promises
Error Handling
Best Practices
Routing and Navigation
Introduction
Routing in a Nutshell
Configuring Routes
RouterOutlet
RouterLink
RouterLinkActive
Getting the Route Prarameters
Programmatic Navigation
Introduction to Java
Java language Fundamentals part1
Java language Fundamentals part2
Java language Fundamentals part3
Array
Classes and Object part1
Classes and Object part2
Exploring Basics Java class Libraries
Inheritance and polymorphism part 1
Inheritance and polymorphism part 2
Abstaract Classes and Interfaces
Access Modifiers,Non Access Modifiers & Packages
Exception Handling Part1
Exception Handling Part1
I/O
Collection [with comparabe,comparator,equals,Hash code] & Generics
Stream Api and Lambda Expression part 1
Stream Api and Lambda Expression part 2
multithreading part1
multithreading part2
Regular Expressions
Introduction to Maven
maven life cycle phases
maven Objectives
Maven project setup
Building Projects Setup
Building projects using Maven
introduction to Spring core,Containers,DI
XML configurations and Annotation configurations
Setter Injection valves as element,valve as attribute,p name space
Setter Injection list,set,map,properties,ref,d-check
Constructor Injection valves as element,valve as attribute,p & c name space
Constructor Injection list,set,map,ref,ambiguities
Inner Beans,Auto-wiring,Bean Scopes
Annotations @Autowired @Qualifier @Component
Java based configurations
Introduction to Spring MVC,Life Cycle
Model Interface ModelAndView ModelMap
@Controller @RequestMapping
@PathVariable @RequestParam @ModelAttribute @Transactional
Form Handling, Validations
Spring MVC with JDBC Integration
Spring MVC with Hibernate Integration
Introduction to Springboot
Spring Boot Test Connections using Postman API
Spring Boot using InMemory Database
Spring Boot Data JPA
Spring Boot CRUD Application MySQL Database
Spring Boot Building Swagger Document
Spring Boot Security
Spring Boot JWT Token
Annotation's like @Controller @RestController @Entity @Table @GeneratedValue @Autowired @Enumerated @PostMapping @GetMapping @DeleteMapping @PostMapping @RequestMapping @Service @Entity @Repository @PreAuthorize @CrossOrigin @Configuration @Bean @Component @Value @JsonIgnore @NotBlank @Email
Introduction to Micro services
Springcloud sleuth and zipkin Application
Springcloud gateway Application
Springcloud Eureka Discovery server Application
Springboot Application Docker
Springboot kubernetes cluster
Basic of ExpressJS
Getting started with Express
Features of Express
Installation of express
First Application using Express JS
Understanding Express JS Request
Request object
Request object properties
Request object methods
Real time example
Understanding Express JS Response
Response object
Response object properties
Response object methods
Understanding Get and Post operations
Intro to Get Request
Developing code to handle Get Request
Intro to Post Request
Developing code to handle Post Request
Routing in Express
Intro to Routing
Route methods
Route Paths
Route Params
Route Handlers
Response methods
Template Engines
Introduction to Template Engines
Different Template Engine
Intro to Pug
Using pug with express
Working with EJS
Working with handlebars
Advanced Express JS
Error Handling
File Uploading Real Time Modules
Nodemailer
Multer
Creating First Database
Creating Document and Saving it to Collection
Dropping a Database
Creating a Collection Using db.createCollection(name,options)
Dropping a Collection
CRUD Operations
Creating/Inserting a document in collection using javascript file
Inserting Array of Documents
Reading a Document - Querying
Reading a Document with $lt, $gt operator
Updating Documents
Deleting documents
Indexes and ObjectIds
Introduction to Indexes
Understanding Impact of Indexes
Creating Index
Finding Indexes
Understanding ObjectIds
Creating ObjectIds
Advantages of ObjectIds created by MongoDB
Disadvantages of ObjectIds created by MongoDB
Aggregation and Data Modelling in MongoDB
Using aggregate() method
Using distinct() and count()
Sorting documents
Introduction to Data Modelling in MongoDB
Data Modeling using References
Data Modeling using Embedded documents
Relationships in MongoDB
One-To-One Relationship
One-To-Many Relationship
Many-To-Many Relationship
Using MongoClient
Using Mongoose
Creating database using MongoClient and Mongoose
CRUD operations on MongoDB with Nodejs
Insert
Update
Delete
Retrieve
Introduction To Cloud Computing
Creating Free AWS Account
Understanding Infrastructure in AWS
Understanding Regions and Availability Zones
Installing Required software’s in Desktop
Git Bash
Putty & Puttygen
Terminal
SSH / SCP
Creating Servers Using EC2
Setting Firewalls - Security Groups
Setting up access to AWS cloud Using SSH Keys
Introduction to Continuous Integration & Delivery
Overview of Jenkins
Jenkins Setup
Plugins and its uses
Setting up your Build Jobs
Automated Builds using webhooks
Jenkins security
Code Quality Integration
Integrating Nexus
Distributed Builds / Master-Slave Configuration
Performing Continuous Delivery
Implementing Pipelines
Introduction to Containerisation
Monolithic Architecture
Microservices Architecture
Introduction to Virtualisation
Virtualisation vs Containerisation
Docker Architecture
Setting up Docker
Docker Registry
Docker Images Vs Docker Containers
Running Docker Containers
Docker Volumes
Docker Networks
Docker Logs
Docker Tags
Dockerize Applications
Introduction to High Availability
Introduction to Container Orchestration
Container Orchestration Tools
Overview of Kubernetes
Kubernetes Architecture
Components of Kubernetes
Overview of Minikube
Setup Minikube
Kubernetes Objects
Pods
Replica Sets
Deployments
Services
ClusterIP
NodePort
Load Balancer
Config Maps
Secrets
Ingress
Overview of Production Clusters
Overview of AWS EKS
Setup EKS
Deploy Applications On EKS
Our Fullstack training has precisely been developed to reach out to the demand of the learners with keeping in mind the industry standards. This DevOps course will particularly be helpful for the career advancement of the following audience
Graduates from College.
Currently working employees looking to upskill themselves.
Candidates looking for a change in the IT Field.
As such, there are no specific prerequisites for Fullstack Training in Hyderabad. If you are familiar with programming and foundation skills with a sense of curiosity and willingness to learn you are all set for the Fullstack training.
Fullstack Training Classes are conducted over the Weekdays and Weekends through classroom and online sessions. Please get in touch with the team to get exact schedule and timings.
Fullstack Course duration is 2 Months
Weekday Fullstack Training classes will be one hour long and Weekend classes will be three hours long.
Please find the detailed Fullstack course curriculum in the Fullstack training curriculum section.
Yes, we will assist our students with all the interview preparation techniques.
the best environment for upskilling and skilling your career. I've joined for DevOps training over here. The trainer was way too good! Explained everything with detailed real time examples. It has a very good infrastructure. One of the best DevOps training institutes in Hyderabad.
I had completed the DevOps Course. The DevOps training was well organized and structured. I really liked the trainer’s approach. Very supportive and relaxed. His approach has benefited me in terms of confidence. An empowering course – very encouraging with a lot of practical work. The study material, exercises and case studies provided were very good. Working on these case studies will help you a lot to with learning the topics. I would definitely recommend.
Best DevOps Training in Hyderabad with hand on Labs and great exposure to all the other Technologies .Best platform to learn DevOps course.
I took DevOps training in Hyderabad.it is the best institute if you want to make yourself better. The best part is, the classes won't be like someone standing there and giving lectures, there is a lot of interaction between the trainers and the students.
After Taking Devops Training in Hyderabad , I got placed in as a DevOps Engineer. this institute has helped me a lot to unleash my potential.
It has been a very good school of learning, trainer explains every individual concept (Theory/Practical) in-depth when I joined for DevOps training at the Gachibowli branch Hyderabad. It has an extraordinary infrastructure with un-interrupted broadband. Your doubts can be clarified with live projects. Altogether it was an awesome experience learning .
Best place to learn DevOps course in hyderabad, the trainer is well knowledged and trains in depth. Practice sessions helped me a lot and the infrastructure is superb compared to other places. I have cracked my certification exam in DevOps training course with the help of the training here.
I have finished my Devops training in hyderabad . I got the job right after the training. I'm very happy. You should definitely check out us to elevate your career prospects.
Build an XML to CSV converter using Python
This python project which will help students to brush up their basic python skills to build a real-world XML to CSV Converter.
Industry: Data Operations
Tools: Python, XML, CSV, Data Ingestion
Building a Photo Editor from scratch using Flask and Numpy
Numpy is a versatile package to do data operations on matrices, numbers and number operations. With this project we will learn doing number operations on Images and apply filters, cropping, flipping and resizing using Numpy.
Industry: Media and Photography
Tools: Python, Numpy, Image Processing, Matplotlib
Exploratory Data Analysis on Retail Shop Sales data.
Performing exploratory data analysis to find patterns in data which will determine the approach to take in Machine Learning. Exploratory data analysis will help identifying features which can be used or discarded in the Machine Learning approach.
Industry: Retail
Tools: Exploratory Data Analysis, Data Preprocessing, Scaling Techniques, Missing Value Imputations.
Fruit type prediction using K Nearest Neighbors algorithm
K Nearest Neighbor algorithm is used to predict the type of fruit given its mass, height, width and color score. We will understand on how to use KNN algorithm and how to tune the hyperparameters such as n_neighbors and k value.
Industry: Food and Beverages
Tools: Supervised Machine Learning, K Nearest Neighbor Classifier, KNN Algorithm, Pandas, Numpy, Matplotlib
Predict Malignancy in Mammographic Masses using Decision Tree Classifiers
Mammographic masses is a public dataset from UCI machine learning repository which has information on mass shape, structure, density and age of a person. With this project we will use Decision Tree classifier to detect Malignancy (either Malignant or Benign) of the Mammographic Mass
Industry: Healthcare
Tools: Decision Tree Classifier, Sklearn, Pandas, Numpy, Graphviz, Matplotlib
Predict whether a candidate will be shortlisted in H1B Visa process using Random Forest Algorithm.
Every year there are close to 1 lakh 65 thousand applications for H1B Visa processing and only few get shortlisted in the process. A lot of information is used for scrutinizing candidates in the selection process. In this project we will use Random Forest algorithm on past five years of H1B processing data to identify if a H1B application will be selected or rejected in the application process.
Industry: US H1B Visa - USCIS
Tools: Random Forest, Pandas, Sklearn, Numpy, Seaborn, Matplotlib
Predict Breast Cancer using Support Vector Machine algorithm.
Wisconsin Breast Cancer dataset has 569 sample of Breast cancer observations determining Malignancy or Benign state of breast mass. Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. In this project we will use SVM algorithm to detect malignancy in the cell.
Industry: Healthcare
Tools: SVM, Numpy, Pandas, Sklearn
HR Analytics for Attrition Prediction using Logistic Regression
Employee Attrition is an important subject to gauge the satisfaction of the employee in a company. HR departments take various measures to arrest employee attrition. In this project we will use Logistic regression to predict who is the potential employee who is in a verge of leaving the company.
Industry: Human Resources
Tools: Logistic Regression, Sklearn, Numpy, Pandas, Matplotlib
Churn Analysis in Telecommunication:
A customer can be called as a “churner” when he/she discontinue their subscription in a company and move their business to a competitor. Prediction as well as prevention of customer churn brings a huge additional revenue source for every business.
Here, we use a telecom customer data set to classify the set of possible customers who are likely to churn
Industry: Telecommunications
Tools: SVM, K Nearest Neighbors Classifier, Random Forest, Logistic Regression, Pandas, Numpy, Sklearn, Seaborn, Matplotlib
Predicting Housing prices using Regression
Description: Predict the sales price for each house based on input features provided for the house.
Industry: Real Estate
Tools: Linear Regression, Multi Linear Regression, Sklearn, Pandas, Numpy, Matplotlib
Social Network Ads based Prediction
Description: Predicting if a user buys a specific product or not based on the ad populated on the Social Network. Data uses specific demographic information about the user to do the prediction.
Industry: Social Media
Tools: Hierarchical Clustering, Scipy, Sklearn, Pandas, Numpy, Seaborn
Retail Customer segmentation based on spending patterns
Description: Customer analysis plays a crucial role in determining the profitability of Retail companies. Segmentation of the customers based on their purchase patterns helps Retail companies to cluster their user base and serve them effectively.
Industry: Retail
Tools: K-Means, Clustering, Sklearn, Pandas, Numpy, Seaborn
Market Basket Analysis using Apriori Algorithm
Market Basket Analysis is a technique which identifies the strength of association between pairs of products purchased together and identify patterns of co-occurrence. A co-occurrence is when two or more things take place together.
The technique determines relationships of what products were purchased with which other product(s).
Industry: Retail
Tools: Apriori Algorithm, Numpy, Pandas
SMS Spam Detection using Natural Language Processing
In our day-to-day lives, we receive a large number of spam/junk messages either in the form of Text (SMS) or E-mails. It is important to filter these spam messages since they are not truthful or trustworthy.
In this case study, we apply various machine learning algorithms to categorize the messages depending on whether they are spam or not.
Industry: Social Media
Tools: NLP, Sklearn, Logistic Regression
Sentiment analysis on Restaurant Reviews using Natural Language processing and Supervised Learning
It involves in identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude towards a particular topic, product, etc. is positive, negative, or neutral.
Industry: Food and Beverages, E-Commerce
Tools: NLP, Sklearn, Support Vector Machine
Image Classification using Deep Learning
Classifying Images based on the features is a tough problem. With Deep Learning algorithms like CNN it has become fairly easy. In this project we will learn to classify two faces using Convolutional Neural Networks.
Industry: Media
Tools: Deep Learning, Neural Networks, Tensorflow, Keras, CNN
Content Based Recommender Engine using Deep Learning
Content based recommender systems use the content in the data to segment items and then use them for recommending similar items. In this project we will use Deep Learning and Convolutional Neural Networks to perform content-based recommender engine.
Industry: E-Commerce, Retail
Tools: Tensorflow, Keras, CNN, Transfer learning, Deep Learning
Chatbots using Recurrent Neural Networks and Deep Learning
A chatbot is an artificial intelligence (AI) software that can simulate a conversation (or a chat) with a user in natural language through messaging applications, websites, mobile apps or through the telephone. In this project we will build a conversational chatbot using Deep Learning.
Industry: Retail, Banking, E-Commerce, Media
Tools: Tensorflow, Keras, RNN, LSTM, Numpy
Stock market price prediction
This project deals with the predictions of stock market prices using history of Data. It also considers the physical factors vs. psychological, rational and irrational behavior etc. Machine learning techniques implemented in Python acts as game changer for the predictions. Algorithms including Linear regression, LSTM and ARIMA model are used for the same.
Industry: Finance
Tools: Tensorflow, Keras, LSTM, RNN
Exploratory Data analysis of Crime records in Boston
This project analyses data using quantitative prediction of crimes in Boston and drawing visualizations of Trends in the data over the years. Exploratory Data Analysis is carried on the crimes data using lots of techniques from Linear model to Stochastic gradient boosting.
Industry: Defense and Law Enforcement
Tools: Linear Regression, XGBoost, Numpy, Sklearn, Pandas, Matplotlib
