JAVA FULL STACK COURSE TRAINING IN HYDERABAD

About LearnAi.co.in

LearnAI.co.in is a leading training institute that caters to Java Full Stack courses, offering comprehensive learning paths for beginners, intermediates, and experts alike. Our beginner-level courses provide a strong foundation in Java programming and web development, ensuring a smooth transition into the world of full-stack development. For intermediate learners, we offer in-depth training on frontend and backend technologies, enabling them to build dynamic web applications. Our expert-level courses delve into advanced concepts, microservices, and industry best practices, ensuring that professionals stay at the forefront of the rapidly evolving tech landscape. Join LearnAI.co.in today and embark on your journey towards becoming a proficient Java Full Stack developer.

Upcoming Batches

CourseBranchTraining TypeWeekDay
Java Full StackDilsukhnagarHybrid1stWednesday
Java Full StackDilsukhnagarHybrid2ndMonday
Java Full StackDilsukhnagarHybrid3rdWednesday
Java Full StackDilsukhnagarHybrid4thMonday

What is Java Full Stack?

Java Full Stack is a versatile web development approach that involves using Java for both the front-end and back-end of a website or application. It's like being a master chef who not only prepares the delicious dishes but also serves them with style - Java Full Stack developers handle both the user interface and the server-side, creating a seamless and efficient user experience.

Why to learn Java Full Stack?

Learning Java Full Stack is a smart career move as it equips you with a versatile skill set that's in high demand. With the ability to work on both the front-end and back-end of web applications, you'll be well-positioned for a successful career in a rapidly evolving tech industry, opening up diverse job opportunities and ensuring long-term professional growth.

Who can Pursue Java Full Stack?

  • Any Degree pursuing or Graduated,
  • Bachelor’s, Master’s, PhD, and
  • Anyone interested in course
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COURSE CURRICULUM

Python

  • Introduction
  • Installation
  • Fundamental of Python
  • Variables
  • Comments
  • Print Statement
  • Operators
  • Mutable Data Types
  • Data Types
  • Special Data Types
  • Conditions
  • Loops
  • Functions
  • Break, Continue and Pass Statements
  • String Object and working
  • List
  • Tuple
  • Set
  • Dictionaries
  • Map
  • Reduce
  • Filter
  • Classes
  • Objects
  • Inheritance
  • Multiple Inheritance
  • Modules
  • Error Handling

Data Visualization

  • Matplotlib
  • Seaborn
  • Plotly
  • Cuflinks
  • Bokesh

Libraries

  • Numpy
  • Pandas
  • Random
  • Math
  • Scipy
  • sklearn
  • Keras
  • Tensorflow
  • OpenCV
  • NLTK
  • Spacy
  • Lot more…

Tableau

  • Introduction to Data Visualization
  • Introduction to Tableau
  • Basics charts and dashboards
  • Special Char Types
  • Dashboard design and principles
  • Connections with servers
  • Local file access
  • Hands on experience with worksheet

Web Scraping

  • Url
  • Beautiful Soup

Data base

  • SQL
  • MongoDB

Statistics

  • Basics of Statistics
  • Descriptive Statistics
  • Inferential Statistics
  • Qualitative Analysis
  • Quantitative Analysis
  • Hypothesis Testing
  • Data Distribution
  • Probability Distribution
  • Normal Distribution
  • Poison Distribution
  • Outlier Detection
  • Other Statistical Fundamentals

Machine Learning

  • Supervised Learning
  • Unsupervised Learning
  • Semi Supervised Learning
  • Reinforcement Learning

Supervised Learning

  1. Regression Models
  2. Classification Models

Regression Models

  • Introduction to Regression Models
  • Linear Models
  • Linear Regression
  • Multiple Regression
  • Ordinary Least Square Method
  • Non-Linear Models
  • Support Vector Regressor
  • Random Forest Regressor
  • Decision Tree

Evaluation Metrics for Regression Models

  • R-Square
  • Adjusted R Square
  • Mean Square Error
  • Root Mean Square Error
  • Mean Absolute Error

Classification Models

  • Introduction to Classification Models
  • Logistics Regression
  • Naïve Bayes
  • Support Vector Classifier
  • K-NN Classifier
  • Decision Tree Classifier
  • Random Forest Classifier

Evaluation Metrics for Classification Models

  • Accuracy
  • Precision
  • Recall
  • F1 Score
  • ROC Curve

Unsupervised Learning

  • Introduction to Unsupervised Learning

Clustering Models

  • Introduction to Clustering Models
  • K- Mean Clustering
  • Hierarchical Clustering
  • High Dimensional Clustering

Dimension Reduction

  • Principal Component Analysis (PCA) Reinforcement Learning

Reinforcement Learning

  • Introduction to Reinforcement Learning

Featurization, Model Selection & Tuning

  • Feature Extraction
  • Model Defects & Evaluation Metrics
  • Model Selection and Tuning
  • Comparison of machine learning models

Neural Networks

  • Introduction to Deep Learning
  • Fundamental of Neural Networks
  • TensorFlow and Keras
  • Artificial Neural Networks (ANN)
  • Convolution Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • Evaluation of Deep Learning
  • Neural Networks Basics
  • Gradient Descent
  • Introduction to Perceptron & Neural Networks
  • Batch Normalization
  • Activation and Loss Functions
  • Hyper parameter tuning
  • SoftMax
  • Deep Neural Networks
  • Weights initialization

Image Pre-processing

  • Computer Vision
  • Open CV
  • Noise Detection
  • Noise Reduce
  • Low Pass Filters
  • Forward Propagation
  • Backward Propagation
  • Pooling & Padding

Natural Language Processing

  • Introduction to NLP
  • Corpus
  • Natural Language Understanding (NLU)
  • Natural Language Generator (NLG)
  • Tokenization (Word, Sentence, Blank, RegEx)
  • Frequency Distribution
  • Filtering
  • Stemming
  • Lemmatization
  • Stop Words
  • Regular Expressions
  • POS Tagging
  • Syntax Tree
  • Chunking
  • Lemmas
  • Hypernyms
  • Hyponyms
  • Synonyms
  • Antonyms
  • Distance Between words
  • Wordnet
  • Name Entity Recognition
  • Bag of words or Document Matrix
  • Count Vectorization
  • Term Frequency
  • Inverse Document Frequency
  • Sentimental Analysis

Job Roles

  • Java Full Stack Developer
  • Web Application Developer
  • Software Engineer
  • Front-end Developer
  • Back-end Developer
  • Full Stack Architect
  • UI/UX Designer
  • Web Development Consultant
  • IT Project Manager
  • Technical Lead

Components of  Java Full Stack

  • Front-end Technologies
  • Back-end Technologies
  • API Development
  • Version Control
  • Integrated Development Environments (IDEs)
  • Build and Dependency Management
  • Testing and Quality Assurance
  • Containerization and Deployment
  • Continuous Integration/Continuous Deployment (CI/CD)
  • Cloud Services
  • Security
  • Monitoring and Logging
  • Collaboration Tools

What We Offer

24/7 Portal Access
Domain Expertise Trainers
Industrial Standard Course Structure
Job Oriented Programs
Recording Sessions
Assignments on Real time Scenarios
Job Support
Resume Preparations
Internships
Job Assistance
Working on Real Time Projects
Course Completion Certifications

We Provide Higher Quality Services

AND YOU’LL GET SOLUTIONS FOR EVERYTHING
Best Artificial Intelligence Training in Hyderabad with 100% placement Assistance. Learn Data Science with Python, Data Analysis, Artificial Intelligence, Machine Learning, Deep Learning, NLP, Statistics and Tableau.

Branches

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Phone: +91 9390023585
Email: info@learnai.co.in

Head Office

Address: 1st Floor, Rajadhani Theatre Complex, Pillar Number 1546, above Siri Mobiles, Dilsukhnagar, Hyderabad, Telangana 500060.
Phone: +91 9390023585
Email: info@learnai.co.in
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