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Product
Quantity
Price
Web Development Using HTML, CSS & JS1
+
649.00
Python Programming for Beginners1
+
649.00
Machine Learning1
+
649.00
JAVA Programming with OOPs1
+
649.00
Data Structures & Algorithms With JAVA1
+
799.00
Advanced JAVA for Back End Development
Advanced Java for Back-End Development1
+
799.00
Data Science1
+
799.00
COMBO (Machine Learning + Python)1
+
1,190.00
COMBO (Advanced JAVA + JAVA programming)1
+
1,290.00
Full Stack Development
Full Stack Development with JAVA1
+
1,590.00
All courses1
+
2,990.00

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FAQs – Frequently Asked Questions

This is an online Internship program where you will get access to pre-recorded lectures that you can learn from at your own comfort.

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The tutorials are pre-recorded and organized into separate modules so that you can learn at your convenience.
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In addition to the current offer, you can avail more discount if you enroll in multiple courses:

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Web Development Using HTML CSS & JS

Demo Lecture

Course Curriculum

  1. Introduction
  2. Installing IDE
  3. Tags
  4. Tags Part – 2
  5. Div & Span
  6. Lists
  7. Tables
  8. Links & Images
  9. Forms
  10. Frames
  11. CSS Starts
  12. Comments, Margin, Padding & Border
  13. Colors
  14. Box Model
  15. Properties
  16. Properties and Grids
  17. Transitions and Transform
  18. Float and Clear
  19. Positions
  20. Navbar
  21. Z-Index, Opacity and Shadow Effect
  22. Flexbox
  23. Pseudo Selectors
  24. Keyframes
  25. Responsiveness
  26. Media Queries
  27. JavaScript Introduction and Console
  28. Data Types & Variables
  29. Comments, Strings and Its Functions
  30. Operators and Loops
  31. Array, Object and Functions
  32. Alert, Prompt and Confirm
  33. Query Selector and Website
  34. DOM
  35. DOM Part – 2
  36. Event Listeners
  37. Arrow Function
  38. Math Object
  39. Project – Portfolio Website

Python Programming

Demo Lecture

Course Curriculum

  1. Introduction
  2. Installing Python and VS Code
  3. Variables and Data Types
  4. Assignment – 1
  5. Keywords, Type Conversion and Input
  6. Assignment – 2 and 3
  7. Operators
  8. Assignment – 4
  9. Decision Control
  10. Assignment – 5
  11. Assignment – 6
  12. While Loop
  13. Assignment – 7
  14. For Loop and Range
  15. Assignment – 8
  16. Lists
  17. Assignment – 9
  18. STR
  19. Assignment – 10
  20. Tuple
  21. Assignment – 11
  22. Sets
  23. Assignment – 12
  24. Dict
  25. Assignment – 13
  26. Functions
  27. Assignment – 14
  28. OOPs Part – 1
  29. Iterator and Generator
  30. Assignment – 15
  31. Map, Filter, Reduce & Decorator
  32. File Handling
  33. Recursion
  34. Assignment – 16
  35. OOPs Part – 2
  36. OOPs Part – 3
  37. Project – Word Puzzle Game

JAVA Programming

Demo Lecture

Course Curriculum

  1. Introduction to JAVA
  2. Installing the Softwares
  3. Basic JAVA Syntax
  4. Variables and Data Types
  5. Arithmetic Operators
  6. User Inputs
  7. If-Else Condition
  8. While Loop
  9. For Loop
  10. Do While Loop
  11. Loops Practice
  12. Operators
  13. Methods
  14. Classes in JAVA
  15. Getter and Setters
  16. Constructors
  17. Arrays
  18. 2D Arrays
  19. Inheritance
  20. Types of Inheritance
  21. Abstract Classes
  22. Interfaces
  23. Methods Overloading and Overriding
  24. This Keyword
  25. Super Keyword
  26. Polymorphism
  27. Try, Catch and Finally
  28. Checked and Unchecked Exceptions
  29. Throw and Throws Keyword
  30. Multi Threading in JAVA
  31. Methods of Thread
  32. Life Cycle of Thread
  33. Thread Priority
  34. Collection Framework
  35. Packages in JAVA
  36. Access Modifiers
  37. Break and Continue
  38. Recursion
  39. File Handling
  40. Char System, Byte Stream and Buffered Stream
  41. Minor Project
  42. Main Project

Data Structures & Algorithms with JAVA

Demo Lecture

Course Curriculum

  1. Introduction to DSA
  2. Arrays & Arraylist
  3. Linear Search
  4. Linear Search and Practice Questions
  5. Binary Search
  6. Binary Search Question
  7. Search in 2D Arrays
  8. Bubble Sort
  9. Selection Sort
  10. Insertion Sort
  11. Cyclic Sort
  12. Cyclic Sort Questions
  13. String and String Builder
  14. Recursion
  15. Time and Space Complexity
  16. Bitwise Operator and Number System
  17. Practice Questions
  18. Recursion Level-1 Questions
  19. Array Recursion
  20. Patterns and Sorting Using Recursion
  21. Merge Sort
  22. Quick Sort
  23. Backtracking
  24. N-Queens, N-Knights
  25. Linked List
  26. Linked List Questions
  27. Stack & Queues
  28. Stack & Queues Questions
  29. Hashset & Hashmap
  30. Trees & Practice Questions

Machine Learning

Demo Lecture

Course Curriculum

  1. Introduction to Machine Learning
  2. Installing Python
  3. NumPy
  4. Pandas
  5. Types – Supervised, Unsupervised and ReInforced
  6. Linear Regression
  7. Logistic Regression
  8. Clustering
  9. K Nearest Neighbours
  10. Support Vector Machine
  11. Decision Trees and Random Forests
  12. Overfitting and Underfitting
  13. Regularization to ML Libraries
  14. Project – Article Recommendation System
  15. How to use Kaggle Platform
  16. Introduction to Deep Learning

Advance JAVA for Back-End Development

Demo Lecture

Course Curriculum

  1. SQL
  2. REST Architecture
  3. REST Key Concepts
  4. What Are We Building
  5. Layers and IDE
  6. Setup
  7. Creating User API
  8. Working with User API
  9. Completing User API
  10. Handling Exception Globally
  11. Model Mapper
  12. Validation
  13. Creating Category API
  14. Testing and Validating Category API
  15. Creating Entity Relationships
  16. Creating User API
  17. Working on POST API
  18. getAllPost and getPostById API
  19. Finishing POST API
  20. Implementing Pagination
  21. Modifying POST Response
  22. Implementing Sorting
  23. Implementing Searching
  24. Uploading Images in Our Map
  25. Serving Image
  26. Finishing Image Upload
  27. Comment API
  28. Spring Security
  29. Complete Spring Security Implementation
  30. Swagger Documentation
  31. Starting Deployment of Our Blog App
  32. Deploying our App on AWS
  33. Testing Our API Online

Data Science

Demo Lecture

Course Curriculum

  1. Introduction : What is Data Science?
  2. Introduction to Data
  3. Types of data: Structured vs. Unstructured
  4. Basics of data collection and data entry
  5. Data Wrangling
  6. Data cleaning and pre-processing
  7. Handling missing data
  8. Exploratory Data Analysis (EDA)
  9. Descriptive statistics
  10. Data visualization basics with Matplotlib and Seaborn
  11. Machine Learning Overview
  12. Types of machine learning: supervised, unsupervised, and reinforcement learning
  13. Supervised Learning
  14. Regression
  15. Classification
  16. Unsupervised Learning
  17. Clustering
  18. Model Evaluation
  19. Train/Test split
  20. Cross-validation
  21. Performance Metrics (Accuracy, Recall, Precision and Sensitivity)
  22. Basic Queries
  23. SELECT, WHERE LIKE, DISTINCT, BETWEEN, GROUP BY, ORDER BY
  24. Joins: Left, Right, Inner, Full
  25. Data Visualization Tools (Power BI and Tableau)
  26. Overview of Power BI
  27. Using Power Query Editor
  28. Visualizing Loaded Data
  29. Creating dashboard
  30. Overview of Tableau
  31. Visualizing data
  32. Creating Story
  33. Creating dashboard
  34. Mini Project
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