Artificial Intelligence & Data Analytics Industrial Internship Program

(Batches Start from 6th January 2025)

About The Program

With the belief to build a healthy ecosystem as per the Industry Standards REGex Software brings a 6 Months Industrial Internship Program on “AI &  Data Analytics”. We organize AI & Data Analytics Program for improving the knowledge and skills of the Students so that they can become specialized in the field of AI & Data Analytics Program and get their Dream Job in Software Development Field in Big MNCs.

REGex Software Services’s  “AI & Data Analytics ”  is a valuable resource for beginners and experts. This program will introduce you to Machine Learning, Deep Learning, Generative AI, SQL etc. from Basics to Advance. If you want to become Data Scientist, REGex introduce this program for you AI & Advance Data Analytics.

Weekly Duration

20 Hours Per week

Location

Physical (Jaipur)
or 
Online (Google Meet)

Duration

6 Months 

Participants

30 per Batch

What you will Learn

Python

Machine Learning

Deep Learning

Database

AWS

Generative AI

Study Material

  • E-Notes
  • Assignments & Poll test
  • Access of Recordings & Study Material
  • Mentorship Support
  • Work on multiple Minor Projects & Use Cases
  • Work on Live Projects

Output

  • Able to think out of the box
  • Become expert in multiple technology domains like: Python, Machine Learning, Deep Learning, Sql, Map Reduce, Generative Ai, Kafka, and AWS(for deployment)
  • Understand working of ML models deployment with AWS
  • Build projects on multiple technology domains
  • Work on more than 25 Use CASES & Projects
  • Learn to deploy your models on AWS Sagemaker or Google cloud Platform

Why Choose Us

Live Sessions

Live Sessions by Expertise Trainers and Access of Recorded Session is also available.

Live Projects
Get a chance to work on Industry Oriented Projects to implement your learning.
24*7 Support
24*7 Mentorship Support available for all Students to clear all of your doubts.
Opportunities
REGex provides Internship / Job opportunities to the best Students in different Companies.

Placement Opportunities in Companies

You can get Internship/Training Opportunities to get placed in HP, DELL, Honeywell, Rightpoint, Frontdoor, Fractal and many more according to your performance.

Package Offered So Far

5 LPA
8 – 10 LPA
32 LPA

Placed Students//Partnership

What People Tell About Us

Placed Students

Course Content

Python

  • What is Python?
  • History and evolution of Python.
  • Python’s popularity and use cases.
  • Setting up Python (installation).
  • Running Python scripts and using the interactive shell.
  • Variables and data types (integers, floats, strings, booleans).
  • Comments in Python.
  • Basic arithmetic operations.
  • String manipulation.
  • Variables and naming conventions.
  • Conditional statements (if, elif, else).
  • Looping structures (for and while loops).
  • Iterating through sequences (lists, strings, dictionaries).
  • Range and enumeration.
  • Using break and continue.
  • Lists: creation, manipulation, and methods.
  • Tuples: creation, immutability, and uses.
  • Dictionaries: key-value pairs and operations.
  • Sets: unique elements and set operations.
  • List comprehensions.
  • Defining functions.
  • Function parameters and return values.
  • Scope and lifetime of variables.
  • Importing modules.
  • Creating and using custom modules.
  • Reading from and writing to files.
  • Working with different file modes (read, write, append).
  • Using context managers with files.
  • Classes and objects.
  • Attributes and methods.
  • Inheritance and encapsulation.
  • Polymorphism and method overriding.
  • Constructors and destructors.

Machine Learning

  • Machine learning applications
  • ML vs DL
  • Basics of Python [Syntax]
  • Working with Pandas, Numpy & Matplotlib
    ■ Working with Missing Data
    ■ Data Grouping
    ■ Data Subsetting
    ■ Merging & Joining Data Frames
  • Importing Libraries & Datasets
  • Munging & handling missing data
  • Splitting the dataset into Training set & Test set
  • What is actual machine learning
  • Various Aspects of Data – type, Variables & Category
  • Machine Learning & its Various types
  • About Supervised & Unsupervised Learning
  • Understanding Simple Linear Regression
  • Understanding the P -Value
  • Support Vector Regression
  • Principal Component analysis (PCA – Theory)

  • PCA with Case-Study

  • Linear Discriminant Analysis(LDA) for Dimension Reduction

  • Feature Selection to Select the Most Relevant Predictors

Deep Learning

  • NLP Intuition

  • Types of NLP

  • Classification vs Deep Learning Models

  • The Neuron

  • Activation function

  • How Neural network work & learn by itself

  • Gradient Descent

  • Stochastic Gradient Descent

  • Ethics of Deep Learning

  • What are convolutional neural networks [CNN] ?

  • CNN Architecture

  • CNN Code preparation

  • Recurrent Neural Networks

  • Several layers

    ■ ReLLu Operation

    ■ Pooling

    ■ Flattering

    ■ Full Connection

  • Statistics

  • Sample Selection

  • Probability Theory

  • Hypothesis

  • Model Relationship

  • Model Fit

  • Descriptive Statistics

  • Types of Data

  • Qualitative Data

  • Histograms

  • Different Plots

  • Centrality and Spread

  • Outliers

  • Median, Mean, Mode

Computer Vision

  • What is computer vision & its application

  • Face Detection

    ■ Adding more features & Categorization

    ■ Object Detection & Image creation

    ■ Working with Images & vectors

  • Facial Expression Recognition in Code (Binary / Sigmoid /Logistic Regression)

  • Object detection
  • Neural style transfer
  • YOLO
  • RCNNs
  • Resnet 50
  • Tensorboard
  • What are Vectors
  • Working with word Analogy
  • Text Classification
  • Pre Trained word vectors from word2vec
  • Language Models
  • Introduction to Tensorflow / Keras
  • Most used right now
  • Resources gathering
  • Keras dealing with Missing Data
  • Dealing with Categorical data
  • Introduction to GenAI
  • Types of GenAI Models
  • Generative Adversarial Networks (GANs)
  • How to use AI in Business
  • How Chatgpt & Google Gemini works
  • Prompt Engineering Fundamentals

Power BI

  • Introduction
  • Meet Microsoft Power BI Desktop
  • Interface & Workflow
  • Helpful Power BI Resources
  • New Power BI Ribbon
    • Introduction
    • Types of Data Connectors in Power BI Desktop
    • The Power BI Query Editor
    • Demo: Basic Table Transformations in Power BI
    • Power BI Demo:
  • Working with Text Tools
  • Numerical Values
  • Date & Time Tools
  • Creating a Rolling Calendar
  • Grouping & Aggregating Records
  • Pivoting & Unpivoting Data
    • Merging Queries in Power BI Desktop
    • Appending Queries in Power BI Desktop
    • Configuring Power BI Data Source Settings
    • Configuring Power BI Query Refresh Settings
    • Additional Data Types & Categories in Power BI
    • Defining Hierarchies in Power BI Desktop
  • Importing Models from Excel to Power BI
    • Introduction
    • What is a “Data Model”?
    • Principles of Database Normalization
    • Understanding Data Tables vs. Lookup Tables
    • Understanding Table Relationships vs. Merged Tables
    • Creating Table Relationships in Power BI Desktop
  • Snowflake Schemas in Power BI
  • Managing & Editing Table Relationships in Power BI Desktop
  • Managing Active vs. Inactive Relationships
  • Understanding Relationship Cardinality
  • Connecting Multiple Data Tables in Power BI Desktop
  • Understanding Filter Flow
  • Two-Way Filters in Power BI Desktop (USE WITH CAUTION!)
  • Hiding Fields from the Power BI Report View
  • New Power BI Desktop “Model” View
  • Introduction
  • Meet Data Analysis Expressions (DAX)
  • Intro to DAX Calculated Columns
  • Intro to DAX Measures
  • Adding Columns & DAX Measures in Power BI Desktop
  • Implicit vs. Explicit DAX Measures
  • Filter Context Examples in Power BI
  • Understanding DAX Syntax & Operators
  • Common DAX Function Categories
  • DAX Demo: 
    • Basic Date & Time Functions
    • Conditional & Logical Functions (IF/AND/OR)
    • Common Text Functions
    • Joining Data with RELATED
    • Basic Math & Stats Functions
    • COUNT Functions (COUNTA, DISTINCTCOUNT, COUNTROWS)
    • CALCULATE
    • CALCULATE & ALL
    • CALCULATE & FILTER
    • Iterator Functions (SUMX, RANKX)
    • Time Intelligence Formulas
  • FILTER Function
  • LOGICAL Functions
  • MATHEMATICAL Functions
  • STATISTICAL Functions
  • TEXT Functions
  • TIME INTELLEGENT Functions
  • Other Functions
  • Power Query Editor
  • Power BI Aggregation & Template
    • Introduction
    • Exploring the “Report” View in Power BI Desktop
    • Adding Simple Objects to the Power BI Report Canvas
    • Inserting Basic Charts & Visuals in Power BI
    • Conditional Formatting
    • Power BI Report Formatting Options
    • Power BI Report Filtering Options
    • Power BI Demo:
      • Exploring Data with Matrix Visuals
      • Filtering with Date Slicers
  • Showing Key metrics with Cards & KPI Visuals
    • Inserting Text Cards
    • Visualizing Geospatial Data with Maps
    • Visualizing Data with Treemaps
    • Showing Trends with Line & Area Charts
    • Adding Trend Lines & Forecasts 
    • Goal pacing with Gauge Chart
    • Adding Drillthrough Filters
  • Editing Power BI Report Interactions
  • Managing & Viewing Roles in Power BI Desktop

Database

  • Functional Dependency
  • Closure of Attributes
  • Types of Keys: PrimaryKey CandidateKey & Super Key in DBMS
  • Normalization
  • Indexing
  • Transaction and Concurrency Control
  • Transaction in DBMS
  • ACID Propertise in DBMS
  • Joins in DBMS
  • Create & Alter Table
  • Constraints in SQL
  • Sql Queries & Sub Queries
  • SQL Stored Procedure
  • View, Cursor & Trigger in SQL
  • Common Table Expession
  • Replace Null and Coalesce Function
  • Running Total In SQL

AWS (For Deployment)

  • Create your own ML models
  • Deploy ML models
  • AWS Recognition
  • ML & DL Projects
    • Titanic Classification
    • Carbon Emissions
    • Car price analysis
    • Drug Prediction
    • Hand written Digit Recognition
    • CIFAR 10
    • CIFAR 100
    • Cats vs dogs
    • Intel scene Classification
    • Transfer Learning
    • Object Detection
  • Power BI Projects & Use Cases
    • Importing Models from Excel to Power BI
    • Snowflake Schemas in Power BI
    • Showing Key metrics with Cards & KPI Visuals
  • BigData Projects
    • Zomato Analysis
    • Cricket & Football Data Analysis
    • Flipkart Product Data Analytics

 Note: Content may Subject to Change by REGex as per Requirement

Extra Sessions

Additinal Session on GIT, Linux, Docker, AWS Basics, Jenkins and many more for all students.

Fee Structure

Indian Fee (Physical)

Price: ₹40,000/- (Flat 50% off) => ₹20,000/- 

Indian Fee (Online)

Price: ₹40,000/- (Flat 50% off) => ₹20,000/- 

 

  Fee can be paid in NO COST EMI

Cashback Policy

  • You will get your Unique Referral Code after successful paid registration.
  • You will get ₹1000 Cashback directly in your account for each paid registration from your Unique Referral Code on monthly basis(After Closing Registrations of this program) .
  • For Example:- If we received 10 paid registration from your Unique Referral Code then you will receive ₹1000*10 = ₹10,000 on monthly basis.
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(Batches Start from 6th January 2025)

*It will help us to reach more
*Extra off is applicable on 1 time payment only. Seats can be filled or Price can be increased at any time. Refund policy is not available*