Machine Learning & Deep Learning Program

(Batches Start in AugustSeptember & October 2025)

About The Program

With the belief to build a healthy ecosystem as per the Industry Standards REGex Software brings an  Industrial Training/Internship Program on “Machine Learning and Deep Learning”. We organize Machine Learning and Deep Learning  Industrial Training/Internship Program for improving the knowledge and skills of the Students/Professionals, so that they can become specialized in the field of Machine Learning and get their Dream Job in Software Development Field in Big MNCs.

REGex Software Services’s “Machine Learning and Deep Learning” program is a valuable resource for beginners and experts. This program will introduce you to Python, Machine Learning, Deep Learning etc. from Basics to Advance. If you want to become Machine Learning Expert, REGex introduce this program for you.

Key Benefits & Perks

  • Get Summer Internship Offer Letter
  • Need to Spend Min. 5 hours with REGex
  • Get Internship Project Completion Certificate
  • No Previous Knowledge Required
  • Get Summer Training Certificate
  • Get Performance based Letter of Recommendation (LOR)

August Batches Dates

Batch 1: 04th August 2025
Batch 2: 11th August 2025
Batch 3: 18th August 2025
Batch 4: 25th August 2025

September Batches Dates

Batch 1: 01st September 2025
Batch 2: 08th September 2025
Batch 3: 15th September 2025
Batch 4: 22nd September 2025
Batch 5: 29th September 2025

October Batches Dates

Batch 1: 06th October 2025
Batch 2: 13th October 2025
Batch 4: 27th October 2025



Weekly Duration

Location

Participants

20 Hours Per week

Physical (Jaipur)
or 
Online (Google Meet)
25 – 30 per Batch

What you will Learn

Python

Machine Learning

Deep Learning

Generative Ai

Study Material

  • E-Notes
  • Assignments & Poll test
  • 100+ hours on demand Live Video Lectures
  • 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
  • Understand working of ML models deployment with AWS
  • Build projects on multiple technology domains
  • Work on 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.

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.
  • Arithmetic Operators(+,-,*,/,//,%,**)
  • Comparison / Relational Operators (== ,!=)
  • Assignment Operators
  • Logical Operators
  • Bitwise Operators
  • Identity Operators
  • 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.
  • Problem Solving on LeetCode question
  • What is a Function?
  • Types of Functions
  • Function Syntax (Structure)
  • Function Calling
  • Parameters vs Arguments
  • Scope and lifetime of variables.
  • Importing modules.
  • Creating and using custom modules.
  • What is a Module?
  • Types of Modules
  • Why Use Modules?
  • Importing Modules
  • What is a Package?
  • Why Use Packages?
  • Namespace Management
  • Popular Python Packages (numpy, pandas, flask, django, requests, matplotlib, etc.)
  • Reading from and writing to files.
  • Working with different file modes (read, write, append).
  • Using context managers with files.
  • Handling errors with try and except.
  • Raising exceptions.
  • Handling multiple exceptions.
  • Using the ‘finally’ block.
  •  
  • Classes and objects.
  • Attributes and methods.
  • Inheritance and encapsulation.
  • Polymorphism and method overriding.
  • Constructors and destructors.
  • NumPy basics & array manipulations
  • Pandas Series, DataFrames, indexing, merging,
    – Grouping
  • Matplotlib & Seaborn (basic plots, customization)
  • Real-world case studies.
  • Job opportunities and career paths.
  • Contributing to open-source projects.
  • Staying up to date with Python developments.

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
  • K nearest neighbours
  • Logistic regression
  • Naive Bayes
  • Decision trees
  • Random forests
  • Bagging Boosting
  • Maximum likelihood classifier
  • Support vector machines
  • 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

Generative AI

  • Introduction to Generative AI
  • Applications of GenAI:(Text generation, Image generation,Code generation)
  • Evolution of GenAI(From GANs to Transformers and beyond)
  • Introduction to RAG,Retriever,Generator
  • RAG ARCHITECTURE(Input query processing,Retrieval phase,Fusion of retrieved information with the LLM,Response generation)
  • Types of RAG models(RAG-Sequence,RAG-Token)
  • Project(Customer support bots with real-time information retrieval)
  • Introduction to Vector Databases , Key concepts(Vectors and embeddings,High-dimensional space representation)
  • Core Concepts(Indexing and searching vectors,Scalability challenges,Vector quantization techniques)
  • Popular Vector Databases(FAISS (Facebook AI Similarity Search),Milvus,Weaviate)
  • Transformer architecture:(Encoder and Decoder blocks, Multi-Head Attention,Scaled Dot-Product Attention,Positional Encoding)
  • Gemini architecture and its key features , Differences between Gemini and other LLMs (e.g., GPT, BERT)
  • Use cases of Gemini(Multimodal AI (text, images, audio),Content creation, Code generation and debugging)
  • APIs and Tools for Gemini:(Accessing the Gemini API, Authentication and integration)
  • Project (Create Chabot and Deploy it on Streamlit Cloud)
  • Agentic AI Using LangChain and LangGraph project(Business Start up Idea using current businesses in India )

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

Model Deployment & Project

  • Create your own ML models
  • Deploy ML models
  • AWS Recognition
    • 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

 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.

Projects you will work on

Live Client Projects With Development Team Under The Guidance Of Mentor

Fee Structure

Indian Fee (Physical)

Price: ₹59,999/- (Flat 75% off) => ₹14,999/-  
(Limited Period Special Offer)

Indian Fee (Online)

Price: ₹59,999/- (Flat 75% off) => ₹14,999/-  
(Limited Period Special Offer)

International Fee

Price: $1200 (Flat 75% off) => $300
(Limited Period Special Offer)

Fee Can be Paid as No Cost EMI of 6 Months @2500/Month.

Cashback Policy

  • You will get your Unique Referral Code after successful paid registration.
  • You will get Upto ₹1000 Cashback directly in your account for each paid registration from your Unique Referral Code (After Closing Registrations of this program) .
  • For Example:- If we received 10 paid registration from your Unique Referral Code then you will receive Upto ₹1000*10 = ₹10,000.
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(Batches Start from August, September & October 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*