Machine Learning & Deep Learning Program

(Batches Start from July, August, September & October 2024)

About The Program

With the belief to build a healthy ecosystem as per the Industry Standards REGex Software brings an Training/Internship Program on “Machine Learning and Deep Learning”. We organize Machine Learning and Deep Learning Summer 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)

July Batches Dates

Batch 1: 01st July 2024
Batch 2: 8th July 2024
Batch 3: 19th July 2024
Batch 4: 26th July 2024

August Batches Dates

Batch 1: 06th August 2024
Batch 2: 17th August 2024
Batch 3: 27th August 2024

September Batches Dates

Batch 1: 07th September 2024
Batch 2: 17th September 2024
Batch 3: 24th September 2024

October Batches Dates

 Batch 1: 01st October 2024
Batch 2: 8th   October 2024
Batch 3: 19th October 2024
Batch 4: 26th October 2024

Weekly Duration

Location

Duration

Participants

20 Hours Per week

Physical (Jaipur)
or 
Online (Google Meet)
45 – 60 Days
or
3 – 4 Months
25 – 30 per Batch

What you will Learn

Python

Duration: 25 - 30 Hours

Machine Learning

Duration: 35 - 40 Hours

Deep Learning

Duration: 35 - 40 Hours

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.
  • 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.
  • Handling errors with try and except.
  • Raising exceptions.
  • Handling multiple exceptions.
  • Using the ‘finally’ block.
  • List comprehensions.
  • Lambda functions.
  • Decorators.
  • Generators and iterators.
  • Working with external libraries and APIs.
  • PEP 8 style guide.
  • Code readability and maintainability.
  • Writing docstrings.
  • Version control (e.g., Git).
  • Building simple applications.
  • Real-world examples.
  • Solving coding challenges.
  • Final project.
  • Popular Python libraries and frameworks (e.g., NumPy, pandas, Django).
  • Python for web development, data science, and machine learning.
  • Virtual environments and package management.
  • 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

  • Confusion matrix
  • Accuracy Paradox
  • CAP Curve
  • K-Mean Clustering Intuition
  • K-Mean selecting Numbers of Cluster
  • About Reinforcement Learning

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

Model Deployment & Projects

  • 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)

[Summer Batch(45-60 days)]

Price: ₹59,999/- (Flat 75% off) => ₹14,999/- => ₹8,000/-
(Flat 50% Extra off – Limited Period Special Offer)

Indian Fee (Physical)

[Regular Batch(3-4 Months)]

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

Indian Fee (Online)

[Summer & Regular Batch]​

Price: ₹59,999/- (Flat 75% off) => ₹14,999/- => ₹7,500/-
(Flat 50% off – 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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Summer Industrial Internship/Training Program 2024

For Webinar Videos and Demo Session, Join our Youtube Channel

Enroll Now

(Batches Start from July, August, September & October 2024)

*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*