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ML & DL Specialization With Cloud

(Batches Start from 4th, 15th & 26th March 2024)

About The Program

With the belief to build a healthy ecosystem as per the Industry Standards REGex Software brings a Training/Internship Program on “Machine Learning and Deep Learning With Cloud”. We organize Machine Learning, Deep Learning and Cloud 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 With Cloud” program is a valuable resource for beginners and experts. This program will introduce you to Machine Learning, Deep Learning, AWS Cloud: Connecting, Managing, Deploying and Updating Cloud from Basics to Advance etc. from Basics to Advance. If you want to become Machine Learning Expert, REGex introduce this program for you.

Weekly Duration

20 Hours Per week

Location

Physical (Jaipur)
or 
Online (Google Meet)

Duration

6 Months

Participants

25 – 30 per Batch

What you will Learn

Machine Learning

Duration: 35 - 40 Hours

Deep Learning

Duration: 35 - 40 Hours

AWS

Duration: 35 - 40 Hours

Study Material

  • E-Notes.
  • Poll Test & Assignments .
  • Over 300+ hours of Live Video Lectures available on demand.
  • Accessing lecture videos and notes.
  • 24*7 Mentorship Support
  • Engaging in real-time project assignments

Output

  • Able to think out of the box
  • Become expert in multiple technology domains like: Machine Learning, Deep Learning, AWS
  • 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

amazon
wipro
capgemini
google
microsoft
myntra
paytm
ibm
Cognizant
infosys
intel
hcl
hp
honeywell
oracle1
dell
fractal
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What People Tell About Us

Best IT Training and Internship Company in Jaipur. Highly recommended. Supportive faculties, Management, online and offline sessions access with recording access help every student to concentrate more on learning. Practical Learning and working on live projects with team is a main key highlights of REGEX.

Drishti Khandelwal Ex-Student, Django Batch

The experience of learning in the Institute is really good. I've joined the MERN full stack course doing well. Thanks to the mngt. To provide certified facility. Very helping in solving my queries and Institute provide me to practical knowledge and demo project to improve my skills..... Thnks to Regex software service

Gulshan S Arya Ex-Student, Mearn Stack

This training center is exceptional, providing me with extensive knowledge in various domains and technologies. I enrolled in the Python Django course eight months ago, where I learned website development. Prior to joining this coaching, I struggled with speaking English, but now I have gained the ability to communicate effectively. My experience has been extremely positive, and I strongly recommend joining The Regex Software Services at the earliest opportunity.

Mohit Sharma Ex-Student, python django

Competitive Programming is the best course they have - i am part of both python and C++ course. Cracked several interviews with their course, poll test & assignment are always new and beneficial. Best CP course you will find here, i hope this will be beneficial for you

Yaman Singh Ex-Student, CP Batch

Tushar sir is best in delivery. His approach is mind blowing. I have not found any gap although I am from U.S Lots of Big Data tools I have learnt like Hadoop, Hive, Spark, Sqoop & most amazingly Talend ETL Tools which was the most lovely part of training. every component is told in very simple terms with great practical approach

Josh Well Ex-Student, BigData Batch

I recently joined Python Django(Web Development - Full Stack)Course About Course: - I must say instructor makes every concept simple to understand - No Copy Paste,Every line of code is explained - Even given Assignments to work on - Even given Projects to work on If you looking to learn Python Django I highly recommend to go for this course

Salman Khan Ex-Student, Django Batch

I am from UK & loved the teaching. Competitive Programming was the best experience I had in coding. I can truly say the money I spend is worth it. Go for it guys!!

Jack Ryan Ex-Student, CP Batch

Placed Students

Gunjan Saini National Instruments
Gunjan Saini
National Instruments
Prem Wipro & IBM
Prem
Wipro & IBM
Purnima ponrajkumar Indium software
Purnima ponrajkumar
Indium software
sonal Wipro
sonal
Wipro
Mohammad Atash Shaikh Wipro
Mohammad Atash Shaikh
Wipro
Ayush Kumar srivastava Sopra
Ayush Kumar srivastava
Sopra
Sourav Dash Tek System
Sourav Dash
Tek System
Simran Khatri Techmatrix jaipur
Simran Khatri
Techmatrix jaipur
Madhav Sharma Cognizant
Madhav Sharma
Cognizant
Rajkishor. P. Game Redington India Ltd
Rajkishor. P. Game
Redington India Ltd
Bhuvan Nucleus software
Bhuvan
Nucleus software
Akshay Kachave Talent Sikha
Akshay Kachave
Talent Sikha
Vasavi Uppala NCR coperation
Vasavi Uppala
NCR coperation
Ashish Chauhan wipro
Ashish Chauhan
wipro
Pratik Choubey TCS
Pratik Choubey
TCS
Debarya Pal Accenture
Debarya Pal
Accenture
Vritika Vijay Kamra Larsen and toubro infotech
Vritika Vijay Kamra
Larsen and toubro infotech
Mihir Vatsa Infosys
Mihir Vatsa
Infosys
Divyansh Singh Sengar Infosys
Divyansh Singh Sengar
Infosys
Harsha Kumari Bank Of America
Harsha Kumari
Bank Of America
Vatan Gupta Accenture
Vatan Gupta
Accenture
Fardeen Khan Deloitte us
Fardeen Khan
Deloitte us
Divyanshi jain Deloitte
Divyanshi jain
Deloitte
Hritick goyal Ranjio
Hritick goyal
Ranjio
Sumodh c Sabu TCS
Sumodh c Sabu
TCS
Swati Accenture
Swati
Accenture
Nishant Kumar cognizant
Nishant Kumar
cognizant
Saquib Mansuri Circulants
Saquib Mansuri
Circulants
Dhanisha sharma TCS
Dhanisha sharma
TCS
Aditya Prasad Capgemini
Aditya Prasad
Capgemini
Sathvika Chekuri Barclays
Sathvika Chekuri
Barclays
Priyanshu Lasod Barclays Arcgate
Priyanshu Lasod
Barclays Arcgate
Jaya Mendhe Accenture
Jaya Mendhe
Accenture
Dipali Jp Morgan Chase & Co.
Dipali
Jp Morgan Chase & Co.
Meenal Hewlett-Packard
Meenal
Hewlett-Packard
Riya Khanna HSBC
Riya Khanna
HSBC
Muskan Hewlett-Packard
Muskan
Hewlett-Packard
Praveen Jangid Celebal Tech
Praveen Jangid
Celebal Tech
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Course Content

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

AWS Cloud

1. Design Resilient Architectures

  • AWS global infrastructure (for example, Availability Zones, AWS Regions, Amazon Route 53)
  • AWS managed services with appropriate use cases (for example, Amazon Comprehend, Amazon Polly)
  • Basic networking concepts (for example, route tables)
  • Disaster recovery (DR) strategies (for example, backup and restore, pilot light, warm standby, active-active failover, recovery point objective [RPO], recovery time objective [RTO])
  • Distributed design patterns
  • Failover strategies
  • Immutable infrastructure
  • Load balancing concepts (for example, Application Load Balancer)
  • Proxy concepts (for example, Amazon RDS Proxy)
  • Service quotas and throttling (for example, how to configure the service quotas for a workload in a standby environment)
  • Storage options and characteristics (for example, durability, replication)
  • Workload visibility (for example, AWS X-Ray) Skills in:
  • Determining automation strategies to ensure infrastructure integrity
  • Determining the AWS services required to provide a highly available and/or fault-tolerant architecture across AWS Regions or Availability Zones
  • Identifying metrics based on business requirements to deliver a highly available solution
  • Implementing designs to mitigate single points of failure
  • Implementing strategies to ensure the durability and availability of data (for example, backups)
  • Selecting an appropriate DR strategy to meet business requirements
  • Using AWS services that improve the reliability of legacy applications and applications not built for the cloud (for example, when application changes are not possible)
  • Using purpose-built AWS services for workloads

2. Design High-Performing Architectures

  • Hybrid storage solutions to meet business requirements
  • Storage services with appropriate use cases (for example, Amazon S3, Amazon Elastic File System [Amazon EFS], Amazon Elastic Block Store [Amazon EBS])
  • Storage types with associated characteristics (for example, object, file, block) Skills in:
  • Determining storage services and configurations that meet performance demands
  • Determining storage services that can scale to accommodate future needs
  • AWS compute services with appropriate use cases (for example, AWS Batch, Amazon EMR, Fargate)
  • Distributed computing concepts supported by AWS global infrastructure and edge services
  • Queuing and messaging concepts (for example, publish/subscribe)
  • Scalability capabilities with appropriate use cases (for example, Amazon EC2 Auto Scaling, AWS Auto Scaling)
  • Serverless technologies and patterns (for example, Lambda, Fargate)
  • The orchestration of containers (for example, Amazon ECS, Amazon EKS) Skills in:
  • Decoupling workloads so that components can scale independently
  • Identifying metrics and conditions to perform scaling actions
  • Selecting the appropriate compute options and features (for example, EC2 instance types) to meet business requirements
  • Selecting the appropriate resource type and size (for example, the amount of Lambda memory) to meet business requirements
  • AWS global infrastructure (for example, Availability Zones, AWS Regions)
  • Caching strategies and services (for example, Amazon ElastiCache)
  • Data access patterns (for example, read-intensive compared with write-intensive)
  • Database capacity planning (for example, capacity units, instance types, Provisioned IOPS)
  • Database connections and proxies
  • Database engines with appropriate use cases (for example, heterogeneous migrations, homogeneous migrations)
  • Database replication (for example, read replicas)
  • Database types and services (for example, serverless, relational compared with non-relational, in-memory)

3. Design Cost-Optimized Architectures

  • Access options (for example, an S3 bucket with Requester Pays object storage)
  • AWS cost management service features (for example, cost allocation tags, multi-account billing)
  • AWS cost management tools with appropriate use cases (for example, AWS Cost Explorer, AWS Budgets, AWS Cost and Usage Report)
  • AWS storage services with appropriate use cases (for example, Amazon FSx, Amazon EFS, Amazon S3, Amazon EBS)
  • Backup strategies
  • Block storage options (for example, hard disk drive [HDD] volume types, solid state drive [SSD] volume types)
  • Data lifecycles
  • Hybrid storage options (for example, DataSync, Transfer Family, Storage Gateway)
  • Storage access patterns • Storage tiering (for example, cold tiering for object storage)
  • Storage types with associated characteristics (for example, object, file, block)
  • AWS cost management service features (for example, cost allocation tags, multi-account billing)
  • AWS cost management tools with appropriate use cases (for example, Cost Explorer, AWS Budgets, AWS Cost and Usage Report)
  • AWS global infrastructure (for example, Availability Zones, AWS Regions)
  • AWS purchasing options (for example, Spot Instances, Reserved Instances, Savings Plans)
  • Distributed compute strategies (for example, edge processing)
  • Hybrid compute options (for example, AWS Outposts, AWS Snowball Edge)
  • Instance types, families, and sizes (for example, memory optimized, compute optimized, virtualization)
  • Optimization of compute utilization (for example, containers, serverless computing, microservices)
  • Scaling strategies (for example, auto scaling, hibernation)
  • AWS cost management service features (for example, cost allocation tags, multi-account billing)
  • AWS cost management tools with appropriate use cases (for example, Cost Explorer, AWS Budgets, AWS Cost and Usage Report)
  • Caching strategies
  • Data retention policies
  • Database capacity planning (for example, capacity units)
  • Database connections and proxies
  • Database engines with appropriate use cases (for example, heterogeneous migrations, homogeneous migrations)
  • Database replication (for example, read replicas)
  • Database types and services (for example, relational compared with non-relational, Aurora, DynamoDB)
  • AWS cost management service features (for example, cost allocation tags, multi-account billing)
  • AWS cost management tools with appropriate use cases (for example, Cost Explorer, AWS Budgets, AWS Cost and Usage Report)
  • Load balancing concepts (for example, Application Load Balancer)
  • NAT gateways (for example, NAT instance costs compared with NAT gateway costs)
  • Network connectivity (for example, private lines, dedicated lines, VPNs)
  • Network routing, topology, and peering (for example, AWS Transit Gateway, VPC peering)
  • Network services with appropriate use cases (for example, DNS)

 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

Price: ₹59,999/- (Flat 75% off) => ₹25,000/-  
(Limited Period Special Offer)

International Fee

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

Fee Can be Paid as No Cost EMI @2500/Month

Cashback Policy

  • You will get your Unique Referral Code after successful paid registration.
  • You will get ₹2000 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 ₹2000*10 = ₹20,000 on monthly basis.
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(Batches Start from 4th, 15th & 26th March 2024)

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*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*