Winter Training Machine Learning

Winter Training Machine Learning

Introduction to Machine Learning

Machine Learning – As the name suggests is the science of making the machine learn through its previous experiences. It is no less than a wonder that science has progressed to the limit where a computer gets learned with time but to mark your existence in the world like such, one needs to know how to make your machine learn because substantially in the near future your efficiency would be measured through the efficiency of your machine.

Join us if you too believe in innovating your own ideas. Learn Machine Learning along with its application in various projects.

Our Training Program Details

15 Days Training
Course:Machine Learning
Certification By:TechieNest, An ISO 9001:2008 Certified Company
Study Material:Software and PDFs provided to each student.
Trainer Student Ratio:1:20

Course Details


Machine Learning
SESSIONTOPICDURATION
1

Machine Learning

  • Introduction to Machine Learning.
  • Understanding the need.
  • Understanding Big data and machine learning.
  • Running machine learning under linux platform.
  • Introduction to Redhat Enterprise linux.
  • Why linux is important for machine learning with respect to future.
  • Role of Python and R programming in this domain.
  • Basic Introduction of Python syntax and programming logics.
  • Deep dive with Supervised , Unsupervised and Reinforcement learning
  • Algo discussion with use case.
  • Popular machine learning framework like tensorflow , scikit-learn.
2 Hours
2

Python Programming

  • Basic of python and why python for machine learning
  • Installation of software on different OS.
  • Understanding basic syntax with data types
  • List , dict , tuple , string
  • Extracting data from a file
  • Committing your code to GITHUB

2 Hours

3

More About Python Programming

  • Conditional statement and loops
  • Function and modules
  • File handling
  • Creating own modules / library
  • Web scraping with urllib2
  • Grabbing system information from Popen and os library
  • Scanning Network IP & MAC address with loops
2 Hours
4Data Science (A Beginning for New Field of Science )

  • Introduction to Numpy & Matplotlib
  • Managing arrary with numpy
  • Multidimensional array with numpy
  • Unit matrix handling & creating
  • Deleting indexes from matrix
  • Deep dive with Matplotlib
  • Drawing general purpose graphs
  • Graphs with mathematics

Project :- Real Datasets Analysis System

2 Hours
5Pandas

  • Data file formats
  • Data import and export using pandas
  • Exploring about dataframes
  • Real data calculations
  • Working with Graphs on real data
  • Data Analysis

OpenCV

  • Working with images
  • Images conversions
  • Transformation and morphological operations
  • Filters and ROI
  • Working with images and Numpy

Project :- Color Object Detection system (An intro to virtual reality)

2 Hours
6Working with Python for ML

  • Supervised Learning lab with Hello World Program
  • Running the installer and use case
  • Jupyter notebook for machine learning
  • Installation of jupyter notebook
  • DecisionTree classifier implementation
  • Practise lab with DecisionTree algo and number of examples
  • Making own algo with training data with python
2 Hours
7Machine learning techniques

  • Types of learning
  • Advice of applying machine learning
  • Machine learning System Design
  • DecisionTree algo deep dive
  • Training your machine with real time datasets
  • Deep dive with UCI
  • Lab session for loading data from different apis
  • Detecting data from numpy and converting for training and testing data
  • Exercise with ML and others framework
  • Introduction to iris datasets
  • Understanding iris datasets
  • Modifying and loading with scikit-learn
  • Training classifier
  • Algo data process view
2 Hours
8ML Continued with real datasets.

  • Introduction to boston house and pima-indians diabetes datasets
  • Understanding diabetes datasets
  • Applying alogrigthm like Navie Bayes and SVM
  • Implementing graphs with availability
  • Deep dive with algo’s
2 Hours
9Supervised Learning

  • Regression
  • Classification
  • Case study learning in regression
  • Case study learning in classification
  • Comparing the result of DecisionTree and Navie bayes algo
  • Graphploting with Matplotlib for comparison
2 Hours
10Google API for Speech Recognition

  • Understanding Voice Samples
  • Converting voice to text
  • Basic Systems requirements
  • Live Exceptions Handelling
  • Cloud Comparisions and Data Retrival

Natural Language Processing (NLP)

  • Introduction to NLTK
  • Various data terms for NLP
  • Data Seperation

Project:- Develop Your Personal Virtual Assistance like Google Assistant, Cortana and Siri

2 Hours
11Live Image Processing and ML

  • How image search is going to work
  • Taking pictures with python for image processing
  • Image through webcam
  • Face detection system
2 Hours
12Unsupervised Learning

  • Introduction to Neural Network(NN)
  • Types of NN
  • ANN (Artificial Neural Network)
  • Working with problems using ANN
  • ANN with real datasets

Face Recognition

  • Tensorflow
  • What is tensor and how does it work?
  • Working with tensorflow at backend
  • Introduction to Google Libraries and API’s
  • Exploring Features Engineering
  • Training face recognition model

Project:- Development of Live Face Recognition System

2 Hours
13Deep Learning for image search and Recognition with Cloud

  • Searching for image
  • Loading image with cloud library
  • Registering image for training model
  • Browsing image from url and local
  • Training image datasets
  • Recognition of different images to detect face
  • Deregistering images from cloud library

Project:- Develop an Image Classification System over the Cloud

2 Hours
14Face Classification and Expression Recognition

  • Static image models
  • Training images datasets with tensorflow
  • Person Detection
  • Searching for ROI of an object
  • Realtime Face Classifications

Project:- Development of SMART Face Classification System

2 Hours
15Objects Classifications

  • Installing required libraries
  • Tensorflow for various objects
  • Live Camera detections
  • Objects available in images
  • Classify the objects

Project:- Advanced Live Objects Classification System

2 Hours
30 Days Training
Course:Machine Learning
Certification By:TechieNest, An ISO 9001:2008 Certified Company
Study Material:Software and PDFs provided to each student.
Trainer Student Ratio:1:20

Course Details


Machine Learning
SESSIONTOPICDURATION
1

Machine Learning

  • Introduction to Machine Learning.
  • Understanding the need.
  • Understanding Big data and machine learning.
  • Running machine learning under linux platform.
  • Introduction to Redhat Enterprise linux.
  • Why linux is important for machine learning with respect to future.
  • Role of Python and R programming in this domain.
  • Basic Introduction of Python syntax and programming logics.
  • Deep dive with Supervised , Unsupervised and Reinforcement learning
  • Algo discussion with use case.
  • Popular machine learning framework like tensorflow , scikit-learn.
2 Hours
2

Python Programming

  • Basic of python and why python for machine learning
  • Installation of software on different OS.
  • Understanding basic syntax with data types
  • List , dict , tuple , string
  • Extracting data from a file
  • Committing your code to GITHUB

2 Hours

3

More About Python Programming

  • Conditional statement and loops
  • Function and modules
  • File handling
  • Creating own modules / library
  • Web scraping with urllib2
  • Grabbing system information from Popen and os library
  • Scanning Network IP & MAC address with loops
2 Hours
4
  • Introduction to Ipython with jupyter notebook
  • Using jupyter notebook with Ipython & Python
  • UDP Socket programming
  • Exception & Signal handling
  • Making chat program with UDP socket
2 Hours
5
  • Extending chat programming
  • Introduction to pandas
  • Making dataframes with pandas
  • Handling xls & csv files with pandas
  • Loading and extracting existing xls files
2 Hours
6

Project 1- Online/offline SMART Chatting Machine like Whatsapp

2 Hours
7
  • Introduction to Numpy & Matplotlib
  • Managing arrary with numpy
  • Multidimensional array with numpy
  • Unit matrix handling & creating
  • Deleting indexes from matrix
  • Deep dive with Matplotlib
  • Drawing general purpose graphs
  • Graphs with mathematics
2 Hours
8

Working with Python for ML

  • Supervised Learning lab with Hello World Program
  • Running the installer and use case
  • Jupyter notebook for machine learning
  • Installation of jupyter notebook
  • DecisionTree classifier implementation
  • Practise lab with DecisionTree algo and number of examples
  • Making own algo with training data with python
2 Hours
9

Machine learning techniques

  • Types of learning
  • Advice of applying machine learning
  • Machine learning System Design
  • DecisionTree algo deep dive
  • Training your machine with real time datasets
  • Deep dive with UCI
  • Lab session for loading data from different apis
  • Detecting data from numpy and converting for training and testing data
  • Exercise with ML and others framework
2 Hours
10
  • Introduction to iris datasets
  • Understanding iris datasets
  • Modifying and loading with scikit-learn
  • Separating data with numpy
  • Training classifier
  • Algo data process view
  • Decision Tree understanding
2 Hours
11
  • Introduction to Boston house and pima-Indians diabetes datasets
  • Understanding diabetes datasets
  • Applying algorithm like Navie Bayes and SVM
  • Implementing graphs with availability
  • Deep dive with algo’s
2 Hours
12

Project 2: Make your personal virtual assistant like Google Assistant, Cortona and Siri.

2 Hours
13Supervised learning

  • Regression
  • Classification
  • Case study learning in regression
  • Case study learning in classification
  • Comparing the result of Decision Tree and Navie Bayes algo
  • Graphploting with Matplotlib for comparison
2 Hours
14Supervised Advanced

  • Recommendations
  • Deep learning vs Machine learning
  • Case study in clustering and measuring
  • SVM lab
  • Introduction to cloud computing for Machine learning
2 Hours
15Deep Learning for image search and Recognition

  • Searching for image
  • Loading image with cloud library
  • Registering image for training model
  • Browsing image from url and local
  • Training image datasets
  • Recognition of different images to detect face
  • Deregistering images from cloud library
  • Pushing code to github for automatic updates
2 Hours
16Live Image Processing and ML

  • How image search is going to work
  • Taking pictures with python for image processing
  • Loading and registering images
  • Face detection with android sdk
  • Project: SMLS 1.O (Smart Machine Learning System)
2 Hours
17
  • Executing containers for gpu
  • Introduction to openAI
  • Introduction to gym and universe
  • Loading flash games
  • Pygames implementation with containers
  • Real time games playing with gym and universe
2 Hours
18
  • Data summarization
  • Data summarization understanding
  • Writing program for data summarization
  • Deep learning example for future data and breath
2 Hours
19
  • Search engine recommendation
  • Writing program for search engine recommendation
  • Making platform for email classification
  • Detecting spam or HAM in gmail SMTP
  • Connecting with mail.google.com with smtp
  • Read and verify mail category
2 Hours
20

Project 3: Face and expression recognition based Smart Music Player and Communication System using ML Algo’s

2 Hours
21
  • Machine learning with Amazon cloud
  • Image processing with amazon cloud
  • Introduction to Reinforcement learning
  • An example implementation of reinforcement learning
2 Hours
22Neural Networks analysis

  • Understanding neural networks
  • Data learning and machine predictions
  • Neural networks real understanding
  • Neural network implementation with real datasets
2 Hours
23

Project 4: Development of your own JARVIS (An Computerised Intelligent System)

2 Hours
24

Project Completion & Presentation

2 Hours
25Query Session
Certificate Distribution
2 Hours

Step 1

Register online for any desired course, duration & location of your training course & obtain a Registration-ID. Registration-ID is a Unique Registration Number which is generated by our system after successful registration for training A student can have multiple IDs for multiple courses & batches. It is displayed while successful registration and it is also mailed to you immediately after registration by our server. if you don’t find it in your mail then, please check your SPAM folder or junk folder of your mail ID.

Step 2

Please deposit your Course fee to any one of our payment gateway/ Bank Account/ paytm.

Payment Gateway link: Pay through PayUmoney

Bank Account Details
A/C Name: TechieNest Pvt. Ltd.
Bank A/C No: 201000689491
IFSC: INDB0000592
Bank Name: Indusind Bank Limited
Address: Malviya Nagar, Jaipur ( Rajasthan)
Paytm Number9251494002

Step 3

Update us regarding your fee payment by sending picture/scan copy of bank receipt to: training@techienest.in and you will receive a confirmation mail on your mail id.

SUMMER TRAINING OFFERS:

When someone says yes you can do it….it means you can achieve it and when you decide to take an action we come with the surprising offers:

1) Branch toppers up to 30% off

Offer code: TNBT30

For Branch toppers we have the superb fascinating offer 30% off on summer training Courses.

T&C apply:

  • This offer can be redeemed by only two candidates i.e. 1 male and 1 female candidate.
  • Certification proof is mandatory for the validation of branch toppers.

E.g.: Photo-print of Result or Provisional Mark sheet from University

2) On the basis of 12th result, upto Rs. 2000/- off

Offer code: TN12-2000

Upto Rs. 2000/- off will be awarded in all the courses offered in summer training to those Students who had scored more than 85% in 12th

T&C apply:

  • Certification proof of 12th is mandatory to validate the percentage.

3) Group Discount:

Offer code: TNGD-10

Offer code: TNGD-20

Offer code: TNGD-25

  • If a group size is of: 5 -10 then 10% discount on training

10-20 then 20% discount on training

20 and above then 25% discount on training

4) Referral Offer:

Offer code: TNR3

Offer code: TNR5

  • 3% additional discount to the person who is referring
  • 5% additional discount to the one who is being referred

5) For Former students up to 30% off:

Offer code: TNFS30

  • There will be upto 30% discount on students who already did training

6) Nobel Cause students Rs. 1000/- off:

Offer code: TNNC1000

  • Discount of Rs. 1000/- will be given to students who has worked for Noble cause and proof of all certifications related to that span of work are need to be shown.

7) 5% additional Discount for Campus Ambassador:

Offer code: TNA5

  • Additional 5% discount will be given on training program

8) Previous Workshop attended students 10% off:

Offer code: TNPW10

  • For this category students discount of 10% on summer training

9) IIT, NIT students Rs. 2000/- off:

Offer code: TNIN2000

Students belongs to IIT and NIT will be getting off up to Rs. 2000/- on training programs.

NOTE- All the discounts are applicable to the courses whose course fee worth above Rs 5500/-.

Certification

All participants will get Certificate from TechieNest Pvt. Ltd. in association with Aavriti’18 IIT Bombay

Why TechieNest

  • Vast experience of having conducted Big Outreach Workshop collaborating with over 300+ colleges in all over India including IIT Bombay, IIT Hyderabad, IIT Bhubaneswar, IIT Jodhpur, IIT Mandi, NIT Raipur, MNIT Jaipur, MANIT Bhopal, NIT Jalandhar, NIT Patna, NIT Srinagar, IIIT Kalyani, BITS Pilani and likewise.
  • Trained more than 20,000 students in the field of EMBEDDED SYSTEMS & ROBOTICS, MATLAB & Machine Vision, Internet of Things, PLC_SCADA, PYTHON, C/C++, Andriod, VLSI & VHDL, JAVA and such top notch courses.
  • Our trainers are efficient in Raspberry pi, Arduino, PLCs, etc. which forms essential hardware in Electronic Industries nowadays.
  • Outreach workshop partner of Sanchaar-Wissenaire’18, IIT Bhubaneswar, 2017-18
  • Zonal workshop partner of Techkriti’18 IIT Kanpur, 2017-2018
  • Outreach workshop partner of Techfest’15 IIT Bombay & Techfest’16 IIT Bombay
  • Zonal workshop partner of Techkriti’17 IIT Kanpur, 2016-2017
  • Outreach workshop & Training partner of nVision’17 IIT Hyderabad, 2016-17
  • Outreach workshop partner of Ignus’17 IIT Jodhpur, 2016-17
  • AIRC’18 (All India Robotics Championship) in association with Techkriti’18 IIT Kanpur.
  • AIRC’17 (All India Robotics Championship) in association with nVision’17 IIT Hyderabad, 2016-17
  • Offering Project Based Training, Projects on Demand, Corporate Projects, Commercial Projects, and Consultancy in Engineering Projects.
    Dedicated 24×7 R&D lab.
  • Trained over 50+ international students in TechieNest Technology Transfer Program 2014-15.
  • TechieNest has Research Engineers having excellent research aptitude, teaching pedagogy who illustrates their finding through practical demos during workshop/training.
  • Manufacturer of Electronic products delivering the same across the country.
  • Raspberry Pi 3 B
  • Class 10 memory card
  • LAN cable
  • Power cable
  • LEDs
  •  Motor section
  •  LCD
  • Motors
  • IR sensor
  • Ultrasonic Sensor
  • Temperature sensor
  • Relay Board
  • Connecting wires
Course
15 Days Training
Course:Machine Learning
Certification By:TechieNest, An ISO 9001:2008 Certified Company
Study Material:Software and PDFs provided to each student.
Trainer Student Ratio:1:20

Course Details


Machine Learning
SESSIONTOPICDURATION
1

Machine Learning

  • Introduction to Machine Learning.
  • Understanding the need.
  • Understanding Big data and machine learning.
  • Running machine learning under linux platform.
  • Introduction to Redhat Enterprise linux.
  • Why linux is important for machine learning with respect to future.
  • Role of Python and R programming in this domain.
  • Basic Introduction of Python syntax and programming logics.
  • Deep dive with Supervised , Unsupervised and Reinforcement learning
  • Algo discussion with use case.
  • Popular machine learning framework like tensorflow , scikit-learn.
2 Hours
2

Python Programming

  • Basic of python and why python for machine learning
  • Installation of software on different OS.
  • Understanding basic syntax with data types
  • List , dict , tuple , string
  • Extracting data from a file
  • Committing your code to GITHUB

2 Hours

3

More About Python Programming

  • Conditional statement and loops
  • Function and modules
  • File handling
  • Creating own modules / library
  • Web scraping with urllib2
  • Grabbing system information from Popen and os library
  • Scanning Network IP & MAC address with loops
2 Hours
4Data Science (A Beginning for New Field of Science )

  • Introduction to Numpy & Matplotlib
  • Managing arrary with numpy
  • Multidimensional array with numpy
  • Unit matrix handling & creating
  • Deleting indexes from matrix
  • Deep dive with Matplotlib
  • Drawing general purpose graphs
  • Graphs with mathematics

Project :- Real Datasets Analysis System

2 Hours
5Pandas

  • Data file formats
  • Data import and export using pandas
  • Exploring about dataframes
  • Real data calculations
  • Working with Graphs on real data
  • Data Analysis

OpenCV

  • Working with images
  • Images conversions
  • Transformation and morphological operations
  • Filters and ROI
  • Working with images and Numpy

Project :- Color Object Detection system (An intro to virtual reality)

2 Hours
6Working with Python for ML

  • Supervised Learning lab with Hello World Program
  • Running the installer and use case
  • Jupyter notebook for machine learning
  • Installation of jupyter notebook
  • DecisionTree classifier implementation
  • Practise lab with DecisionTree algo and number of examples
  • Making own algo with training data with python
2 Hours
7Machine learning techniques

  • Types of learning
  • Advice of applying machine learning
  • Machine learning System Design
  • DecisionTree algo deep dive
  • Training your machine with real time datasets
  • Deep dive with UCI
  • Lab session for loading data from different apis
  • Detecting data from numpy and converting for training and testing data
  • Exercise with ML and others framework
  • Introduction to iris datasets
  • Understanding iris datasets
  • Modifying and loading with scikit-learn
  • Training classifier
  • Algo data process view
2 Hours
8ML Continued with real datasets.

  • Introduction to boston house and pima-indians diabetes datasets
  • Understanding diabetes datasets
  • Applying alogrigthm like Navie Bayes and SVM
  • Implementing graphs with availability
  • Deep dive with algo’s
2 Hours
9Supervised Learning

  • Regression
  • Classification
  • Case study learning in regression
  • Case study learning in classification
  • Comparing the result of DecisionTree and Navie bayes algo
  • Graphploting with Matplotlib for comparison
2 Hours
10Google API for Speech Recognition

  • Understanding Voice Samples
  • Converting voice to text
  • Basic Systems requirements
  • Live Exceptions Handelling
  • Cloud Comparisions and Data Retrival

Natural Language Processing (NLP)

  • Introduction to NLTK
  • Various data terms for NLP
  • Data Seperation

Project:- Develop Your Personal Virtual Assistance like Google Assistant, Cortana and Siri

2 Hours
11Live Image Processing and ML

  • How image search is going to work
  • Taking pictures with python for image processing
  • Image through webcam
  • Face detection system
2 Hours
12Unsupervised Learning

  • Introduction to Neural Network(NN)
  • Types of NN
  • ANN (Artificial Neural Network)
  • Working with problems using ANN
  • ANN with real datasets

Face Recognition

  • Tensorflow
  • What is tensor and how does it work?
  • Working with tensorflow at backend
  • Introduction to Google Libraries and API’s
  • Exploring Features Engineering
  • Training face recognition model

Project:- Development of Live Face Recognition System

2 Hours
13Deep Learning for image search and Recognition with Cloud

  • Searching for image
  • Loading image with cloud library
  • Registering image for training model
  • Browsing image from url and local
  • Training image datasets
  • Recognition of different images to detect face
  • Deregistering images from cloud library

Project:- Develop an Image Classification System over the Cloud

2 Hours
14Face Classification and Expression Recognition

  • Static image models
  • Training images datasets with tensorflow
  • Person Detection
  • Searching for ROI of an object
  • Realtime Face Classifications

Project:- Development of SMART Face Classification System

2 Hours
15Objects Classifications

  • Installing required libraries
  • Tensorflow for various objects
  • Live Camera detections
  • Objects available in images
  • Classify the objects

Project:- Advanced Live Objects Classification System

2 Hours
30 Days Training
Course:Machine Learning
Certification By:TechieNest, An ISO 9001:2008 Certified Company
Study Material:Software and PDFs provided to each student.
Trainer Student Ratio:1:20

Course Details


Machine Learning
SESSIONTOPICDURATION
1

Machine Learning

  • Introduction to Machine Learning.
  • Understanding the need.
  • Understanding Big data and machine learning.
  • Running machine learning under linux platform.
  • Introduction to Redhat Enterprise linux.
  • Why linux is important for machine learning with respect to future.
  • Role of Python and R programming in this domain.
  • Basic Introduction of Python syntax and programming logics.
  • Deep dive with Supervised , Unsupervised and Reinforcement learning
  • Algo discussion with use case.
  • Popular machine learning framework like tensorflow , scikit-learn.
2 Hours
2

Python Programming

  • Basic of python and why python for machine learning
  • Installation of software on different OS.
  • Understanding basic syntax with data types
  • List , dict , tuple , string
  • Extracting data from a file
  • Committing your code to GITHUB

2 Hours

3

More About Python Programming

  • Conditional statement and loops
  • Function and modules
  • File handling
  • Creating own modules / library
  • Web scraping with urllib2
  • Grabbing system information from Popen and os library
  • Scanning Network IP & MAC address with loops
2 Hours
4
  • Introduction to Ipython with jupyter notebook
  • Using jupyter notebook with Ipython & Python
  • UDP Socket programming
  • Exception & Signal handling
  • Making chat program with UDP socket
2 Hours
5
  • Extending chat programming
  • Introduction to pandas
  • Making dataframes with pandas
  • Handling xls & csv files with pandas
  • Loading and extracting existing xls files
2 Hours
6

Project 1- Online/offline SMART Chatting Machine like Whatsapp

2 Hours
7
  • Introduction to Numpy & Matplotlib
  • Managing arrary with numpy
  • Multidimensional array with numpy
  • Unit matrix handling & creating
  • Deleting indexes from matrix
  • Deep dive with Matplotlib
  • Drawing general purpose graphs
  • Graphs with mathematics
2 Hours
8

Working with Python for ML

  • Supervised Learning lab with Hello World Program
  • Running the installer and use case
  • Jupyter notebook for machine learning
  • Installation of jupyter notebook
  • DecisionTree classifier implementation
  • Practise lab with DecisionTree algo and number of examples
  • Making own algo with training data with python
2 Hours
9

Machine learning techniques

  • Types of learning
  • Advice of applying machine learning
  • Machine learning System Design
  • DecisionTree algo deep dive
  • Training your machine with real time datasets
  • Deep dive with UCI
  • Lab session for loading data from different apis
  • Detecting data from numpy and converting for training and testing data
  • Exercise with ML and others framework
2 Hours
10
  • Introduction to iris datasets
  • Understanding iris datasets
  • Modifying and loading with scikit-learn
  • Separating data with numpy
  • Training classifier
  • Algo data process view
  • Decision Tree understanding
2 Hours
11
  • Introduction to Boston house and pima-Indians diabetes datasets
  • Understanding diabetes datasets
  • Applying algorithm like Navie Bayes and SVM
  • Implementing graphs with availability
  • Deep dive with algo’s
2 Hours
12

Project 2: Make your personal virtual assistant like Google Assistant, Cortona and Siri.

2 Hours
13Supervised learning

  • Regression
  • Classification
  • Case study learning in regression
  • Case study learning in classification
  • Comparing the result of Decision Tree and Navie Bayes algo
  • Graphploting with Matplotlib for comparison
2 Hours
14Supervised Advanced

  • Recommendations
  • Deep learning vs Machine learning
  • Case study in clustering and measuring
  • SVM lab
  • Introduction to cloud computing for Machine learning
2 Hours
15Deep Learning for image search and Recognition

  • Searching for image
  • Loading image with cloud library
  • Registering image for training model
  • Browsing image from url and local
  • Training image datasets
  • Recognition of different images to detect face
  • Deregistering images from cloud library
  • Pushing code to github for automatic updates
2 Hours
16Live Image Processing and ML

  • How image search is going to work
  • Taking pictures with python for image processing
  • Loading and registering images
  • Face detection with android sdk
  • Project: SMLS 1.O (Smart Machine Learning System)
2 Hours
17
  • Executing containers for gpu
  • Introduction to openAI
  • Introduction to gym and universe
  • Loading flash games
  • Pygames implementation with containers
  • Real time games playing with gym and universe
2 Hours
18
  • Data summarization
  • Data summarization understanding
  • Writing program for data summarization
  • Deep learning example for future data and breath
2 Hours
19
  • Search engine recommendation
  • Writing program for search engine recommendation
  • Making platform for email classification
  • Detecting spam or HAM in gmail SMTP
  • Connecting with mail.google.com with smtp
  • Read and verify mail category
2 Hours
20

Project 3: Face and expression recognition based Smart Music Player and Communication System using ML Algo’s

2 Hours
21
  • Machine learning with Amazon cloud
  • Image processing with amazon cloud
  • Introduction to Reinforcement learning
  • An example implementation of reinforcement learning
2 Hours
22Neural Networks analysis

  • Understanding neural networks
  • Data learning and machine predictions
  • Neural networks real understanding
  • Neural network implementation with real datasets
2 Hours
23

Project 4: Development of your own JARVIS (An Computerised Intelligent System)

2 Hours
24

Project Completion & Presentation

2 Hours
25Query Session
Certificate Distribution
2 Hours
How to Enroll

Step 1

Register online for any desired course, duration & location of your training course & obtain a Registration-ID. Registration-ID is a Unique Registration Number which is generated by our system after successful registration for training A student can have multiple IDs for multiple courses & batches. It is displayed while successful registration and it is also mailed to you immediately after registration by our server. if you don’t find it in your mail then, please check your SPAM folder or junk folder of your mail ID.

Step 2

Please deposit your Course fee to any one of our payment gateway/ Bank Account/ paytm.

Payment Gateway link: Pay through PayUmoney

Bank Account Details
A/C Name: TechieNest Pvt. Ltd.
Bank A/C No: 201000689491
IFSC: INDB0000592
Bank Name: Indusind Bank Limited
Address: Malviya Nagar, Jaipur ( Rajasthan)
Paytm Number9251494002

Step 3

Update us regarding your fee payment by sending picture/scan copy of bank receipt to: training@techienest.in and you will receive a confirmation mail on your mail id.

Fee & Discount

SUMMER TRAINING OFFERS:

When someone says yes you can do it….it means you can achieve it and when you decide to take an action we come with the surprising offers:

1) Branch toppers up to 30% off

Offer code: TNBT30

For Branch toppers we have the superb fascinating offer 30% off on summer training Courses.

T&C apply:

  • This offer can be redeemed by only two candidates i.e. 1 male and 1 female candidate.
  • Certification proof is mandatory for the validation of branch toppers.

E.g.: Photo-print of Result or Provisional Mark sheet from University

2) On the basis of 12th result, upto Rs. 2000/- off

Offer code: TN12-2000

Upto Rs. 2000/- off will be awarded in all the courses offered in summer training to those Students who had scored more than 85% in 12th

T&C apply:

  • Certification proof of 12th is mandatory to validate the percentage.

3) Group Discount:

Offer code: TNGD-10

Offer code: TNGD-20

Offer code: TNGD-25

  • If a group size is of: 5 -10 then 10% discount on training

10-20 then 20% discount on training

20 and above then 25% discount on training

4) Referral Offer:

Offer code: TNR3

Offer code: TNR5

  • 3% additional discount to the person who is referring
  • 5% additional discount to the one who is being referred

5) For Former students up to 30% off:

Offer code: TNFS30

  • There will be upto 30% discount on students who already did training

6) Nobel Cause students Rs. 1000/- off:

Offer code: TNNC1000

  • Discount of Rs. 1000/- will be given to students who has worked for Noble cause and proof of all certifications related to that span of work are need to be shown.

7) 5% additional Discount for Campus Ambassador:

Offer code: TNA5

  • Additional 5% discount will be given on training program

8) Previous Workshop attended students 10% off:

Offer code: TNPW10

  • For this category students discount of 10% on summer training

9) IIT, NIT students Rs. 2000/- off:

Offer code: TNIN2000

Students belongs to IIT and NIT will be getting off up to Rs. 2000/- on training programs.

NOTE- All the discounts are applicable to the courses whose course fee worth above Rs 5500/-.

Certification

Certification

All participants will get Certificate from TechieNest Pvt. Ltd. in association with Aavriti’18 IIT Bombay

Why TechieNest

  • Vast experience of having conducted Big Outreach Workshop collaborating with over 300+ colleges in all over India including IIT Bombay, IIT Hyderabad, IIT Bhubaneswar, IIT Jodhpur, IIT Mandi, NIT Raipur, MNIT Jaipur, MANIT Bhopal, NIT Jalandhar, NIT Patna, NIT Srinagar, IIIT Kalyani, BITS Pilani and likewise.
  • Trained more than 20,000 students in the field of EMBEDDED SYSTEMS & ROBOTICS, MATLAB & Machine Vision, Internet of Things, PLC_SCADA, PYTHON, C/C++, Andriod, VLSI & VHDL, JAVA and such top notch courses.
  • Our trainers are efficient in Raspberry pi, Arduino, PLCs, etc. which forms essential hardware in Electronic Industries nowadays.
  • Outreach workshop partner of Sanchaar-Wissenaire’18, IIT Bhubaneswar, 2017-18
  • Zonal workshop partner of Techkriti’18 IIT Kanpur, 2017-2018
  • Outreach workshop partner of Techfest’15 IIT Bombay & Techfest’16 IIT Bombay
  • Zonal workshop partner of Techkriti’17 IIT Kanpur, 2016-2017
  • Outreach workshop & Training partner of nVision’17 IIT Hyderabad, 2016-17
  • Outreach workshop partner of Ignus’17 IIT Jodhpur, 2016-17
  • AIRC’18 (All India Robotics Championship) in association with Techkriti’18 IIT Kanpur.
  • AIRC’17 (All India Robotics Championship) in association with nVision’17 IIT Hyderabad, 2016-17
  • Offering Project Based Training, Projects on Demand, Corporate Projects, Commercial Projects, and Consultancy in Engineering Projects.
    Dedicated 24×7 R&D lab.
  • Trained over 50+ international students in TechieNest Technology Transfer Program 2014-15.
  • TechieNest has Research Engineers having excellent research aptitude, teaching pedagogy who illustrates their finding through practical demos during workshop/training.
  • Manufacturer of Electronic products delivering the same across the country.
Training Kit
  • Raspberry Pi 3 B
  • Class 10 memory card
  • LAN cable
  • Power cable
  • LEDs
  •  Motor section
  •  LCD
  • Motors
  • IR sensor
  • Ultrasonic Sensor
  • Temperature sensor
  • Relay Board
  • Connecting wires
Center

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Contact us

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+91-7340033094
Hyderabad
+91-9251094002
Raipur
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Email ID
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