Executive PG Certificate Program in AI (Machine Learning and Deep Learning) by IIT Roorkee

Certificate

What is the certificate like?

Course Completion Certificate - It is obtained by meeting the minimum certificate criteria

Certificate with Statement of Marks - It is obtained by clearing all exams as per the guidelines mentioned in this document. The examination fee has to be paid by the candidate only if he/she is opting for a Certificate with a statement of marks. (Under Process, Guidelines will be posted soon)

Course Completion Certificate - It is obtained by meeting the minimum certificate criteria

Why IIT Roorkee?

IIT Roorkee is ranked first among all the IITs AND 20th position globally in citations per faculty. Established in 1847, it's one of the oldest technical institutions in Asia. IIT Roorkee fosters a very strong entrepreneurial culture. Some of their alumni are highly successful as entrepreneurs in the new age digital economy.
Why CloudxLab?

CloudxLab is a team of developers, engineers, and educators passionate about building innovative products to make learning fun, engaging, and for life. We are a highly motivated team who build fresh and lasting learning experiences for our users. Powered by our innovation processes, we provide a gamified environment where learning is fun and constructive. From creative design to intuitive apps we create a seamless learning experience for our users. We upskill engineers in deep tech - make them employable & future-ready.

#1^st

Among the IITs in the ‘Citations per Faculty’ parameter

*QS World Rankings

#5

Ranked Engineering College

*India Today 2020

#6

Ranked for IITs

*NIRF 2020

#12

Ranked Best Global Universities in India

*QS World Rankings

Programming Languages and Tools

Hands-on Learning

Gamified Learning Platform
Making learning fun and sustainable

Auto-assessment Tests
Learn by writing code and executing it on lab

No Installation Required
Lab comes pre-installed softwares and accessible everywhere

Mentors / Faculty

Raksha Sharma

Faculty CSE Dept

IIT Roorkee

Sanjeev Manhas

Faculty ECE Dept

IIT Roorkee

Venkat Karun

Staff Software Engineer

Google

Dr. M.L. Virdi

Senior Research Scientist

NASA

Sandeep Giri

Founder at CloudxLab

Past: Amazon, InMobi, D.E.Shaw

Abhinav Singh

Co-Founder at CloudxLab

Past: Byjus

Praveen Pavithran

Co-Founder at Yatis

Past: YourCabs, Cypress Semiconductor

Curriculum

1. Programming Tools and Foundational Concepts: 1. Linux for Data Science

2. Getting Started with Git

3. Python Foundations

4. Machine Learning Prerequisites(Including Numpy, Pandas and Linear Algebra)

5. Getting Started with SQL

6. Statistics Foundations
1. Machine Learning Applications & Landscape: 1. Introduction to Machine Learning

2. Machine Learning Application

3. Introduction to AI

4. Different types of Machine Learning - Supervised, Unsupervised
2. Building end-to-end Machine Learning Project: 1. Machine Learning Projects Checklist

2. Get the data

3. Launch, monitor, and maintain the system

4. Explore the data to gain insights

5. Prepare the data for Machine Learning algorithms

6. Explore many different models and short-list the best ones

7. Fine-tune model
3. Classification: 1. Training a Binary classification

2. Multiclass,Multilabel and Multioutput Classification

3. Performance Measures

4. Confusion Matrix

5. Precision and Recall

6. Precision/Recall Tradeoff

7. The ROC Curve
4. Training Models: 1. Linear Regression

2. Gradient Descent

3. Polynomial Regression

4. Learning Curves

5. Regularized Linear Models

6. Logistic Regression
5. Support Vector Machines: 1. Linear SVM Classification

2. Nonlinear SVM Classification

3. SVM Regression
6. Decision Trees: 1. Training and Visualizing a Decision Tree

2. Making Predictions

3. Estimating Class Probabilities

4. The CART Training Algorithm

5. Gini Impurity or Entropy

6. Regularization Hyperparameters

7. Instability
7. Ensemble Learning and Random Forests: 1. Voting Classifiers

2. Bagging and Pasting

3. Random Patches and Random Subspaces

4. Random Forests

5. Boosting and Stacking
8. Dimensionality Reduction: 1. The Curse of Dimensionality

2. Main Approaches for Dimensionality Reduction

3. PCA

4. Kernel PCA

5. LLE

6. Other Dimensionality Reduction Techniques
1. Introduction to Artificial Neural Networks: 1. From Biological to Artificial Neurons

2. Implementing MLPs using Keras with TensorFlow Backend

3. Fine-Tuning Neural Network Hyperparameters
2. Training Deep Neural Networks: 1. The Vanishing / Exploding Gradients Problems

2. Reusing Pretrained Layers

3. Faster Optimizers

4. Avoiding Overfitting Through Regularization

5. Practical Guidelines to Train Deep Neural Networks
3. Custom Models and Training with Tensorflow: 1. A Quick Tour of TensorFlow

2. Customizing Models and Training Algorithms

3. Tensorflow Functions and Graphs
4. Loading and Preprocessing Data with TensorFlow: 1. Introduction to the Data API

2. TFRecord Format

3. Preprocessing the Input Features

4. TF Transform

5. The TensorFlow Datasets (TDFS) Projects
5. Convolutional Neural Networks: 1. The Architecture of the Visual Cortex

2. Convolutional Layer

3. Pooling Layer

4. CNN Architectures

5. Classification with Keras

6. Transfer Learning with Keras

7. Object Detection

8. YOLO
6. Recurrent Neural Networks: 1. Recurrent Neurons and Layers

2. Basic RNNs in TensorFlow

3. Training RNNs

4. Deep RNNs

5. Forecasting a Time Series

6. LSTM Cell

7. GRU Cell
7. Natural Language Processing: 1. Introduction to Natural Language Processing

2. Creating a Quiz Using TextBlob

3. Finding Related Posts with scikit-learn

4. Generating Shakespearean Text Using Character RNN

5. Sentiment Analysis

6. Encoder-Decoder Network for Neural Machine Translation

7. Attention Mechanisms

8. Recent Innovations in Language Models
8. Autoencoders and GANs: 1. Efficient Data Representations

2. Performing PCA with an Under Complete Linear Autoencoder

3. Stacked Autoencoders

4. Unsupervised Pre Training Using Stacked Autoencoders

5. Denoising Autoencoders

6. Sparse Autoencoders

7. Variational Autoencoders

8. Generative Adversarial Networks
9. Reinforcement Learning: 1. Learning to Optimize Rewards

2. Policy Search

3. Introduction to OpenAI Gym

4. Neural Network Policies

5. Evaluating Actions: The Credit Assignment Problem

6. Policy Gradients

7. Markov Decision Processes

8. Temporal Difference Learning and Q-Learning

9. Deep Q-Learning Variants

10. The TF-Agents Library

12+

Months of Blended Training

365

Days of Lab Access

25+

Projects

13K+

Learners

Download Curriculum

Projects

Project 13.
Mask R-CNN with OpenCV for Object Detection

Learn how to read a pre-trained TensorFlow model for object detection using OpenCV.

Project 14.
Image Classification with Pre-trained Keras models

Learn how to access the pre-trained models(here we get pre-trained ResNet model) from Keras of TensorFlow 2 to classify images.

Project 15.
Build cats classifier using transfer learning

Build a basic neural network to classify if a given image is of cat or not using transfer learning technique with Python and Keras.

Project 16.
Art Generation Project

Use TensorFlow 2 to generate an image that is an artistic blend of a content image and style image using Neural Style Transfer.

Project 17.
Credit Card Fraud Detection using Machine Learning

Learn how to over-sample the dataset with imbalanced classes using the SMOTE technique and how to use the thus obtained data to build a fraudulent tra ...Read more

Project 18.
Image Stitching using OpenCV and Python (Creating Panorama Project)

As you know, the Google photos app has stunning automatic features like video making, panorama stitching, collage making, and many more. In this proje ...Read more

Project 19.
NYSE Stock Closing Price Prediction using TensorFlow 2

Predict stock market closing prices for a firm using GRU, a state-of-art deep learning algorithm for sequential data, with Keras and Python.

Project 20.
Introduction to Transfer Learning (Cat vs Non-cats Project)

Apply the idea of Transfer Learning to build an image classifier with Tensorflow2, and use it to predict the class of an input image - whether it is a ...Read more

Project 21.
Building Cat vs Non-Cat Image Classifier using NumPy

Use Python and Numpy to build a Logistic Regression Classifier from scratch, and apply it to predict the class of an input image - whether it is a cat ...Read more

Project 22.
Iris Flowers Classification using Deep Learning & Keras

Use Python and Tensorflow 2 Keras to build a dense deep neural network classifier to predict the classes of flowers in the Iris dataset.

Project 23.
Classify Clothes from Fashion MNIST Dataset

Build a model to classify clothes into various categories in Fashion MNIST dataset.

Project 24.
Face recognition

Identify person from digital image or video.

Project 25.
Game - OpenGym

Project using OpenGym on Reinforcement Learning.

Project 1.
Churn Email Inbox with Python

Churn the mail activity from various individuals in an open source project development team.

Project 2.
Predicting the median housing prices in California

In this project we will build a machine learning model to predict housing prices. We will learn various data manipulation, visualization and cleaning ...Read more

Project 3.
Classifying handwritten digits in MNIST dataset

The MNIST dataset is considered as Hello World! of Machine Learning. Write your first classification logic. Starting with Binary Classification learn ...Read more

Project 4.
Noise removal from the images

Build a model that takes a noisy image as an input and outputs the clean image.

Project 5.
Build a spam classifier

Build a model to classify email as spam or ham. First, download examples of spam and ham from Apache SpamAssassin’s public datasets and then train a m ...Read more

Project 6.
Predict which passengers survived in the Titanic shipwreck

The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. In this project, you build a model to predict which passengers survi ...Read more

Project 7.
Image Classification with Pre-trained InceptionV3 Network

This project aims to impart the knowledge of how to access the pre-trained models from TensorFlow 2, and appreciate its powerful classification capaci ...Read more

Project 8.
Host an Image Classification App on Heroku

Heroku is a cloud platform for the deployment and management purposes of web applications. You will learn how to deploy DeepLearning based Flask web a ...Read more

Project 9.
Predicting Noisy Images using KNN Classifier

We will train a KNN classifier to predict MNIST images from their noisy version.

Project 10.
Build an Image Classifier in Fashion MNIST dataset

Build a CNN from scratch to classify FashionMNIST data using Tensorflow2, Matplotlib and Python.

Project 11.
Deploy Machine Learning models to Production using Flask

Learn how to deploy a machine learning model as a web application using the Flask framework.

Project 12.
Sentiment Analysis using IMDB dataset

Create a sentiment analysis model with the IMDB dataset using TensorFlow 2.

Project 13.
Mask R-CNN with OpenCV for Object Detection

Learn how to read a pre-trained TensorFlow model for object detection using OpenCV.

Project 14.
Image Classification with Pre-trained Keras models

Learn how to access the pre-trained models(here we get pre-trained ResNet model) from Keras of TensorFlow 2 to classify images.

Project 15.
Build cats classifier using transfer learning

Build a basic neural network to classify if a given image is of cat or not using transfer learning technique with Python and Keras.

Project 16.
Art Generation Project

Use TensorFlow 2 to generate an image that is an artistic blend of a content image and style image using Neural Style Transfer.

Project 17.
Credit Card Fraud Detection using Machine Learning

Learn how to over-sample the dataset with imbalanced classes using the SMOTE technique and how to use the thus obtained data to build a fraudulent tra ...Read more

Project 18.
Image Stitching using OpenCV and Python (Creating Panorama Project)

As you know, the Google photos app has stunning automatic features like video making, panorama stitching, collage making, and many more. In this proje ...Read more

Project 19.
NYSE Stock Closing Price Prediction using TensorFlow 2

Predict stock market closing prices for a firm using GRU, a state-of-art deep learning algorithm for sequential data, with Keras and Python.

Project 20.
Introduction to Transfer Learning (Cat vs Non-cats Project)

Apply the idea of Transfer Learning to build an image classifier with Tensorflow2, and use it to predict the class of an input image - whether it is a ...Read more

Project 21.
Building Cat vs Non-Cat Image Classifier using NumPy

Use Python and Numpy to build a Logistic Regression Classifier from scratch, and apply it to predict the class of an input image - whether it is a cat ...Read more

Project 22.
Iris Flowers Classification using Deep Learning & Keras

Use Python and Tensorflow 2 Keras to build a dense deep neural network classifier to predict the classes of flowers in the Iris dataset.

Project 23.
Classify Clothes from Fashion MNIST Dataset

Build a model to classify clothes into various categories in Fashion MNIST dataset.

Project 24.
Face recognition

Identify person from digital image or video.

Project 25.
Game - OpenGym

Project using OpenGym on Reinforcement Learning.

Project 1.
Churn Email Inbox with Python

Churn the mail activity from various individuals in an open source project development team.

Project 2.
Predicting the median housing prices in California

In this project we will build a machine learning model to predict housing prices. We will learn various data manipulation, visualization and cleaning ...Read more

Project 3.
Classifying handwritten digits in MNIST dataset

The MNIST dataset is considered as Hello World! of Machine Learning. Write your first classification logic. Starting with Binary Classification learn ...Read more

Project 4.
Noise removal from the images

Build a model that takes a noisy image as an input and outputs the clean image.

Project 5.
Build a spam classifier

Build a model to classify email as spam or ham. First, download examples of spam and ham from Apache SpamAssassin’s public datasets and then train a m ...Read more

Project 6.
Predict which passengers survived in the Titanic shipwreck

The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. In this project, you build a model to predict which passengers survi ...Read more

Project 7.
Image Classification with Pre-trained InceptionV3 Network

This project aims to impart the knowledge of how to access the pre-trained models from TensorFlow 2, and appreciate its powerful classification capaci ...Read more

Project 8.
Host an Image Classification App on Heroku

Heroku is a cloud platform for the deployment and management purposes of web applications. You will learn how to deploy DeepLearning based Flask web a ...Read more

Project 9.
Predicting Noisy Images using KNN Classifier

We will train a KNN classifier to predict MNIST images from their noisy version.

Project 10.
Build an Image Classifier in Fashion MNIST dataset

Build a CNN from scratch to classify FashionMNIST data using Tensorflow2, Matplotlib and Python.

Project 11.
Deploy Machine Learning models to Production using Flask

Learn how to deploy a machine learning model as a web application using the Flask framework.

Project 12.
Sentiment Analysis using IMDB dataset

Create a sentiment analysis model with the IMDB dataset using TensorFlow 2.

Project 13.
Mask R-CNN with OpenCV for Object Detection

Learn how to read a pre-trained TensorFlow model for object detection using OpenCV.

Which Executive PG Certificate Program by IIT Roorkee should you choose?

Topics available	Executive PG Certificate Program in Data Science	Executive PG Certificate Program in Machine Learning Operations
Foundations (Python, Linux, Git, SQL, Analytics)
Machine Learning
Deep Learning Foundations - Artificial Neural Networks
Advanced Deep Learning
Business Analytics
Big Data (Hadoop & Spark)
DevOps Foundations - Deploy Model to Production
Advanced DevOps
	View Details	View Details

Apply Now

Application Process

Step 1. Submit the application form and SOP(Statement of Purpose)
Register by filling the application form. Admission Test in the next step will be immediately after filling the form.

Step 2. Admission Test
Online admission test of 60 minutes containing 20 multiple choice questions to assess your quantitative aptitude and programming skills.

Step 3. Personal Interview
Online personal interview for 45 minutes with a course mentor. The interviewer will ask questions based on your Statement of Purpose, work experience(if any) and your motivation and aspirations to join the course.

Step 4. Join The Program
If selected, the admission office will send the letter of acceptance. Submit the admission fees in due time to confirm the seat

Note: Admission test should be taken immediately after submitting the application using the link displayed post application submission.

Eligibility Criteria

1. Anybody in their final year of undergraduate degree or has completed their undergraduation is eligible to apply for the course
1. Must have studied Mathematics in 12th standard

Scholarships

1. 10% Scholarships are available for students, women from STEM background and unemployed
1. 5% Scholarship available for IIT Alumni and CloudxLab Alumni.

PS: Details to avail the scholarship will be sent post application-submission and only one scholarship applicable per learner

Certification Guideline

You will be required to have at least 60% attendance in live sessions, complete at least 75% of the course, 1 Capstone Project and 6 Guided projects - Analyse emails from Python, 3 mandatory projects from Machine Learning and 2 mandatory projects from Deep Learning. All the above requirements need to be met within the deadline of the course (12 Months) to be eligible for the certificate from IIT Roorkee

No Cost EMI at

399*/Month

Or Program Fee 4,999*

*Early Bird Offer

12 Months Programm
365 Days of Online Lab Access
24*7 Support
Application Deadline 08^th Jan'22
Certificate from IIT Roorkee

Sold Out » Apply for Next Batch»
(4 Seats Left)

Please note that there is an additional 5% off for one-time payment

Placement Assistance

1-1 mentoring with Industry Experts

You will have 1-1 mentoring sessions with industry experts.

Mock Interview Sessions

We will be conducting mock interview sessions which will help you prepare for the interviews

Placement Eligibility Test

We have around 300+ recruitment partners who will be interviewing you based on your performances in PET

Profile Building Sessions

Sessions will be conducted to guide you on creating the perfect resume and professional profile to get noticed by recruiters

Dedicated Job Portal

Opportunities from companies who approach us asking for our learner profiles will be posted on our job portal to providevisibility to your profile

Career Guidance Webinars

Career Guidance Webinars from seasoned industry experts

Testimonials

I have been using CloudxLab for a while now, and they are amazing! The best part about using CloudxLab is that you do not need to wait for someone to ...Read more

Rajtilak Bhattacharjee

This course is suitable for everyone. Me being a product manager had not done hands-on coding since quite some time. Python was completely new to me. ...Read more

Kamal Upadhyay

It has been a wonderful learning experience with CXL. This is one of the courses that will probably stay with me for a significant amount of time. The ...Read more

Pratik Sonthalia

This is one of the best-designed course, very informative and well paced. The killer feature of machine/deep learning coursed from CloudxLab is the li ...Read more

Dhyan Prem

Senior Software Developer at Decision Resources Group

Sessions were great, pace was also very good. Each of the steps were explained well multiple times to ensure everyone ...Read more

Nitin Nigam

Thanks a lot,it was great course! I'm happy that you lead in this path to AI/ML/DL.I hope to continue to collaborate with you in future.

Domenico Flioravanti

Thank you so much Sandeep for all your great sessions. It will help in our career a lot. Your session is very much explanatory and understandable. Kud ...Read more

Hemanta Lenka

I have been using CloudxLab for a while now, and they are amazing! The best part about using CloudxLab is that you do not need to wait for someone to ...Read more

Rajtilak Bhattacharjee

This course is suitable for everyone. Me being a product manager had not done hands-on coding since quite some time. Python was completely new to me. ...Read more

Kamal Upadhyay

It has been a wonderful learning experience with CXL. This is one of the courses that will probably stay with me for a significant amount of time. The ...Read more

Pratik Sonthalia

This is one of the best-designed course, very informative and well paced. The killer feature of machine/deep learning coursed from CloudxLab is the li ...Read more

Dhyan Prem

Senior Software Developer at Decision Resources Group

Sessions were great, pace was also very good. Each of the steps were explained well multiple times to ensure everyone ...Read more

Nitin Nigam

Thanks a lot,it was great course! I'm happy that you lead in this path to AI/ML/DL.I hope to continue to collaborate with you in future.

Domenico Flioravanti

Thank you so much Sandeep for all your great sessions. It will help in our career a lot. Your session is very much explanatory and understandable. Kud ...Read more

Hemanta Lenka

I have been using CloudxLab for a while now, and they are amazing! The best part about using CloudxLab is that you do not need to wait for someone to ...Read more

Rajtilak Bhattacharjee

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Executive PG Certificate Program in in Machine Learning Operations (MLOps) By IIT Roorkee

Executive PG Certificate Program in Machine Learning Operations (MLOps) By IIT Roorkee

12 Months Online Program

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Frequently Asked Questions

Will I get support?

Yes! Please feel free to ask your questions on CloudxLab forum and our community and team of experts will answer your questions. We believe forum will add better perspectives, ideas, and solutions to your questions.

Can I get a certificate for the projects completed?

We have created a set of Guided Projects on our platform. You may complete these guided projects and earn the certificate for free. Check it out here

Can I expect any placement support?

Yes, we do offer placement assistance that includes career guidance, resume building tips and mock interviews. Each participant will receive staunch support from the industry mentors, who also direct you through various placement opportunities within the industry. Above all, we are partnered with leading MNC’s that offer placement opportunities to our participants.

Do I need to install any software before starting this course?

No, we will provide you with the access to our online lab and BootML so that you do not have to install anything on your local machine

What is the validity of course material?

We understand that you might need course material for a longer duration to make most out of your subscription. You will get lifetime access to the course material so that you can refer to the course material anytime.

What if I miss a class?

You will never lose any lecture. You can view the recorded session of the class in your LMS.

What is the refund policy?

We provide a 100% fee refund if the request is raised within the first 2 instructor-led sessions. Please contact us at reachus@cloudxlab.com to request a refund within the stipulated time. Thereafter, no refund is provided.

I have some more questions. Can I talk to someone?

Absolutely! Please contact us here. You can also reach us anytime on our 24/7 support helpline by calling us on +918049202224

Will there be Options to Pay using EMI/Installments

Yes, you can choose to pay by installments on the payment page.

How will the payment for the course be made?

The course fee will be paid in two parts:

Admission fee - Which is a small percentage of the total course fee which helps to reserve your seat for the program
Installments - The remaining course fee will be paid in 11 Installments paid monthly for 11 months as part of No Cost EMI

If I have more questions during the week days apart from the live sessions, how can I get it cleared?

You will be able to get your queries cleared over WhatsApp, email, call and discussion forums on cloudxlab.

What all will be asked during the personal interview?

The Interviewer during the personal interview will be asking you questions regarding the Statement of Purpose (SOP) you have filled out in your application form, your motivation for joining this course and what you find most interesting in this field.

Is there an admission fee for the PG Certification Programs?

No, there are no additional charges for admission. But, if you are choosing to pay by EMI, there will be a fixed admission charge that is to be paid. This amount will be subtracted from the total course fees.

Executive PG Certificate Program in AI (Machine Learning and Deep Learning) By IIT Roorkee

Application Deadline 8 Jan

12 months

Online

25+

IIT Roorkee

About the Course

Program Highlights

Executive PG Certificate from IIT Roorkee

1 Week Immersion Program

Placement Eligibility Test

25+ Hands-On Project

Timely Doubt Resolution

Access to Cloud Lab

Application Deadline 08th Jan'22

Our Students Work At

And More

Certificate

What is the certificate like?

Why IIT Roorkee?

Why CloudxLab?

#1st

#5

#6

#12

Programming Languages and Tools

Hands-on Learning

Gamified Learning Platform

Auto-assessment Tests

No Installation Required

Mentors / Faculty

Raksha Sharma

Sanjeev Manhas

Venkat Karun

Dr. M.L. Virdi

Sandeep Giri

Abhinav Singh

Praveen Pavithran

Curriculum

Foundation Courses

Course on Machine Learning

Course on Deep Learning

Months of Blended Training

Days of Lab Access

Projects

Learners

Projects

Project 13. Mask R-CNN with OpenCV for Object Detection

Project 14. Image Classification with Pre-trained Keras models

Project 15. Build cats classifier using transfer learning

Project 16. Art Generation Project

Project 17. Credit Card Fraud Detection using Machine Learning

Project 18. Image Stitching using OpenCV and Python (Creating Panorama Project)

Project 19. NYSE Stock Closing Price Prediction using TensorFlow 2

Project 20. Introduction to Transfer Learning (Cat vs Non-cats Project)

Project 21. Building Cat vs Non-Cat Image Classifier using NumPy

Project 22. Iris Flowers Classification using Deep Learning & Keras

Project 23. Classify Clothes from Fashion MNIST Dataset

Project 24. Face recognition

Project 25. Game - OpenGym

Project 1.Churn Email Inbox with Python

Project 2. Predicting the median housing prices in California

Project 3. Classifying handwritten digits in MNIST dataset

Project 4. Noise removal from the images

Project 5. Build a spam classifier

Project 6. Predict which passengers survived in the Titanic shipwreck

Project 7. Image Classification with Pre-trained InceptionV3 Network

Project 8. Host an Image Classification App on Heroku

Project 9. Predicting Noisy Images using KNN Classifier

Project 10. Build an Image Classifier in Fashion MNIST dataset

Project 11. Deploy Machine Learning models to Production using Flask

Project 12. Sentiment Analysis using IMDB dataset

Project 13. Mask R-CNN with OpenCV for Object Detection

Project 14. Image Classification with Pre-trained Keras models

Project 15. Build cats classifier using transfer learning

Project 16. Art Generation Project

Project 17. Credit Card Fraud Detection using Machine Learning

Project 18. Image Stitching using OpenCV and Python (Creating Panorama Project)

Project 19. NYSE Stock Closing Price Prediction using TensorFlow 2

Project 20. Introduction to Transfer Learning (Cat vs Non-cats Project)

Application Deadline 08^th Jan'22

#1^st

Project 13.
Mask R-CNN with OpenCV for Object Detection

Project 14.
Image Classification with Pre-trained Keras models

Project 15.
Build cats classifier using transfer learning

Project 16.
Art Generation Project

Project 17.
Credit Card Fraud Detection using Machine Learning

Project 18.
Image Stitching using OpenCV and Python (Creating Panorama Project)

Project 19.
NYSE Stock Closing Price Prediction using TensorFlow 2

Project 20.
Introduction to Transfer Learning (Cat vs Non-cats Project)

Project 21.
Building Cat vs Non-Cat Image Classifier using NumPy

Project 22.
Iris Flowers Classification using Deep Learning & Keras

Project 23.
Classify Clothes from Fashion MNIST Dataset

Project 24.
Face recognition

Project 25.
Game - OpenGym

Project 1.
Churn Email Inbox with Python

Project 2.
Predicting the median housing prices in California

Project 3.
Classifying handwritten digits in MNIST dataset

Project 4.
Noise removal from the images

Project 5.
Build a spam classifier

Project 6.
Predict which passengers survived in the Titanic shipwreck

Project 7.
Image Classification with Pre-trained InceptionV3 Network

Project 8.
Host an Image Classification App on Heroku

Project 9.
Predicting Noisy Images using KNN Classifier

Project 10.
Build an Image Classifier in Fashion MNIST dataset

Project 11.
Deploy Machine Learning models to Production using Flask

Project 12.
Sentiment Analysis using IMDB dataset

Project 13.
Mask R-CNN with OpenCV for Object Detection

Project 14.
Image Classification with Pre-trained Keras models

Project 15.
Build cats classifier using transfer learning

Project 16.
Art Generation Project

Project 17.
Credit Card Fraud Detection using Machine Learning

Project 18.
Image Stitching using OpenCV and Python (Creating Panorama Project)

Project 19.
NYSE Stock Closing Price Prediction using TensorFlow 2

Project 20.
Introduction to Transfer Learning (Cat vs Non-cats Project)

Project 21.
Building Cat vs Non-Cat Image Classifier using NumPy

Project 22.
Iris Flowers Classification using Deep Learning & Keras

Project 23.
Classify Clothes from Fashion MNIST Dataset

Project 24.
Face recognition

Project 25.
Game - OpenGym

Project 1.
Churn Email Inbox with Python

Project 2.
Predicting the median housing prices in California

Project 3.
Classifying handwritten digits in MNIST dataset

Project 4.
Noise removal from the images

Project 5.
Build a spam classifier

Project 6.
Predict which passengers survived in the Titanic shipwreck

Project 7.
Image Classification with Pre-trained InceptionV3 Network

Project 8.
Host an Image Classification App on Heroku

Project 9.
Predicting Noisy Images using KNN Classifier

Project 10.
Build an Image Classifier in Fashion MNIST dataset

Project 11.
Deploy Machine Learning models to Production using Flask

Project 12.
Sentiment Analysis using IMDB dataset

Project 13.
Mask R-CNN with OpenCV for Object Detection