50% discount on all of my machine learning courses

I am pleased to announce huge discount on all of my courses.


1. Introduction to Graph Database using Neo4j:



Graph databases are gaining popularity these days because of its ease of use, simplicity and agility. As compared to relational or document databases, graph databases are great in expressing real world data model with same set of semantics as a product manager would express. Unlike document databases it supports transactional operations. Unlike relational databases, it supports fast querying across large databases.

Ebay is successfully using Neo4J graph databases for optimizing routes for local delivery. Walmart is using Neo4J database for real time local recommendations. Graph databases are also popular for fraud detection use cases.

In this course you will learn fundamentals of graph databases. No prior background in graph database is assumed which makes it ideal for beginner developers. I'll cover rationale behind using graph database.

Here is broad spectrum of topics that I'll be covering

Topics

  • Fundamentals of graph database

  • Why use graph database

  • Data modeling

  • CRUD operations

  • Aggregation

  • Building recommendations using graph database

At the end of course you'll be able to demonstrate your command on graph database and will be ready to build brand new applications. Graph database is also useful for solving Graph Neural network challenges.


Discount link 1 : https://bit.ly/3mHzF8M

Discount link 2: https://bit.ly/322NO8C




2. Builiding iOS question answer application with BERT model:




This course teaches you step by step on how tp build iOS question answering application. It explores the world of machine learning from application developer's perspective. It explains the world of word embeddings which is fundamental technology behind text processing. As Andrew Ng has said "AI is new electricity". The course highlight difference among AI (Artificial Intelligence, Machine learning and deep learning. It also teaches few embedding technologies like glove, word2vec and BERT.

BERT is state of art transformer model developed by Google and has proven to be equivalent of CNN in computer vision technology. This course uses pretrained BERT model and explains how to use it in IOS question answering app.

The students once armed with this knowledge will be able to demonstrate their command on machine learning and can use this technology for several different apps.

The author assumes that the student does not have any background in machine learning.

The course is structured as follows

  • App Preview : Shows preview of app that we are going to build

  • Embeddings : Explains what word embeddings are and why are they important

  • Deep Neural Network : It covers fundamentals of deep learning, and multi layer perceptron

  • BERT, Glove, Word2Vec : Popular word embedding technologies

  • Build UI from scratch : Shows how to build UI by using basic controls in iOS swift

  • Step by Step Coding : Each function is explained in details with step by step walkthrough of the code

  • Text to Speech and Speech to text : This sections explains how to use test to speech and speech top text conversion libraries in iOS app so that user can speak question into the app and hear the answer . This is extremely useful for physically challenged users who can not type using keyboard

  • Run the app on iPhone : Shows the flow of the app on the phone.


Discount link 1 : https://bit.ly/3mHzF8M

Discount link 2: https://bit.ly/2HKfM29





3. Learn to build dog breed image classifier mobile app :



Learn to build dog breed image classifier iPhone app using Apple's crate ML and core ML SDK. Deep learning is popular where a machine can be trained to detect objects in images. Once trained, it can be used to detect objects in any image. The app does not require any wifi or cellular connectivity. It uses deep learning to train the model from scratch on your own image dataset. The model can then be used inside an mobile app using Apple's coreML SDK. We'll build this app in this course. Since the app does not send your images or vides to remote service, it maintains your privacy and data secured.

Build a strong foundation in pose detection engines with this tutorial for beginners.

  • Understanding fundamentals of CreateML and CoreML

  • Understanding fundamentals of deep learning and CNN

  • Train a model on your own dataset using create ML SDK and XCode

  • Build a real life object detection mobile application using coreml and swift

  • A Powerful Skill at Your Fingertips Learning the fundamentals of object detection puts a powerful and very useful tool at your fingertips. swift, create ml and coreml are free, easy to learn, has excellent documentation.

No prior knowledge of CNN or deep learning is assumed. I'll be covering topics like CNN from scratch.

Jobs in computer vision area are plentiful, and being able to learn object detection will give you a strong edge.

Learning object detection will help you become a computer vision developer which is in high demand.

Content and Overview

This course teaches you on how to build object detection engine using open source create ml, coreml and swift . You will work along with me step by step to build following answers

  • Train Object Detection model

  • Build Mobile object detection app using trained model

What am I going to get from this course?

  • Learn object detection from professional trainer from your own desk.

  • Over 10 lectures teaching you how to build object detection engine

  • Suitable for beginner programmers and ideal for users who learn faster when shown.

  • Visual training method, offering users increased retention and accelerated learning.

  • Breaks even the most complex applications down into simplistic steps.

  • Offers challenges to students to enable reinforcement of concepts. Also solutions are described to validate the challenges.

Discount link: https://bit.ly/3eiq3ym




4. Learn to build Simpsons image classifier mobile app :




Learn to build Simpsons image classifier iPhone app using Apple's crate ML and core ML SDK. Deep learning is popular where a machine can be trained to detect objects in images. Once trained, it can be used to detect objects in any image. The app does not require any wifi or cellular connectivity. It uses deep learning to train the model from scratch on your own image dataset. The model can then be used inside an mobile app using Apple's coreML SDK. We'll build this app in this course. Since the app does not send your images or vides to remote service, it maintains your privacy and data secured.

Build a strong foundation in pose detection engines with this tutorial for beginners.

  • Understanding fundamentals of CreateML and CoreML

  • Understanding fundamentals of deep learning and CNN

  • Train a model on your own dataset using create ML SDK and XCode

  • Build a real life object detection mobile application using coreml and swift

  • A Powerful Skill at Your Fingertips Learning the fundamentals of object detection puts a powerful and very useful tool at your fingertips. swift, create ml and coreml are free, easy to learn, has excellent documentation.

No prior knowledge of CNN or deep learning is assumed. I'll be covering topics like CNN from scratch.

Jobs in computer vision area are plentiful, and being able to learn object detection will give you a strong edge.

Learning object detection will help you become a computer vision developer which is in high demand.

Content and Overview

This course teaches you on how to build object detection engine using open source create ml, coreml and swift . You will work along with me step by step to build following answers

  • Train Object Detection model

  • Build Mobile object detection app using trained model

What am I going to get from this course?

  • Learn object detection from professional trainer from your own desk.

  • Over 10 lectures teaching you how to build object detection engine

  • Suitable for beginner programmers and ideal for users who learn faster when shown.

  • Visual training method, offering users increased retention and accelerated learning.

  • Breaks even the most complex applications down into simplistic steps.

  • Offers challenges to students to enable reinforcement of concepts. Also solutions are described to validate the challenges.

Discount link: https://bit.ly/3mH486G



5. Learn to build food image classifier mobile app :



Learn to build food image classifier iPhone app using Apple's crate ML and core ML SDK. Deep learning is popular where a machine can be trained to detect objects in images. Once trained, it can be used to detect objects in any image. The app does not require any wifi or cellular connectivity. It uses deep learning to train the model from scratch on your own image dataset. The model can then be used inside an mobile app using Apple's coreML SDK. We'll build this app in this course. Since the app does not send your images or vides to remote service, it maintains your privacy and data secured.

Build a strong foundation in pose detection engines with this tutorial for beginners.

  • Understanding fundamentals of CreateML and CoreML

  • Understanding fundamentals of deep learning and CNN

  • Train a model on your own dataset using create ML SDK and XCode

  • Build a real life object detection mobile application using coreml and swift

  • A Powerful Skill at Your Fingertips Learning the fundamentals of object detection puts a powerful and very useful tool at your fingertips. swift, create ml and coreml are free, easy to learn, has excellent documentation.

No prior knowledge of CNN or deep learning is assumed. I'll be covering topics like CNN from scratch.

Jobs in computer vision area are plentiful, and being able to learn object detection will give you a strong edge.

Learning object detection will help you become a computer vision developer which is in high demand.


Content and Overview

This course teaches you on how to build object detection engine using open source create ml, coreml and swift . You will work along with me step by step to build following answers

  • Train Object Detection model

  • Build Mobile object detection app using trained model

What am I going to get from this course?

  • Learn object detection from professional trainer from your own desk.

  • Over 10 lectures teaching you how to build object detection engine

  • Suitable for beginner programmers and ideal for users who learn faster when shown.

  • Visual training method, offering users increased retention and accelerated learning.

  • Breaks even the most complex applications down into simplistic steps.

  • Offers challenges to students to enable reinforcement of concepts. Also solutions are described to validate the challenges.

Discount link: https://bit.ly/2HPH6vl


6. Learn to build boat image classifier mobile app :



Learn to build boat image classifier iPhone app using Apple's crate ML and core ML SDK. Deep learning is popular where a machine can be trained to detect objects in images. Once trained, it can be used to detect objects in any image. The app does not require any wifi or cellular connectivity. It uses deep learning to train the model from scratch on your own image dataset. The model can then be used inside an mobile app using Apple's coreML SDK. We'll build this app in this course. Since the app does not send your images or vides to remote service, it maintains your privacy and data secured.

Build a strong foundation in pose detection engines with this tutorial for beginners.

  • Understanding fundamentals of CreateML and CoreML

  • Understanding fundamentals of deep learning and CNN

  • Train a model on your own dataset using create ML SDK and XCode

  • Build a real life object detection mobile application using coreml and swift

  • A Powerful Skill at Your Fingertips Learning the fundamentals of object detection puts a powerful and very useful tool at your fingertips. swift, create ml and coreml are free, easy to learn, has excellent documentation.

No prior knowledge of CNN or deep learning is assumed. I'll be covering topics like CNN from scratch.

Jobs in computer vision area are plentiful, and being able to learn object detection will give you a strong edge.

Learning object detection will help you become a computer vision developer which is in high demand.

Content and Overview

This course teaches you on how to build object detection engine using open source create ml, coreml and swift . You will work along with me step by step to build following answers

  • Train Object Detection model

  • Build Mobile object detection app using trained model

What am I going to get from this course?

  • Learn object detection from professional trainer from your own desk.

  • Over 10 lectures teaching you how to build object detection engine

  • Suitable for beginner programmers and ideal for users who learn faster when shown.

  • Visual training method, offering users increased retention and accelerated learning.

  • Breaks even the most complex applications down into simplistic steps.

  • Offers challenges to students to enable reinforcement of concepts. Also solutions are described to validate the challenges.

Discount link: https://bit.ly/2HWtA99


7. Learn to build caltech-101 image classifier mobile app :





Learn to build Caltech-101 image classifier iPhone app using Apple's crate ML and core ML SDK. Deep learning is popular where a machine can be trained to detect objects in images. Once trained, it can be used to detect objects in any image. The app does not require any wifi or cellular connectivity. It uses deep learning to train the model from scratch on your own image dataset. The model can then be used inside an mobile app using Apple's coreML SDK. We'll build this app in this course. Since the app does not send your images or vides to remote service, it maintains your privacy and data secured.

Build a strong foundation in pose detection engines with this tutorial for beginners.

  • Understanding fundamentals of CreateML and CoreML

  • Understanding fundamentals of deep learning and CNN

  • Train a model on your own dataset using create ML SDK and XCode

  • Build a real life object detection mobile application using coreml and swift

  • A Powerful Skill at Your Fingertips Learning the fundamentals of object detection puts a powerful and very useful tool at your fingertips. swift, create ml and coreml are free, easy to learn, has excellent documentation.

No prior knowledge of CNN or deep learning is assumed. I'll be covering topics like CNN from scratch.

Jobs in computer vision area are plentiful, and being able to learn object detection will give you a strong edge.

Learning object detection will help you become a computer vision developer which is in high demand.


Content and Overview

This course teaches you on how to build object detection engine using open source create ml, coreml and swift . You will work along with me step by step to build following answers

  • Train Object Detection model

  • Build Mobile object detection app using trained model

What am I going to get from this course?

  • Learn object detection from professional trainer from your own desk.

  • Over 10 lectures teaching you how to build object detection engine

  • Suitable for beginner programmers and ideal for users who learn faster when shown.

  • Visual training method, offering users increased retention and accelerated learning.

  • Breaks even the most complex applications down into simplistic steps.

  • Offers challenges to students to enable reinforcement of concepts. Also solutions are described to validate the challenges.

Discount link: https://bit.ly/380WrnU


8. Learn how to build pose detection deep learning iPhone app :





Learn to build real time pose detection iPhone app using Posenet deep learning algorithm. Deep learning is popular where a machine can be trained to detect poses in video and images. Once trained, it can be used to detect poses in any video or image. The app does not require any wifi or cellular connectivity. It uses deep learning and pretrained posenet model. It leverages apple's coreml and vision SDK to achieve pose detection entirely on the phone. Since the app does not send your images or vides to remote service, it maintains your privacy and data secured.

Build a strong foundation in pose detection engines with this tutorial for beginners.

  • Understanding fundamentals of pose detection

  • Understanding fundamentals of deep learning and CNN

  • Benefits of posenet for fitness apps

  • Build a real life pose detection in video using posenet, computer vision, coreml and swift

  • Build a real life pose detection in image using posenet, computer vision,, coreml and swift

  • A Powerful Skill at Your Fingertips Learning the fundamentals of real time pose detection puts a powerful and very useful tool at your fingertips. swift, posenet and coreml are free, easy to learn, has excellent documentation.

No prior knowledge of CNN or deep learning is assumed. I'll be covering topics like CNN from scratch.

Jobs in computer vision area are plentiful, and being able to learn real time object detection will give you a strong edge. YOLO is state of art technology that can quickly help you achieve your goal.

Learning pose detection with posenet will help you become a computer vision developer which is in high demand.

Content and Overview

This course teaches you on how to build real time pose detection engine using open source posenet, coreml and swift . You will work along with me step by step to build following answers

  • Real time pose detection in Video

  • Real time pose detection in image

  • Fundamentals of CNN and posenet

What am I going to get from this course?

  • Learn posenent and build real time pose detection engine from professional trainer from your own desk.

  • Over 15 lectures teaching you how to build real time pose detection engine

  • Suitable for intermediate programmers and ideal for users who learn faster when shown.

  • Visual training method, offering users increased retention and accelerated learning.

  • Breaks even the most complex applications down into simplistic steps.

  • Offers challenges to students to enable reinforcement of concepts. Also solutions are described to validate the challenges.

Discount link: https://bit.ly/3eeJlEu

9. Real time object detection in video using YOLO on iPhone :




Learn to build real time object detection engine using YOLO deep learning algorithm. Deep learning is popular where a machine can be trained to detect objects in video and images. Once trained, it can be used to detect objects in any video or image.

Yolo (You only look Once) algorithm has become popular because of its real time nature. It can detect objects at 45 frames per second or within 20 ms. This makes it attractive to use it in self driving car where detecting objects in real time is key to avoid collisions. Unlike its predecessor, YOLO looks at image only once.

Build a strong foundation in image search engines with this tutorial for beginners.

  • Understanding fundamentals of YOLO

  • Understanding fundamentals of deep learning and CNN

  • Benefits of YOLO for self driving car use case

  • Build a real life object detection in video using YOLO, coreml and swift

  • Build a real life object detection in image using YOLO, coreml and swift

  • A Powerful Skill at Your Fingertips Learning the fundamentals of real time object detection puts a powerful and very useful tool at your fingertips. swift, YOLO and coreml are free, easy to learn, has excellent documentation.

No prior knowledge of CNN or deep learning is assumed. I'll be covering topics like CNN from scratch.

Jobs in object detection area are plentiful, and being able to learn real time object detection will give you a strong edge. YOLO is state of art technology that can quickly help you achieve your goal.

Learning object detection with YOLO will help you become a computer vision developer which is in high demand.


Content and Overview

This course teaches you on how to build real time object detection engine using open source YOLO, OPNCV and Python . You will work along with me step by step to build following answers

  • Real time object detection in Video

  • Real time object detection in image

  • Fundamentals of CNN and YOLO