You’re looking for a full Convolutional Neural Network (CNN) course that teaches you what you need to build a Python model of Image Recognition, right?
You found the correct course for Convolutional Neural Networks!
You will be able to: After finishing this course:
Identify issues in Image Detection that can be overcome using CNN templates.
Using Keras and Tensorflow libraries to build CNN models in Python and evaluate their performance.
Practise, explore and grasp Deep Learning principles confidently,
Have a good understanding of templates such as LeNet, GoogleNet, VGG16, etc. for advanced image recognition
How’s this course going to help?
All students who undertake this Convolutional Neural Networks course are presented with a Verifiable Certificate of Completion.
If you are an analyst or an ML scientist or a student who wants to learn and apply Deep Learning to problems with real-world image recognition, this course will provide you with a strong foundation by teaching you some of the most advanced Deep Learning concepts and their implementation in Python without becoming too mathematical.
Why should this course be selected for you?
This course covers all the steps one should take to use Convolutional Neural Networks to create an image recognition model.
Most courses concentrate only on teaching how to perform the analysis, but we believe that a strong theoretical understanding of the concepts allows us to create a good model. And one should be able to judge how good the model is after running the analysis and to interpret the results in order to actually be able to help the business.
What makes us able to teach you?
Abhishek and Pukhraj teach the course. As Global Analytics Consulting managers, we have helped companies solve their business problems using deep learning techniques and have used our experience to include the practical aspects of data analysis in this course
.What is the difference between Data Mining, Machine Learning, and Deep Learning?
Simply put, machine learning and data mining use the same algorithms and methods as data mining, with the exception of different kinds of forecasts. Machine learning reproduces existing patterns and knowledge when data mining finds previously unknown patterns and knowledge, and further automatically applies the information to data, decision-making, and behaviour.
In the other hand, deep learning employs specialised computational power and special forms of neural networks and applies them to vast quantities of knowledge in order to read, understand, and recognise complex patterns. Examples in deep learning are automated language processing and medical diagnosis.
For whom this course is intended:
People who seek a computer processing career
Working professionals starting their path to Deep Learning
Anyone interested to learn picture recognition in a brief amount of time from the novice stage