You're looking for a complete Convolutional Neural Network (CNN) course that teaches you everything you need to create an Image Classification model in Python, right?
You've found the right Convolutional Neural Networks course!
After completing this course you will be able to:
Identify the Image Classification problems which can be solved using CNN Models.
Create CNN models in Python using Keras and Tensorflow libraries and analyze their results.
Confidently practice, discuss and understand Deep Learning concepts
Have a clear understanding of how Neural Networks work internally, and what are various concepts related to this niche.
How this course will help you?
A Verifiable Certificate of Completion is presented to all students who undertake this Convolutional Neural networks course.
If you are an Analyst or an ML scientist, or a student who wants to learn and apply Deep learning in Real world image recognition problems, this course will give you a solid base for that by teaching you some of the most advanced concepts of Deep Learning and their implementation in Python without getting too Mathematical.
Why should you choose this course?
This course covers all the steps that one should take to create an image classification model using Convolutional Neural Networks.
Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.
Download Practice files
With each lecture, there are class notes attached for you to follow along. There is a final practical assignment for you to practically implement your learning.
What is covered in this course?
Understanding Deep Learning
Activation Functions
How Neural Network works & learns
Gradient Descent vs Stochastic Gradient Descent
CNN - Building & Evaluating a model
Hands-on Project
Go ahead and click the enroll button, and I'll see you in lesson 1!