You're looking for a complete Deep Learning course that teaches you everything you need to create an Image Classification model, an Image segmentation model or any generic Neural Networks in Python, right?
You've found the right course!
After completing this course you will be able to:
Understand the concepts of Neural Networks
Identify the Image Classification problems which can be solved using CNN Models.
Create ANN, CNN & RNN 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 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?
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?
Basics of Neural Networks
Understanding Deep Learning
Activation Functions
How Neural Network works & learns
Gradient Descent vs Stochastic Gradient Descent
ANN, CNN & RNN - Building & Evaluating a model
Pre-Trained Models
Transfer Learning
Hands-on Project
Go ahead and click the enroll button, and I'll see you in lesson 1!