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Learn Deep Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders and Reinforcement Learning From Industry Experts
Have you ever wondered how self-driving cars are running on roads or how Netflix recommends the movies which you may like or how Amazon recommends you products or how Google search gives you such an accurate results or how speech recognition in your smartphone works or how the world champion was beaten at the game of Go?
Machine learning is behind these innovations. In the recent times, it has been proven that machine learning and deep learning approach to solving a problem gives far better accuracy than other approaches. This has led to a Tsunami in the area of Machine Learning.
Most of the domains that were considered specializations are now being merged into Machine Learning. This has happened because of the following:
Every domain of computing such as data analysis, software engineering, and artificial intelligence is going to be impacted by Machine Learning. Therefore, every engineer, researcher, manager or scientist would be expected to know Machine Learning.
So naturally, you are excited about Machine learning and would love to dive into it. This specialization is designed for those who want to gain hands-on experience in solving real-life problems using machine learning and deep learning. After finishing this specialization, you will find creative ways to apply your learnings to your work. For example
See you in the specialization and happy learning!
(Early Bird Offer)
Churn the mail activity from various individuals in an open source project development team.
Classify images from the Fashion MNIST dataset using Tensorflow 2, Matplotlib, and Python
Learn how to train a neural network from scratch to classify data using TensorFlow 2, and how to use the weights of an already trained model to achieve classification to another set of data.
Create a custom loss function in Keras with TensorFlow 2 backend.
Learn how to access the pre-trained models(here we get pre-trained ResNet model) from Keras of TensorFlow 2 to classify images.
In this project, you will build a basic neural network to classify if a given image is of cat or not using transfer learning technique with Python and Keras.
Learn how to read a pre-trained TensorFlow model for object detection using OpenCV.
Use TensorFlow 2 to generate an image that is an artistic blend of a content image and style image using Neural Style Transfer.
Predict stock market closing prices for a firm using GRU, a state-of-art deep learning algorithm for sequential data, with Keras and Python.
Create a sentiment analysis model with the IMDB dataset using TensorFlow 2.
Learn how to practically implement the autoencoder, stacking an encoder and decoder using TensorFlow 2, and depict reconstructed output images by the autoencoder model using the Fashion MNIST dataset.
Learn how to deploy a deep learning model as a web application using the Flask framework.
Our course is exhaustive and the certificate rewarded by us is proof that you have taken a big leap in Machine Learning and Deep Learning.
The knowledge you have gained from working on projects, videos, quizzes, hands-on assessments and case studies gives you a competitive edge.
Highlight your new skills on your resume, LinkedIn, Facebook and Twitter. Tell your friends and colleagues about it.
On completing this course, you will be able to have a complete understanding of how to train a Deep Learning network. Also, as part of the course, you will be working on 4 real-world projects which will give you full expertise on how to build neural networks. The course also enables you to avoid the challenges of overfitting, underfitting, data augmentation etc in real-world scenarios.
You should have the knowledge of about Machine Learning to get the concepts of this course.
You can check https://youtu.be/dXCx4anEcgU for watching the Course Preview.
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
Have more questions? Please contact us at email@example.com