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|Course feature||Lifetime Access, 24x7 Support, Real-time code analysis and feedback, 100% Money Back Guarantee, Certified Trainer|
|Interested Audience||You learn about, and compare, many of the computing and storage services available in Google Cloud Platform, including Google App Engine, Google Compute Engine, Google Kubernetes Engine, Google Cloud Storage, Google Cloud SQL, and BigQuery. You learn about important resource and policy management tools, such as the Google Cloud Resource Manager hierarchy and Google Cloud Identity and Access Management.|
Agilitics courses information , Agilitics courses information
Real-time code analysis and feedback
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Tensorflow is based on the Python, the most popular programming language for data analytics and engineering in the world.
In this course, you will equip yourself the basic and advanced knowledge of Python. After that, you will learn the basic and advanced topics in Tensorflow.
By the completion of this course, you will be able to develop your own NN, CNN and RNN model for image recognition and sentimental analysis using either Tensorflow or Keras.
Test takers should be comfortable with:
Foundational principles of ML and Deep Learning
Building ML models in TensorFlow 2.x
Building image recognition, object detection, text recognition algorithms with deep neural networks and convolutional neural networks
Using real-world images in different shapes and sizes to visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy
Exploring strategies to prevent overfitting, including augmentation and dropouts
Applying neural networks to solve natural language processing problems using TensorFlow
NSF or Full Time Students
Machine Learning Engineers and Developers
You must be comfortable with variables, linear equations, graphs of functions, histograms, and statistical means.
You should be a good programmer. Ideally, you should have some experience programming in Python because the programming exercises are in Python. However, experienced programmers without Python experience can usually complete the programming exercises anyway.
Think strategically and analytically about ML as a business process and consider the fairness implications with respect to ML
- How ML optimization works and how various hyperparameters affect models during optimization
- How to write models in TensorFlow using both pre-made estimators as well as custom ones and train them locally or in Cloud AI Platform
- Why feature engineering is critical to success and how you can use various technologies including Cloud Dataflow and Cloud Dataprep
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One of best I have encountered in my life. Freedom to interact and respond candidly and with courage for every question is not an easy task for Trainers which they did it exceptionally well.
The course is well structure. Timing is also right. The trainer Mr Raj is professional. And he asnwer all my question and doubts.
The course is one of the two in the track of Agile Professional Coach. It is designed to provide both wide and deep knowledge to become a competent Coach with the addirional skills of a Trainer and a Mentor. The two trainers, Preeth Panday and Naveen K Singh, are excellent Facilitators and Coaches with patience and promptness. Their mastery in this area stands out while their mode of delivery captures the interest of the trainees. They demonstrated professionalism with a personal touch.
- Python programming
- Machine Learning with Deep NN
- Image Recognition using Convolutional NN
- Transfer Leanring with Pretrained Models
- Sentimental Analysis using Recurrent NN