Learn TensorFlow on Google Cloud with hands-on, structured modules
Build real-world ML skills with practical projects and labs
Learn from industry experts with proven credentials
Gain a globally recognised certification to enhance your credibility
Enjoy flexible learning options that fit your schedule
Master scalable ML models using TensorFlow and Google Cloud
What our training includes:
Upcoming sessions
Develop a data strategy around machine learning
Examine use cases that are then reimagined through an ML lens
Recognize biases that ML can amplify
Leverage Google Cloud Platform tools and environment to do ML
Learn from Google's experience to avoid common pitfalls
Carry out data science tasks in online collaborative notebooks
Invoke pre-trained ML models from Cloud AI Platform
Describe how to improve data quality
Perform exploratory data analysis
Build and train supervised learning models
Optimize and evaluate models using loss functions and performance metrics
Mitigate common problems that arise in machine learning
Create repeatable and scalable training, evaluation, and test datasets
Create TensorFlow 2.x and Keras machine learning models
Describe Tensorflow 2.x key components
Use the tf.data library to manipulate data and large datasets
Use the Keras Sequential and Functional APIs for simple and advanced model creation
Train, deploy, and productionalize ML models at scale with Cloud AI Platform
Compare the key required aspects of a good feature
Combine and create new feature combinations through feature crosses
Perform feature engineering using BQML, Keras, and TensorFlow 2.x
Understand how to preprocess and explore features with Cloud Dataflow and Cloud Dataprep
Understand and apply how TensorFlow transforms features
Optimize model performance with hyperparameter tuning
Experiment with neural networks and fine-tune performance
Enhance ML model features with embedding layers
Summary
Upon finishing the training, you will:
1
Master TensorFlow 2.x and Keras to build advanced machine learning models
2
Design scalable machine learning solutions using Google Cloud’s AI Platform and TensorFlow
3
Implement feature engineering with Cloud Dataprep and TensorFlow for data optimisation
4
Optimise model performance through hyperparameter tuning and advanced evaluation metrics
5
Gain hands-on experience deploying real-world ML models with Google Cloud tools
Overall ratings by our students
Our Machine Learning with TensorFlow on Google Cloud course in Saudi Arabia is designed to teach you how to build, train, and deploy machine learning models using TensorFlow and Google Cloud tools. You'll gain hands-on experience with Google Cloud's AI Platform, TensorFlow 2.x, and Keras to create scalable machine learning models. This course helps you develop real-world skills that are in high demand across various industries, opening up lucrative career opportunities in machine learning and AI fields.
Our course in Riyadh, Saudi Arabia, is open to individuals with a background in programming, data analysis, or a related field. Familiarity with Python programming is required, as it is the primary language used in TensorFlow. While prior experience in machine learning or data science can be beneficial, beginners with basic knowledge of coding and data analysis are welcome. Additionally, proficiency in English is essential as course materials and instructions are in English.
In this course, you'll master TensorFlow 2.x and Keras for building machine learning models. Topics include:
1. Supervised learning
2. Feature engineering with Cloud Dataprep
3. Hyperparameter tuning
4. Model optimisation
Upon completing the Machine Learning with TensorFlow on Google Cloud course in Saudi Arabia, our certified professionals in Saudi Arabia can explore the following career paths:
1. Machine Learning Engineer
2. Data Scientist
3. AI Specialist
4. Cloud Solutions Architect
Yes, our certification program is available in locations across GCC regions. We offer comprehensive instruction and training globally, which helps students to gain this important certification.
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