Machine Learning with TensorFlow on Google Cloud in Australia
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 suit your schedule
Master scalable ML models using TensorFlow and Google Cloud
Convenient and hassle-free payment plans
4.7/54.7/5Overview
What our training includes:
- Master TensorFlow 2.x and Keras for building machine learning models
- Train models with Google Cloud's AI Platform for scalable machine learning solutions
- Optimise models using loss functions, performance metrics, and hyperparameter tuning
- Implement feature engineering with BQML, TensorFlow, and Cloud Dataprep
- Analyse data with Cloud Dataflow and preprocess using TensorFlow's tf.data
- Gain hands-on experience with real-world projects in machine learning applications
Upcoming sessions
Curriculum
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
Meet your Trainer
Our Trainers
Learners Point has a reputation for high-quality training that makes a difference in people's lives. We undertake a practical and innovative approach to working closely with businesses to improve their workforce. Our expertise is wide-ranging with ample support from our expert trainers who are globally recognized and hold a diverse set of experiences in their field of expertise. We are proud of our instructors who take ownership of our distinctive and comprehensive training methodologies, help our students imbibe those with ease, and accomplish gracefully.
We at Learners Point believe in encouraging our students to embark upon a journey of lifelong learning and self-development, with the aid of our comprehensive and distinctive courses tailored to current market trends. The manifestation of our career-oriented approach is what we assure through a pleasant professional enriched environment with cutting-edge technology, and an outstanding while highly acknowledged training staff that uses up-to-date methodologies and quality course material. With our aim to mold professionals to be future leaders, our industry expert trainers provide the best in town mentorship to our students while endowing them with the thirst for knowledge and inspiring them to strive for professional and human excellence.
Learning Outcomes
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
Learners Point Certificate
Earn a Course Completion Certificate, an official Learners Point credential that confirms that you have successfully completed a course with us.
KHDA Certificate
Earn a KHDA attested Course Certificate. The Knowledge and Human Development Authority (KHDA) is the educational quality assurance and regulatory authority of the Government of Dubai, United Arab Emirates.
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Frequently asked questions
You will get to learn how to create, train, and implement machine learning models using TensorFlow and Google Cloud tools in our Machine Learning with TensorFlow on Google Cloud course in Australia. 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 in high demand across various industries, opening up lucrative career opportunities in machine learning and AI fields.
Our course in Australia 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 coding and data analysis knowledge 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:
- Supervised learning
- Feature engineering with Cloud Dataprep
- Hyperparameter tuning
- Model optimisation
In this course, you'll work with key tools and technologies such as TensorFlow 2.x, Keras, Google Cloud's AI Platform, Cloud Dataflow, and Cloud Dataprep. You'll also use the TensorFlow framework for building and deploying machine learning models and gain experience with advanced data preprocessing, model optimisation, and cloud-based deployments. These tools are critical for handling real-world machine learning projects at scale.
Unlike many other courses, our Machine Learning with TensorFlow on Google Cloud course in Australia focuses on practical, hands-on learning with real-world applications. You'll use TensorFlow 2.x and Google Cloud tools to build scalable machine learning solutions, which sets this course apart from competitors that offer more theoretical content. Additionally, this course provides in-depth exposure to cloud-based model deployment and optimisation, making it highly relevant for today's AI-driven job market.
Upon completing the Machine Learning with TensorFlow on Google Cloud course, our certified professionals in Australia can explore the following career paths:
● Machine Learning Engineer
● Data Scientist
● AI Specialist
● Cloud Solutions Architect
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