Last updated 9/2023
Duration: 5h 13m | Video: .MP4, 1280×720 30 fps | Audio: AAC, 48 kHz, 2ch | Size: 2.04 GB
Genre: eLearning | Language: English
Build, train, and deploy ML models with TensorFlow: A hands-on journey through Google Cloud’s powerful infrastructure
What you’ll learn
Master the foundational principles behind simple ML models such as Linear and Logistic Regression models using TensorFlow.
Construct intricate Artificial Neural Networks (ANN) to tackle more complex data challenges.
Design Convolutional Neural Networks (CNN) for image and pattern recognition tasks.
Harness the capabilities of Google Cloud’s Colab to execute Python codes for ML tasks efficiently.
Explore the functionalities of Google Vertex and how it augments Jupyter notebook constructions.
Implement end-to-end machine learning workflows, from data preprocessing to model deployment
Requirements
Basic knowledge of Python and familiarity with Jupyter notebooks; beginners welcome, as foundational concepts are covered.
Description
If you’re a budding data enthusiast, developer, or even an experienced professional wanting to make the leap into the ever-growing world of machine learning, have you often wondered how to integrate the power of TensorFlow with the vast scalability of Google Cloud? Do you dream of deploying robust ML models seamlessly without the fuss of infrastructure management?
Delve deep into the realms of machine learning with our structured guide on “Machine Learning with TensorFlow on Google Cloud.” This course isn’t just about theory; it’s a hands-on journey, uniquely tailored to help you utilize TensorFlow’s prowess on the expansive infrastructure that Google Cloud offers.
In this course, you will
Develop
foundational models such as Linear and Logistic Regression using TensorFlow.
Master
advanced architectures like Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) for intricate tasks.
Harness
the power and convenience of Google Cloud’s Colab to run Python code effortlessly.
Construct
sophisticated Jupyter notebooks with real-world datasets on Google Colab and Vertex.
But why dive into TensorFlow on Google Cloud? As machine learning solutions become increasingly critical in decision-making, predicting trends, and understanding vast datasets, TensorFlow’s integration with Google Cloud is the key to rapid prototyping, scalable computations, and cost-effective solutions.
Throughout your learning journey, you’ll immerse yourself in a series of projects and exercises, from constructing your very first ML model to deploying intricate deep learning networks on the cloud.
This course stands apart because it bridges the gap between theory and practical deployment, ensuring that once you’ve completed it, you’re not just knowledgeable but are genuinely ready to apply these skills in real-world scenarios.
Take the next step in your machine learning adventure. Join us, and let’s build, deploy, and scale together.
Who this course is for
Aspiring data enthusiasts keen on exploring machine learning using TensorFlow.
Developers looking to leverage cloud infrastructure for ML tasks.
Professionals eager to combine TensorFlow’s capabilities with Google Cloud.
Beginners seeking a structured introduction to ML on the cloud.
Experienced learners aiming to deepen their knowledge and skillset in the field of ML using TensorFlow on GCP.
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