Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
6 Hour(s) 8 Minute(s)
Language
English
Taught by
Start-Tech Academy
Certificate
- Available
- *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
Course Overview
Build, train, and deploy ML models with TensorFlow: A hands-on journey through Google Cloud's powerful infrastructure
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.
Course Content
- 10 section(s)
- 60 lecture(s)
- Section 1 Introduction
- Section 2 Basics of Machine Learning
- Section 3 Perceptron - Introduction to neural network
- Section 4 Artificial neural network
- Section 5 Creating arctificial neural network on Google Colab
- Section 6 CNN - Introduction
- Section 7 CNN on Google Colab
- Section 8 Project - Creating CNN model from scratch
- Section 9 Project : Data Augmentation for avoiding overfitting
- Section 10 Congratulations & about your certificate
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
Skills covered in this course
Reviews
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JJavohir Raximbayev
Juda zo`r
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IIshaaq MM
It was good and informative. Just don't explain line by line in code. Also, Give more details for explanations. You choose the correct words for explanations. But, those explanations are not enough. Get into more detailed explanations.
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AAsmita Majumdar
It is very good session
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RRIadh
good
