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Machine Learning with TensorFlow on Google Cloud

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Machine Learning with TensorFlow on Google Cloud, available at $19.99, has an average rating of 4.61, with 58 lectures, 4 quizzes, based on 49 reviews, and has 4305 subscribers.

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You will learn about 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 This course is ideal for individuals who are Aspiring data enthusiasts keen on exploring machine learning using TensorFlow. or Developers looking to leverage cloud infrastructure for ML tasks. or Professionals eager to combine TensorFlow's capabilities with Google Cloud. or Beginners seeking a structured introduction to ML on the cloud. or Experienced learners aiming to deepen their knowledge and skillset in the field of ML using TensorFlow on GCP. It is particularly useful for Aspiring data enthusiasts keen on exploring machine learning using TensorFlow. or Developers looking to leverage cloud infrastructure for ML tasks. or Professionals eager to combine TensorFlow's capabilities with Google Cloud. or Beginners seeking a structured introduction to ML on the cloud. or Experienced learners aiming to deepen their knowledge and skillset in the field of ML using TensorFlow on GCP.

Enroll now: Machine Learning with TensorFlow on Google Cloud

Summary

Title: Machine Learning with TensorFlow on Google Cloud

Price: $19.99

Average Rating: 4.61

Number of Lectures: 58

Number of Quizzes: 4

Number of Published Lectures: 56

Number of Published Quizzes: 4

Number of Curriculum Items: 62

Number of Published Curriculum Objects: 60

Original Price: $49.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

  • 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.

Target Audiences

  • 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.

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 Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Course resources

Lecture 3: Google cloud – Google Colab vs Vertex AI

Chapter 2: Basics of Machine Learning

Lecture 1: Linear regression basics

Lecture 2: Logistic regression basics

Chapter 3: Perceptron – Introduction to neural network

Lecture 1: Introduction to ANN

Lecture 2: Single Neural Cell

Lecture 3: Example of a Perceptron

Lecture 4: What are Activation Functions

Lecture 5: Sigmoid Activation Function

Lecture 6: Linear regression case study

Lecture 7: Linear regression case study – demonstration

Lecture 8: Logistic regression case study

Lecture 9: Logistic regression case study – demonstration

Chapter 4: Artificial neural network

Lecture 1: Parallel vs Sequential Stacking

Lecture 2: Important terms

Lecture 3: How Neural Networks work

Lecture 4: Finding the optima using Gradient Descent

Lecture 5: Concept Behind Using Gradient Descent

Lecture 6: Back Propagation in neural network

Lecture 7: Types and Uses of Activation Functions

Lecture 8: Multiclass Classification

Lecture 9: Difference Between Gradient Descent and Stochastic Gradient Descent

Lecture 10: Epochs

Chapter 5: Creating arctificial neural network on Google Colab

Lecture 1: Information on Keras and Tensorflow

Lecture 2: Dataset for classification

Lecture 3: Normalization and Test-Train split

Lecture 4: Different ways to create ANN

Lecture 5: Building the Neural Network

Lecture 6: Compiling and Training the Neural Network model

Lecture 7: Evaluating performance and Predicting

Lecture 8: Building Neural Network for Regression Problem

Lecture 9: Complex ANN Architectures using Functional API

Lecture 10: Understanding Checkpoints and Callbacks in Keras

Chapter 6: CNN – Introduction

Lecture 1: CNN – Introduction

Lecture 2: CNN – Implementation

Lecture 3: Stride in CNN

Lecture 4: Padding in CNN

Lecture 5: Filters in CNN

Lecture 6: Example of Filters and Feature maps in CNN

Lecture 7: Channels in CNN

Lecture 8: RGB Channels Illustration

Lecture 9: Pooling layer in CNN

Chapter 7: CNN on Google Colab

Lecture 1: CNN model – Preprocessing

Lecture 2: CNN model – structure and Compile

Lecture 3: CNN model – Training and results

Lecture 4: CNN model – Impact of pooling layer

Chapter 8: Project – Creating CNN model from scratch

Lecture 1: Introduction to the project

Lecture 2: Data for the project

Lecture 3: Project – Data Preprocessing in Python

Lecture 4: Project – Training CNN model in Python

Lecture 5: Project in Python – model results

Chapter 9: Project : Data Augmentation for avoiding overfitting

Lecture 1: Project – Data Augmentation Preprocessing

Lecture 2: Project – Data Augmentation Training and Results

Chapter 10: Congratulations & about your certificate

Lecture 1: About your certificate

Lecture 2: Bonus Lecture

Instructors

Rating Distribution

  • 1 stars: 0 votes
  • 2 stars: 1 votes
  • 3 stars: 2 votes
  • 4 stars: 20 votes
  • 5 stars: 26 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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