Course Summary
This course introduces students to the basics of TensorFlow, a popular open-source software library for data analysis and machine learning. Students will learn how to build and train models using TensorFlow, and will gain experience working with real-world datasets.Key Learning Points
- Learn how to build and train machine learning models using TensorFlow
- Gain experience working with real-world datasets
- Explore deep learning techniques and applications
Job Positions & Salaries of people who have taken this course might have
- USA: $112,000
- India: ₹1,066,000
- Spain: €43,000
- USA: $112,000
- India: ₹1,066,000
- Spain: €43,000
- USA: $96,000
- India: ₹797,000
- Spain: €33,000
- USA: $112,000
- India: ₹1,066,000
- Spain: €43,000
- USA: $96,000
- India: ₹797,000
- Spain: €33,000
- USA: $113,000
- India: ₹1,507,000
- Spain: €49,000
Related Topics for further study
Learning Outcomes
- Understand the basics of TensorFlow and its applications in machine learning
- Learn how to build and train models using TensorFlow
- Gain experience working with real-world datasets and deep learning techniques
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of programming concepts
- Familiarity with Python
Course Difficulty Level
IntermediateCourse Format
- Self-paced
- Online
- Video lectures
- Hands-on exercises
Similar Courses
- Applied Data Science with Python
- Machine Learning
- Deep Learning
Related Education Paths
Notable People in This Field
- Andrew Ng
- François Chollet
Related Books
Description
This course is focused on using the flexibility and “ease of use” of TensorFlow 2.x and Keras to build, train, and deploy machine learning models. You will learn about the TensorFlow 2.x API hierarchy and will get to know the main components of TensorFlow through hands-on exercises. We will introduce you to working with datasets and feature columns. You will learn how to design and build a TensorFlow 2.x input data pipeline. You will get hands-on practice loading csv data, numPy arrays, text data, and images using tf.Data.Dataset. You will also get hands-on practice creating numeric, categorical, bucketized, and hashed feature columns.
Knowledge
- Use the Keras Sequential and Functional APIs for simple and advanced model creation
- Design and build a TensorFlow 2.x input data pipeline
- Use the tf.data library to manipulate data and large datasets
- Train, deploy, and productionalize ML models at scale with Cloud AI Platform
Outline
- Introduction to course
- Intro to Course
- Getting Started with Google Cloud and Qwiklabs
- Introduction to TensorFlow
- Introduction to TensorFlow
- TensorFlow API Hierarchy
- Components of TensorFlow: Tensors and Variables
- Lab Intro Introduction to Tensors and Variables
- Lab Intro Writing low-level TensorFlow programs
- Readings
- Introduction to TensorFlow
- API Hierarchy
- Tensors and Variables
- Design and Build a TensorFlow Input Data Pipeline
- Overview
- Working in-memory and with files
- Getting the data ready for model training
- Lab Intro Load CSV and Numpy Data
- Lab Intro Loading Image Data
- Lab Intro Feature Columns
- Optional Lab Intro TFRecord and tf.Example
- Training on Large Datasets with tf.data API
- Lab Intro Manipulating data with Tensorflow Dataset API
- Optional Lab Intro Feature Analysis Using TensorFlow Data Validation and Facets
- Readings
- PRACTICE QUIZ: Getting the data ready for model training
- Training on Large Datasets with tf.data API
- Design and Build Input Data Pipeline
- Training neural networks with Tensorflow 2 and the Keras Sequential API
- Overview
- Activation functions
- Activation functions: Pitfalls to avoid in Backpropagation
- Neural Networks with Keras Sequential API
- Lab intro Keras Sequential API
- Readings
- Activation Functions
- Neural Networks with TF2 and Keras
- Training neural networks with Tensorflow 2 and the Keras Functional API
- Neural Networks with Keras Functional API
- Regularization: The Basics
- Regularization: L1, L2, and Early Stopping
- Regularization: Dropout
- Serving models in the Cloud
- Lab intro Keras Functional API
- Readings
- The Keras Functional API
- Regularization
- Serving Models in the Cloud
- Summary
- Course Summary
- Quiz Questions to ALL Lessons
- Course Slide
- Course Summary
Summary of User Reviews
The Intro to TensorFlow course on Coursera has received positive reviews from learners. Many users found the course to be comprehensive and easy to follow, making it a great introduction to TensorFlow for beginners.Key Aspect Users Liked About This Course
Comprehensive and easy to followPros from User Reviews
- Well-structured and organized content
- Good pacing that allows learners to absorb concepts easily
- Hands-on exercises that reinforce learning
- Great introduction to machine learning with TensorFlow
- Good coverage of the basics of neural networks
Cons from User Reviews
- Some learners found the course to be too basic
- Limited coverage of more advanced topics
- Some users felt that the course lacked real-world applications of TensorFlow
- Occasional technical issues with the platform
- The course does not offer a certificate of completion for free
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